From a8474cda5fe8d6f73ed32cc7e15f79cbe672d77d Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Thu, 23 Aug 2012 17:10:47 -0500 Subject: Made some minor changes to trait data and analysis page template --- misc/byobu_commands.txt | 6 ++++++ misc/runserver.txt | 1 + misc/virtual_env.txt | 1 + 3 files changed, 8 insertions(+) create mode 100644 misc/byobu_commands.txt create mode 100644 misc/runserver.txt create mode 100644 misc/virtual_env.txt (limited to 'misc') diff --git a/misc/byobu_commands.txt b/misc/byobu_commands.txt new file mode 100644 index 00000000..c9c22b29 --- /dev/null +++ b/misc/byobu_commands.txt @@ -0,0 +1,6 @@ +byobu -RD +control-a then :multiuser on +control-a then :acladd sam + +type: screen -list for sessions +screen -r sam/27189.byobu diff --git a/misc/runserver.txt b/misc/runserver.txt new file mode 100644 index 00000000..71e3dfd7 --- /dev/null +++ b/misc/runserver.txt @@ -0,0 +1 @@ +python runserver.py diff --git a/misc/virtual_env.txt b/misc/virtual_env.txt new file mode 100644 index 00000000..7a8f20be --- /dev/null +++ b/misc/virtual_env.txt @@ -0,0 +1 @@ +source ~/ve27/bin/activate -- cgit v1.2.3 From f4be530a00631c5a3b68cb41687d71834dd58c90 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Fri, 24 Aug 2012 16:24:51 -0500 Subject: Got the show/hide outliers button working and began fixing the issue where parents appeared twice in the strain list --- misc/byobu_commands.txt | 2 +- wqflask/wqflask/show_trait/DataEditingPage.py | 67 +++++----------------- .../new/javascript/trait_data_and_analysis.coffee | 16 ++++++ .../new/javascript/trait_data_and_analysis.js | 18 +++++- 4 files changed, 49 insertions(+), 54 deletions(-) (limited to 'misc') diff --git a/misc/byobu_commands.txt b/misc/byobu_commands.txt index c9c22b29..48e85396 100644 --- a/misc/byobu_commands.txt +++ b/misc/byobu_commands.txt @@ -3,4 +3,4 @@ control-a then :multiuser on control-a then :acladd sam type: screen -list for sessions -screen -r sam/27189.byobu +screen -r zas1024/25679.byobu diff --git a/wqflask/wqflask/show_trait/DataEditingPage.py b/wqflask/wqflask/show_trait/DataEditingPage.py index 68de5ae4..d07cec59 100755 --- a/wqflask/wqflask/show_trait/DataEditingPage.py +++ b/wqflask/wqflask/show_trait/DataEditingPage.py @@ -1694,75 +1694,38 @@ class DataEditingPage(templatePage): mainForm = None # Just trying to get things working primary_strains = self.addTrait2Table(fd=fd, varianceDataPage=varianceDataPage, strainlist=allstrainlist_neworder, mainForm=mainForm, thisTrait=thisTrait, other_strainsExist=other_strainsExist, attribute_ids=attribute_ids, attribute_names=attribute_names, strains='primary') - #primary_table.append(primary_header) - #for i in range(len(primary_body)): - # print("hji") - # pass - #primary_table.append(primary_body[i]) - other_strains = [] for strain in thisTrait.data.keys(): print("hjk - strain is:", strain) - if strain not in allstrainlist_neworder: - #pass + if strain not in allstrainlist_neworder + fd.f1list + fd.parlist: allstrainlist_neworder.append(strain) other_strains.append(strain) if other_strains: - #other_table = HT.TableLite(cellspacing=0, cellpadding=0, Id="sortable2", Class="tablesorter") #Table object with other (for example, non-BXD / MDP) traits - #other_header = self.getTableHeader(fd=fd, thisTrait=thisTrait, nCols=nCols, attribute_names=attribute_names) #Generate header for other table object; same function is used as the one used for the primary table, since the header is the same + unappended_par_f1 = fd.f1list + fd.parlist + par_f1_strains = ["_2nd_" + strain for strain in unappended_par_f1] + other_strains.sort() #Sort other strains - other_strains = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + other_strains #Append F1 and parent strains to the beginning of the sorted list of other strains - - #MDPText = HT.Span("Samples:", Class="ffl fwb fs12") - #MDPMenu1 = HT.Select(name='MDPChoice1') - #MDPMenu2 = HT.Select(name='MDPChoice2') - #MDPMenu3 = HT.Select(name='MDPChoice3') - #MDPMenu1.append(('%s Only' % fd.RISet,'1')) - #MDPMenu2.append(('%s Only' % fd.RISet,'1')) - #MDPMenu3.append(('%s Only' % fd.RISet,'1')) - #MDPMenu1.append(('Non-%s Only' % fd.RISet,'2')) - #MDPMenu2.append(('Non-%s Only' % fd.RISet,'2')) - #MDPMenu3.append(('Non-%s Only' % fd.RISet,'2')) - #MDPMenu1.append(('All Cases','0')) - #MDPMenu2.append(('All Cases','0')) - #MDPMenu3.append(('All Cases','0')) - #self.MDPRow1.append(HT.TD(MDPText),HT.TD(MDPMenu1)) - #self.MDPRow2.append(HT.TD(MDPText),HT.TD(MDPMenu2)) - #self.MDPRow3.append(HT.TD(MDPText),HT.TD(MDPMenu3)) - - other_strains = self.addTrait2Table(fd=fd, varianceDataPage=varianceDataPage, strainlist=other_strains, mainForm=mainForm, thisTrait=thisTrait, attribute_ids=attribute_ids, attribute_names=attribute_names, strains='other') - - #other_table.append(other_header) - #for i in range(len(other_body)): - # print("hjn") - # pass - #other_table.append(other_body[i]) - else: - pass + other_strains = par_f1_strains + other_strains + + other_strains = self.addTrait2Table(fd=fd, + varianceDataPage=varianceDataPage, + strainlist=other_strains, + mainForm=mainForm, + thisTrait=thisTrait, + attribute_ids=attribute_ids, + attribute_names=attribute_names, + strains='other') if other_strains or (fd.f1list and thisTrait.data.has_key(fd.f1list[0])) \ or (fd.f1list and thisTrait.data.has_key(fd.f1list[1])): print("hjs") fd.allstrainlist = allstrainlist_neworder - ## We put isSE into hddn - #if nCols == 6 and fd.varianceDispName != 'Variance': - # #mainForm.append(HT.Input(name='isSE', value="yes", type='hidden')) - # hddn['isSE'] = "yes" - - #primary_div = HT.Div(primary_table, Id="primary") #Container for table with primary (for example, BXD) strain values - #container.append(primary_div) - - #if other_strains: - # other_div = HT.Div(other_table, Id="other") #Container for table with other (for example, Non-BXD/MDP) strain values - #container.append(HT.Div(' ', height=30)) - #container.append(other_div) self.primary_strains = primary_strains self.other_strains = other_strains - #table.append(HT.TR(HT.TD(container))) - #title5Body.append(table) + def addTrait2Table(self, fd, varianceDataPage, strainlist, mainForm, thisTrait, other_strainsExist=None, attribute_ids=[], attribute_names=[], strains='primary'): #XZ, Aug 23, 2010: I commented the code related to the display of animal case diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee index bbee9c5a..77415d32 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee @@ -141,6 +141,22 @@ $ -> processed += "-" processed += value return processed + + + show_hide_outliers = -> + console.log("FOOBAR in beginning of show_hide_outliers") + label = $('#show_hide_outliers').val() + console.log("lable is:", label) + if label == "Hide Outliers" + $('#show_hide_outliers').val("Show Outliers") + else if label == "Show Outliers" + console.log("Found Show Outliers") + $('#show_hide_outliers').val("Hide Outliers") + console.log("Should be now Hide Outliers") + + console.log("before registering show_hide_outliers") + $('#show_hide_outliers').click(show_hide_outliers) + console.log("after registering show_hide_outliers") _.mixin(_.str.exports()); # Add string fuctions directly to underscore $('#value_table').change(edit_data_change) diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js index 1a067820..6357a7da 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js @@ -10,7 +10,7 @@ }; $(function() { - var edit_data_change, hide_tabs, make_table, process_id, stats_mdp_change, update_stat_values; + var edit_data_change, hide_tabs, make_table, process_id, show_hide_outliers, stats_mdp_change, update_stat_values; hide_tabs = function(start) { var x, _i, _results; _results = []; @@ -173,6 +173,22 @@ } return processed; }; + show_hide_outliers = function() { + var label; + console.log("FOOBAR in beginning of show_hide_outliers"); + label = $('#show_hide_outliers').val(); + console.log("lable is:", label); + if (label === "Hide Outliers") { + return $('#show_hide_outliers').val("Show Outliers"); + } else if (label === "Show Outliers") { + console.log("Found Show Outliers"); + $('#show_hide_outliers').val("Hide Outliers"); + return console.log("Should be now Hide Outliers"); + } + }; + console.log("before registering show_hide_outliers"); + $('#show_hide_outliers').click(show_hide_outliers); + console.log("after registering show_hide_outliers"); _.mixin(_.str.exports()); $('#value_table').change(edit_data_change); console.log("loaded"); -- cgit v1.2.3 From 4cc46d84810ca9492e9a38e1a1f88ab36d214791 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Tue, 28 Aug 2012 16:05:09 -0500 Subject: Various changes related to the webqtlCaseData object and the way values/variances/num_cases are called in DataEditingPage.py --- misc/find.txt | 1 + misc/python_stuff.txt | 1 + wqflask/base/webqtlCaseData.py | 69 ++++-- wqflask/base/webqtlTrait.py | 52 ++-- wqflask/utility/webqtlUtil.py | 7 +- wqflask/wqflask/show_trait/DataEditingPage.py | 276 +++++++++++---------- wqflask/wqflask/show_trait/show_trait_page.py | 27 +- .../wqflask/templates/trait_data_and_analysis.html | 24 +- 8 files changed, 261 insertions(+), 196 deletions(-) create mode 100644 misc/find.txt create mode 100644 misc/python_stuff.txt (limited to 'misc') diff --git a/misc/find.txt b/misc/find.txt new file mode 100644 index 00000000..5c792d80 --- /dev/null +++ b/misc/find.txt @@ -0,0 +1 @@ +find | grep _____ diff --git a/misc/python_stuff.txt b/misc/python_stuff.txt new file mode 100644 index 00000000..f36fe338 --- /dev/null +++ b/misc/python_stuff.txt @@ -0,0 +1 @@ +Classes should always inherit "object" \ No newline at end of file diff --git a/wqflask/base/webqtlCaseData.py b/wqflask/base/webqtlCaseData.py index f68354be..7805df06 100755 --- a/wqflask/base/webqtlCaseData.py +++ b/wqflask/base/webqtlCaseData.py @@ -24,28 +24,59 @@ # # Last updated by GeneNetwork Core Team 2010/10/20 -class webqtlCaseData: - """ - one case data in one trait - """ +print("Mr. Mojo Risin 2") - val = None #Trait Value - var = None #Trait Variance - N = None #Number of individuals +class webqtlCaseData(object): + """one case data in one trait + + """ - def __init__(self, val=val, var=var, N=N): - self.val = val - self.var = var - self.N = N + def __init__(self, name, value=None, variance=None, num_cases=None): + self.name = name + self.value = value # Trait Value + self.variance = variance # Trait Variance + self.num_cases = num_cases # Number of individuals/cases + self.this_id = None # Set a sane default (can't be just "id" cause that's a reserved word) - def __str__(self): + def __repr__(self): str = "" - if self.val != None: - str += "value=%2.3f" % self.val - if self.var != None: - str += " variance=%2.3f" % self.var - if self.N != None: - str += " ndata=%d" % self.N + if self.value != None: + str += "value=%2.3f" % self.value + if self.variance != None: + str += " variance=%2.3f" % self.variance + if self.num_cases != None: + str += " ndata=%d" % self.num_cases return str + + @property + def display_value(self): + if self.value: + return "%2.3f" % self.value + else: + return "x" + + @property + def display_variance(self): + if self.variance: + return "%2.3f" % self.variance + else: + return "x" + + + #try: + # traitVar = thisvar + # dispVar = "%2.3f" % thisvar + #except: + # traitVar = '' + # dispVar = 'x' + + #try: + # traitVal = thisval + # dispVal = "%2.3f" % thisval + #except: + # traitVal = '' + # dispVal = 'x' + - __repr__ = __str__ + #def this_val_full(self): + # strain_name = \ No newline at end of file diff --git a/wqflask/base/webqtlTrait.py b/wqflask/base/webqtlTrait.py index 23b98238..337493ef 100755 --- a/wqflask/base/webqtlTrait.py +++ b/wqflask/base/webqtlTrait.py @@ -225,7 +225,10 @@ class webqtlTrait: - def retrieveData(self, strainlist=[]): + def retrieveData(self, strainlist=None): + + if strainlist == None: + strainlist = [] assert self.db and self.cursor if self.db.type == 'Temp': @@ -328,32 +331,33 @@ class webqtlTrait: self.cursor.execute(query) results = self.cursor.fetchall() self.data.clear() + if results: self.mysqlid = results[0][-1] - if strainlist: - for item in results: - if item[0] in strainlist: - val = item[1] - if val != None: - var = item[2] - ndata = None - if self.db.type in ('Publish', 'Temp'): - ndata = item[3] - self.data[item[0]] = webqtlCaseData(val, var, ndata) + #if strainlist: + for item in results: + #name, value, variance, num_cases = item + if not strainlist or (strainlist and name in strainlist): + #if value != None: + # num_cases = None + # if self.db.type in ('Publish', 'Temp'): + # ndata = item[3] + name = item[0] + self.data[name] = webqtlCaseData(*item) #name, value, variance, num_cases) #end for - else: - for item in results: - val = item[1] - if val != None: - var = item[2] - ndata = None - if self.db.type in ('Publish', 'Temp'): - ndata = item[3] - self.data[item[0]] = webqtlCaseData(val, var, ndata) - #end for - #end if - else: - pass + # else: + # for item in results: + # val = item[1] + # if val != None: + # var = item[2] + # ndata = None + # if self.db.type in ('Publish', 'Temp'): + # ndata = item[3] + # self.data[item[0]] = webqtlCaseData(val, var, ndata) + # #end for + # #end if + #else: + # pass def keys(self): return self.__dict__.keys() diff --git a/wqflask/utility/webqtlUtil.py b/wqflask/utility/webqtlUtil.py index 6409e781..99451e33 100755 --- a/wqflask/utility/webqtlUtil.py +++ b/wqflask/utility/webqtlUtil.py @@ -252,7 +252,12 @@ def FloatList2String(lst): return "" def ListNotNull(lst): - 'Determine if the elements in a list are all null' + '''Obsolete - Use built in function any (or all or whatever) + + + Determine if the elements in a list are all null + + ''' for item in lst: if item is not None: return 1 diff --git a/wqflask/wqflask/show_trait/DataEditingPage.py b/wqflask/wqflask/show_trait/DataEditingPage.py index f9133f27..43f05f14 100755 --- a/wqflask/wqflask/show_trait/DataEditingPage.py +++ b/wqflask/wqflask/show_trait/DataEditingPage.py @@ -11,6 +11,7 @@ import yaml from htmlgen import HTMLgen2 as HT from base import webqtlConfig +from base import webqtlCaseData from utility import webqtlUtil, Plot from base.webqtlTrait import webqtlTrait from dbFunction import webqtlDatabaseFunction @@ -172,17 +173,17 @@ class DataEditingPage(templatePage): if thisTrait == None: thisTrait = webqtlTrait(data=fd.allTraitData, db=None) - # Variance submit page only - if fd.enablevariance and not varianceDataPage: - pass - #title2Body.append("Click the next button to go to the variance submission form.", - # HT.Center(next,reset)) - else: - pass - # We'll get this part working later - print("Calling dispBasicStatistics") - self.dispBasicStatistics(fd, thisTrait) - #self.dispMappingTools(fd, title4Body, thisTrait) + ## Variance submit page only + #if fd.enablevariance and not varianceDataPage: + # pass + # #title2Body.append("Click the next button to go to the variance submission form.", + # # HT.Center(next,reset)) + #else: + # pass + # # We'll get this part working later + # print("Calling dispBasicStatistics") + # self.dispBasicStatistics(fd, thisTrait) + # #self.dispMappingTools(fd, title4Body, thisTrait) ############################# ## Trait Value Table @@ -892,13 +893,13 @@ class DataEditingPage(templatePage): for strain in thisTrait.data.keys(): strainName = strain.replace("_2nd_", "") if strain not in strainlist: - if (thisTrait.data[strainName].val != None): + if thisTrait.data[strainName].value != None: if strain.find('F1') < 0: specialStrains.append(strain) - if (thisTrait.data[strainName].val != None) and (strain not in (fd.f1list + fd.parlist)): + if (thisTrait.data[strainName].value != None) and (strain not in (fd.f1list + fd.parlist)): other_strains.append(strain) #XZ: at current stage, other_strains doesn't include parent strains and F1 strains of primary group else: - if (thisTrait.data[strainName].val != None) and (strain not in (fd.f1list + fd.parlist)): + if (thisTrait.data[strainName].value != None) and (strain not in (fd.f1list + fd.parlist)): primary_strains.append(strain) #XZ: at current stage, the primary_strains is the same as fd.strainlist / ZS: I tried defining primary_strains as fd.strainlist instead, but in some cases it ended up including the parent strains (1436869_at BXD) if len(other_strains) > 3: @@ -941,41 +942,58 @@ class DataEditingPage(templatePage): vals3 = [] #Using all strains/cases for values - for i, strainNameOrig in enumerate(all_strains): + #for strain_type in (all_strains, primary_strains, other_strains): + for strainNameOrig in all_strains: strainName = strainNameOrig.replace("_2nd_", "") - try: - thisval = thisTrait.data[strainName].val - thisvar = thisTrait.data[strainName].var - thisValFull = [strainName, thisval, thisvar] - except: - continue + #try: + print("* type of thisTrait:", type(thisTrait)) + print(" name:", thisTrait.__class__.__name__) + print(" thisTrait:", thisTrait) + print(" type of thisTrait.data[strainName]:", type(thisTrait.data[strainName])) + print(" name:", thisTrait.data[strainName].__class__.__name__) + print(" thisTrait.data[strainName]:", thisTrait.data[strainName]) + thisval = thisTrait.data[strainName].value + print(" thisval:", thisval) + thisvar = thisTrait.data[strainName].variance + print(" thisvar:", thisvar) + thisValFull = [strainName, thisval, thisvar] + print(" thisValFull:", thisValFull) + #except: + # continue vals1.append(thisValFull) + + + #vals1 = [[strainNameOrig.replace("_2nd_", ""), + # thisTrait.data[strainName].val, + # thisTrait.data[strainName].var] + # for strainNameOrig in all_strains]] + # #Using just the RISet strain - for i, strainNameOrig in enumerate(primary_strains): + for strainNameOrig in primary_strains: strainName = strainNameOrig.replace("_2nd_", "") - try: - thisval = thisTrait.data[strainName].val - thisvar = thisTrait.data[strainName].var - thisValFull = [strainName,thisval,thisvar] - except: - continue + #try: + thisval = thisTrait.data[strainName].value + thisvar = thisTrait.data[strainName].variance + thisValFull = [strainName,thisval,thisvar] + #except: + # continue vals2.append(thisValFull) #Using all non-RISet strains only - for i, strainNameOrig in enumerate(other_strains): + for strainNameOrig in other_strains: strainName = strainNameOrig.replace("_2nd_", "") - try: - thisval = thisTrait.data[strainName].val - thisvar = thisTrait.data[strainName].var - thisValFull = [strainName,thisval,thisvar] - except: - continue + #try: + thisval = thisTrait.data[strainName].value + thisvar = thisTrait.data[strainName].variance + thisValFull = [strainName,thisval,thisvar] + #except: + # continue vals3.append(thisValFull) @@ -985,15 +1003,15 @@ class DataEditingPage(templatePage): vals = [] #Using all strains/cases for values - for i, strainNameOrig in enumerate(all_strains): + for strainNameOrig in all_strains: strainName = strainNameOrig.replace("_2nd_", "") - try: - thisval = thisTrait.data[strainName].val - thisvar = thisTrait.data[strainName].var - thisValFull = [strainName,thisval,thisvar] - except: - continue + #try: + thisval = thisTrait.data[strainName].value + thisvar = thisTrait.data[strainName].variance + thisValFull = [strainName,thisval,thisvar] + #except: + # continue vals.append(thisValFull) @@ -1344,24 +1362,26 @@ class DataEditingPage(templatePage): # updated by NL 5-28-2010 # Interval Mapping chrMenu = HT.Select(name='chromosomes1') - chrMenu.append(tuple(["All",-1])) + chrMenu.append(("All",-1)) for i in range(len(fd.genotype)): - if len(fd.genotype[i]) > 1: - chrMenu.append(tuple([fd.genotype[i].name,i])) + if len(fd.genotype[i]) > 1: + chrMenu.append((fd.genotype[i].name, i)) #Menu for Composite Interval Mapping chrMenu2 = HT.Select(name='chromosomes2') - chrMenu2.append(tuple(["All",-1])) + chrMenu2.append(("All",-1)) for i in range(len(fd.genotype)): - if len(fd.genotype[i]) > 1: - chrMenu2.append(tuple([fd.genotype[i].name,i])) + if len(fd.genotype[i]) > 1: + chrMenu2.append((fd.genotype[i].name, i)) if fd.genotype.Mbmap: scaleText = HT.Span("Mapping Scale:", Class="ffl fwb fs12") - scaleMenu1 = HT.Select(name='scale1', onChange="checkUncheck(window.document.dataInput.scale1.value, window.document.dataInput.permCheck1, window.document.dataInput.bootCheck1)") + scaleMenu1 = HT.Select(name='scale1', + onChange="checkUncheck(window.document.dataInput.scale1.value, window.document.dataInput.permCheck1, window.document.dataInput.bootCheck1)") scaleMenu1.append(("Megabase",'physic')) scaleMenu1.append(("Centimorgan",'morgan')) - scaleMenu2 = HT.Select(name='scale2', onChange="checkUncheck(window.document.dataInput.scale2.value, window.document.dataInput.permCheck2, window.document.dataInput.bootCheck2)") + scaleMenu2 = HT.Select(name='scale2', + onChange="checkUncheck(window.document.dataInput.scale2.value, window.document.dataInput.permCheck2, window.document.dataInput.bootCheck2)") scaleMenu2.append(("Megabase",'physic')) scaleMenu2.append(("Centimorgan",'morgan')) @@ -1521,7 +1541,7 @@ class DataEditingPage(templatePage): submitTable = HT.TableLite(cellspacing=0, cellpadding=0, width="100%", Class="target2") - if mappingMethodId != None: + if not mappingMethodId: if int(mappingMethodId) == 1: submitTable.append(mapping_row) submitTable.append(mapping_script) @@ -1567,17 +1587,6 @@ class DataEditingPage(templatePage): title4Body.append(submitTable) - def natural_sort(strain_list): - - sorted = [] - for strain in strain_list: - try: - strain = int(strain) - try: sorted[-1] = sorted[-1] * 10 + strain - except: sorted.append(strain) - except: - sorted.append(strain) - return sorted ########################################## ## Function to display trait tables @@ -1727,8 +1736,9 @@ class DataEditingPage(templatePage): attribute_names=attribute_names, strains='other') - if other_strains or (fd.f1list and thisTrait.data.has_key(fd.f1list[0])) \ - or (fd.f1list and thisTrait.data.has_key(fd.f1list[1])): + #TODO: Figure out why this if statement is written this way - Zach + if (other_strains or (fd.f1list and thisTrait.data.has_key(fd.f1list[0])) + or (fd.f1list and thisTrait.data.has_key(fd.f1list[1]))): print("hjs") fd.allstrainlist = allstrainlist_neworder @@ -1738,85 +1748,93 @@ class DataEditingPage(templatePage): def addTrait2Table(self, fd, varianceDataPage, strainlist, mainForm, thisTrait, - other_strainsExist=None, attribute_ids=[], - attribute_names=[], strains='primary'): + other_strainsExist=None, attribute_ids=None, + attribute_names=None, strains='primary'): + + if attribute_ids == None: + attribute_ids = [] + + if attribute_names == None: + attribute_names = [] + #XZ, Aug 23, 2010: I commented the code related to the display of animal case #strainInfo = thisTrait.has_key('strainInfo') and thisTrait.strainInfo print("in addTrait2Table") table_body = [] vals = [] - for i, strainNameOrig in enumerate(strainlist): - strainName = strainNameOrig.replace("_2nd_", "") - - try: - thisval = thisTrait.data[strainName].val - thisvar = thisTrait.data[strainName].var - thisValFull = [strainName,thisval,thisvar] - except: - continue - - vals.append(thisValFull) - upperBound, lowerBound = Plot.findOutliers(vals) # ZS: Values greater than upperBound or less than lowerBound are considered outliers. + #################### Only used to find upperBound and lowerBound + #for strainNameOrig in strainlist: + # strainName = strainNameOrig.replace("_2nd_", "") + # print("pen: %s - %s" % (strainNameOrig, strainName)) + # thisval = thisTrait.data[strainName].value + # thisvar = thisTrait.data[strainName].variance + # thisValFull = [strainName, thisval, thisvar] + # + # vals.append(thisValFull) + # + #upperBound, lowerBound = Plot.findOutliers(vals) # ZS: Values greater than upperBound or less than lowerBound are considered outliers. the_strains = [] - for i, strainNameOrig in enumerate(strainlist): - strain = {} - print("zyt - strainNameOrig:", strainNameOrig) - trId = strainNameOrig - #selectCheck = HT.Input(type="checkbox", name="selectCheck", value=trId, Class="checkbox", onClick="highlight(this)") - + for counter, strainNameOrig in enumerate(strainlist, 1): strainName = strainNameOrig.replace("_2nd_", "") strainNameAdd = '' if fd.RISet == 'AXBXA' and strainName in ('AXB18/19/20','AXB13/14','BXA8/17'): strainNameAdd = HT.Href(url='/mouseCross.html#AXB/BXA', text=HT.Sup('#'), Class='fs12', target="_blank") - + try: - thisval, thisvar, thisNP = thisTrait.data[strainName].val, thisTrait.data[strainName].var, thisTrait.data[strainName].N - if thisNP: - mainForm.append(HT.Input(name='N'+strainName, value=thisNP, type='hidden')) - else: - pass - except: - thisval = thisvar = 'x' - - try: - traitVal = thisval - dispVal = "%2.3f" % thisval - except: - traitVal = '' - dispVal = 'x' + strain = thisTrait.data[strainName] + except KeyError: + print("No strain %s, let's create it now" % strainName) + strain = webqtlCaseData.webqtlCaseData(strainName) + print("zyt - strainNameOrig:", strainNameOrig) + #trId = strainNameOrig + #selectCheck = HT.Input(type="checkbox", name="selectCheck", value=trId, Class="checkbox", onClick="highlight(this)") - strain['strain_name'] = strainName - strainNameDisp = HT.Span(strainName, Class='fs14 fwn ffl') + + #try: + # thisval, thisvar, thisNP = thisTrait.data[strainName].value, thisTrait.data[strainName].var, thisTrait.data[strainName].N + # if thisNP: + # mainForm.append(HT.Input(name='N'+strainName, value=thisNP, type='hidden')) + # else: + # pass + #except: + # thisval = thisvar = 'x' - if varianceDataPage: - try: - traitVar = thisvar - dispVar = "%2.3f" % thisvar - except: - traitVar = '' - dispVar = 'x' + #thisval = thisTrait.data[strainName].value + #thisvar = thisTrait.data[strainName].variance + #thisTrait.data[strainName].num_cases - if thisval == 'x': - traitVar = '' #ZS: Used to be 0, but it doesn't seem like a good idea for values of 0 to *always* be at the bottom when you sort; it makes more sense to put "nothing" + #strain['strain_name'] = strainName + #strainNameDisp = HT.Span(strainName, Class='fs14 fwn ffl') - #className = 'fs13 b1 c222 ' - #valueClassName = 'fs13 b1 c222 valueField ' - #rowClassName = 'novalue ' - else: - if (thisval >= upperBound) or (thisval <= lowerBound): - strain['outlier'] = "outlier" # We're going to use this as a class, so we want it to be a word - #className = 'fs13 b1 c222 outlier ' - #valueClassName = 'fs13 b1 c222 valueField ' - #rowClassName = 'outlier' - else: - strain['outlier'] = "not_outlier" - #className = 'fs13 b1 c222 ' - #valueClassName = 'fs13 b1 c222 valueField ' - #rowClassName = ' ' + #if varianceDataPage: + #try: + # traitVar = thisvar + # dispVar = "%2.3f" % thisvar + #except: + # traitVar = '' + # dispVar = 'x' + + #if thisval == 'x': + # traitVar = '' #ZS: Used to be 0, but it doesn't seem like a good idea for values of 0 to *always* be at the bottom when you sort; it makes more sense to put "nothing" + # + # #className = 'fs13 b1 c222 ' + # #valueClassName = 'fs13 b1 c222 valueField ' + # #rowClassName = 'novalue ' + #else: + # if (thisval >= upperBound) or (thisval <= lowerBound): + # strain['outlier'] = "outlier" # We're going to use this as a class, so we want it to be a word + # #className = 'fs13 b1 c222 outlier ' + # #valueClassName = 'fs13 b1 c222 valueField ' + # #rowClassName = 'outlier' + # else: + # strain['outlier'] = "not_outlier" + # #className = 'fs13 b1 c222 ' + # #valueClassName = 'fs13 b1 c222 valueField ' + # #rowClassName = ' ' # #if varianceDataPage: # varClassName = valueClassName + str(traitVar) @@ -1851,15 +1869,15 @@ class DataEditingPage(templatePage): # # onChange= "javascript:this.form['V%s'].value=this.form['V%s'].value;" % (strainNameOrig.replace("/", ""), strainNameOrig.replace("/", "")), Class=varClassName) if strains == 'primary': - strain['the_id'] = "Primary_" + str(i+1) + strain.this_id = "Primary_" + str(counter) #table_row = HT.TR(Id="Primary_"+str(i+1), Class=rowClassName) else: - strain['the_id'] = "Other_" + str(i+1) + strain.this_id = "Other_" + str(counter) #table_row = HT.TR(Id="Other_"+str(i+1), Class=rowClassName) - strain['value'] = traitVal - - strain['se'] = dispVar + #strain['value'] = traitVal + # + #strain['se'] = dispVar #if varianceDataPage: #table_row.append(HT.TD(str(i+1), selectCheck, width=45, align='right', Class=className)) #table_row.append(HT.TD(strainNameDisp, strainNameAdd, align='right', width=100, Class=className)) diff --git a/wqflask/wqflask/show_trait/show_trait_page.py b/wqflask/wqflask/show_trait/show_trait_page.py index b1f71e55..b42f5e8e 100644 --- a/wqflask/wqflask/show_trait/show_trait_page.py +++ b/wqflask/wqflask/show_trait/show_trait_page.py @@ -161,24 +161,25 @@ class ShowTraitPage(DataEditingPage): thisTrait.returnURL = '%s&CellID=%s' % (thisTrait.returnURL, CellID) #retrieve trait information - try: - thisTrait.retrieveInfo() - thisTrait.retrieveData() - self.updMysql() - self.cursor.execute("insert into AccessLog(accesstime,ip_address) values(Now(),%s)", user_ip) - self.openMysql() - except Exception as why: - print("Got an exception:", why) - heading = "Retrieve Data" - detail = ["The information you requested is not avaiable at this time."] - self.error(heading=heading, detail=detail) - return + #try: + thisTrait.retrieveInfo() + thisTrait.retrieveData() + self.updMysql() + self.cursor.execute("insert into AccessLog(accesstime,ip_address) values(Now(),%s)", user_ip) + self.openMysql() + #except Exception as why: + # print("Got an exception:", why) + # heading = "Retrieve Data" + # detail = ["The information you requested is not avaiable at this time."] + # self.error(heading=heading, detail=detail) + # return ##read genotype file fd.RISet = thisTrait.riset fd.readGenotype() - if webqtlUtil.ListNotNull(map(lambda x:x.var, thisTrait.data.values())): + #if webqtlUtil.ListNotNull(map(lambda x:x.var, thisTrait.data.values())): + if any([x.variance for x in thisTrait.data.values()]): fd.displayVariance = 1 fd.varianceDispName = 'SE' fd.formID = 'varianceChoice' diff --git a/wqflask/wqflask/templates/trait_data_and_analysis.html b/wqflask/wqflask/templates/trait_data_and_analysis.html index 4bf06d45..94ba0aad 100644 --- a/wqflask/wqflask/templates/trait_data_and_analysis.html +++ b/wqflask/wqflask/templates/trait_data_and_analysis.html @@ -8,7 +8,7 @@ + + {% for sample in sample_type.sample_list %} + + + + + + {# Todo: Add IDs #} + + + {% if sample_type.se_exists() %} + + + {# Todo: Add IDs #} + + {% endif %} + + {# Loop through each attribute type and input value #} + {% for attribute in sample_type.attributes|sort() %} + + {% endfor %} + + {% endfor %} + +
-
+
{# @@ -3059,7 +3059,7 @@
{% for strain_type in (primary_strains, other_strains) %} -
{# Slightly tortuous, but best way to get the id we need #} +
{# Slightly tortuous, but best way to get the id we need #} @@ -3076,20 +3076,20 @@ {% for strain in strain_type %} - + {# Todo: Add IDs #} @@ -3099,8 +3099,8 @@ {# Todo: Add IDs #} {% endfor %} @@ -3133,4 +3133,8 @@ - {% endblock %} + + + + + {% endblock %} -- cgit v1.2.3 From f1d7725c5f7529c5f587bab4ea89d3467b903ddb Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Wed, 12 Sep 2012 18:22:45 -0500 Subject: Worked towards correctly passing form data to correlation page and addressed various bugs that arose while doing so --- misc/byobu_commands.txt | 6 - misc/find.txt | 1 - misc/notes.txt | 41 +++++++ misc/python_stuff.txt | 1 - misc/runserver.txt | 1 - misc/virtual_env.txt | 1 - wqflask/base/webqtlFormData.py | 121 +++++++++++---------- wqflask/wqflask/correlation/CorrelationPage.py | 82 +++++++------- .../new/javascript/trait_data_and_analysis.coffee | 12 +- .../new/javascript/trait_data_and_analysis.js | 9 +- 10 files changed, 160 insertions(+), 115 deletions(-) delete mode 100644 misc/byobu_commands.txt delete mode 100644 misc/find.txt create mode 100644 misc/notes.txt delete mode 100644 misc/python_stuff.txt delete mode 100644 misc/runserver.txt delete mode 100644 misc/virtual_env.txt (limited to 'misc') diff --git a/misc/byobu_commands.txt b/misc/byobu_commands.txt deleted file mode 100644 index 48e85396..00000000 --- a/misc/byobu_commands.txt +++ /dev/null @@ -1,6 +0,0 @@ -byobu -RD -control-a then :multiuser on -control-a then :acladd sam - -type: screen -list for sessions -screen -r zas1024/25679.byobu diff --git a/misc/find.txt b/misc/find.txt deleted file mode 100644 index 5c792d80..00000000 --- a/misc/find.txt +++ /dev/null @@ -1 +0,0 @@ -find | grep _____ diff --git a/misc/notes.txt b/misc/notes.txt new file mode 100644 index 00000000..be023c1d --- /dev/null +++ b/misc/notes.txt @@ -0,0 +1,41 @@ +To get server running: + +Start up virtual environment: +source ~/ve27/bin/activate + +To set WQFLASK_SETTINGS environment variable: +export WQFLASK_SETTINGS=~/gene/wqflask/cfg/zach_settings.py (or wherever file is located) + +To search for commands in history if necessary: +history | grep "(whatever is being searched for)" + +Run server: +python runserver.py + +=========================================== + +Start screen session +byobu -RD (to start) +control-a then :multiuser on +control-a then :acladd sam + +type: screen -list for sessions +screen -r zas1024/25679.byobu + +or if only one: + +screen -r zas1024/ + +=========================================== + +Coffeescript Stuff: + +coffee -c (filename) +coffee -c -w (to watch for changes and recompile) +coffee --help (for information about setting options) + +=========================================== + +Python stuff: + +Classes should always inherit "object" \ No newline at end of file diff --git a/misc/python_stuff.txt b/misc/python_stuff.txt deleted file mode 100644 index f36fe338..00000000 --- a/misc/python_stuff.txt +++ /dev/null @@ -1 +0,0 @@ -Classes should always inherit "object" \ No newline at end of file diff --git a/misc/runserver.txt b/misc/runserver.txt deleted file mode 100644 index 71e3dfd7..00000000 --- a/misc/runserver.txt +++ /dev/null @@ -1 +0,0 @@ -python runserver.py diff --git a/misc/virtual_env.txt b/misc/virtual_env.txt deleted file mode 100644 index 7a8f20be..00000000 --- a/misc/virtual_env.txt +++ /dev/null @@ -1 +0,0 @@ -source ~/ve27/bin/activate diff --git a/wqflask/base/webqtlFormData.py b/wqflask/base/webqtlFormData.py index a9e3b7d4..177c72a2 100755 --- a/wqflask/base/webqtlFormData.py +++ b/wqflask/base/webqtlFormData.py @@ -60,17 +60,12 @@ class webqtlFormData: print("in webqtlFormData start_vars are:", pf(start_vars)) for item in webqtlFormData.attrs: self.__dict__[item] = None - #self.__dict__.update(start_vars) + for item in start_vars: self.__dict__[item] = start_vars[item] - print(" Now self.dict is:", pf(self.__dict__)) - #for item in self.attrs: - # if getattr(self, item, None): - # print("Setting item %s to None" % (item,)) - # self.attrs[item] = None - # else: - # self.attrs[item] = self.attrs[item].strip() + #print(" Now self.dict is:", pf(self.__dict__)) + #Todo: This can't be good below...rework try: self.remote_ip = req.connection.remote_ip except: @@ -151,23 +146,32 @@ class webqtlFormData: def readGenotype(self): - 'read genotype from .geno file' + '''read genotype from .geno file''' if self.RISet == 'BXD300': self.RISet = 'BXD' - else: - pass - assert self.RISet + + assert self.RISet, "self.RISet needs to be set" + #genotype_1 is Dataset Object without parents and f1 #genotype_2 is Dataset Object with parents and f1 (not for intercross) + self.genotype_1 = reaper.Dataset() - self.genotype_1.read(os.path.join(webqtlConfig.GENODIR, self.RISet + '.geno')) + + full_filename = os.path.join(webqtlConfig.GENODIR, self.RISet + '.geno') + + # reaper barfs on unicode filenames, so here we ensure it's a string + full_filename = str(full_filename) + self.genotype_1.read(full_filename) + + print("Got to after read") + try: # NL, 07/27/2010. ParInfo has been moved from webqtlForm.py to webqtlUtil.py; _f1, _f12, _mat, _pat = webqtlUtil.ParInfo[self.RISet] - except: + except KeyError: _f1 = _f12 = _mat = _pat = None - self.genotype_2 =self.genotype_1 + self.genotype_2 = self.genotype_1 if self.genotype_1.type == "riset" and _mat and _pat: self.genotype_2 = self.genotype_1.add(Mat=_mat, Pat=_pat) #, F1=_f1) @@ -177,78 +181,83 @@ class webqtlFormData: else: self.incparentsf1 = 0 self.genotype = self.genotype_1 + self.strainlist = list(self.genotype.prgy) - self.f1list = self.parlist = [] + self.f1list = [] + self.parlist = [] + if _f1 and _f12: self.f1list = [_f1, _f12] if _mat and _pat: self.parlist = [_mat, _pat] + - def readData(self, strainlst=[], incf1=[]): - 'read user input data or from trait data and analysis form' + def readData(self, strainlist, incf1=None): + '''read user input data or from trait data and analysis form''' + + if incf1 == None: + incf1 = [] if not self.genotype: self.readGenotype() - if not strainlst: + if not strainlist: if incf1: - strainlst = self.f1list + self.strainlist + strainlist = self.f1list + self.strainlist else: - strainlst = self.strainlist + strainlist = self.strainlist + #print("before traitfiledata self.traitfile is:", pf(self.traitfile)) - traitfiledata = self.formdata.getfirst('traitfile') - traitpastedata = self.formdata.getfirst('traitpaste') - variancefiledata = self.formdata.getfirst('variancefile') - variancepastedata = self.formdata.getfirst('variancepaste') - Nfiledata = self.formdata.getfirst('Nfile') + traitfiledata = getattr(self, "traitfile", None) + traitpastedata = getattr(self, "traitpaste", None) + variancefiledata = getattr(self, "variancefile", None) + variancepastedata = getattr(self, "variancepaste", None) + Nfiledata = getattr(self, "Nfile", None) + #### Todo: Rewrite below when we get to someone submitting their own trait ##### if traitfiledata: - tt = string.split(traitfiledata) - vals = map(webqtlUtil.StringAsFloat, tt) + tt = traitfiledata.split() + values = map(webqtlUtil.StringAsFloat, tt) elif traitpastedata: - tt = string.split(traitpastedata) - vals = map(webqtlUtil.StringAsFloat, tt) + tt = traitpastedata.split() + values = map(webqtlUtil.StringAsFloat, tt) else: - vals = map(self.FormDataAsFloat, strainlst) - - if len(vals) < len(strainlst): - vals += [None]*(len(strainlst) - len(vals)) - elif len(vals) > len(strainlst): - vals = vals[:len(strainlst)] - else: - pass + values = map(self.FormDataAsFloat, strainlist) + if len(values) < len(strainlist): + values += [None] * (len(strainlist) - len(values)) + elif len(values) > len(strainlist): + values = values[:len(strainlist)] + if variancefiledata: - tt = string.split(variancefiledata) - vars = map(webqtlUtil.StringAsFloat, tt) + tt = variancefiledata.split() + variances = map(webqtlUtil.StringAsFloat, tt) elif variancepastedata: - tt = string.split(variancepastedata) - vars = map(webqtlUtil.StringAsFloat, tt) + tt = variancepastedata.split() + variances = map(webqtlUtil.StringAsFloat, tt) else: - vars = map(self.FormVarianceAsFloat, strainlst) + variances = map(self.FormVarianceAsFloat, strainlist) - if len(vars) < len(strainlst): - vars += [None]*(len(strainlst) - len(vars)) - elif len(vars) > len(strainlst): - vars = vars[:len(strainlst)] - else: - pass + if len(variances) < len(strainlist): + variances += [None]*(len(strainlist) - len(variances)) + elif len(variances) > len(strainlist): + variances = variances[:len(strainlist)] if Nfiledata: tt = string.split(Nfiledata) nstrains = map(webqtlUtil.IntAsFloat, tt) - if len(nstrains) < len(strainlst): - nstrains += [None]*(len(strainlst) - len(nstrains)) + if len(nstrains) < len(strainlist): + nstrains += [None]*(len(strainlist) - len(nstrains)) else: - nstrains = map(self.FormNAsFloat, strainlst) + nstrains = map(self.FormNAsFloat, strainlist) - ##vals, vars, nstrains is obsolete + ##values, variances, nstrains is obsolete self.allTraitData = {} - for i, _strain in enumerate(strainlst): - if vals[i] != None: - self.allTraitData[_strain] = webqtlCaseData(vals[i], vars[i], nstrains[i]) + for i, _strain in enumerate(strainlist): + if values[i] != None: + self.allTraitData[_strain] = webqtlCaseData(values[i], variances[i], nstrains[i]) diff --git a/wqflask/wqflask/correlation/CorrelationPage.py b/wqflask/wqflask/correlation/CorrelationPage.py index 9caf6595..62e4c4ab 100644 --- a/wqflask/wqflask/correlation/CorrelationPage.py +++ b/wqflask/wqflask/correlation/CorrelationPage.py @@ -177,42 +177,37 @@ def get_custom_trait(form_data, cursor): #XZ, 09/18/2008: get the information such as value, variance of the input strain names from the form. -def get_sample_data(form_data): - if form_data.allstrainlist: - mdpchoice = form_data.formdata.getvalue('MDPChoice') - #XZ, in HTML source code, it is "BXD Only" or "BXH only", and so on +def get_sample_data(fd): + print("fd is:", pf(fd.__dict__)) + if fd.allstrainlist: + mdpchoice = fd.MDPChoice + #XZ, in HTML source code, it is "BXD Only", "BXH Only", and so on if mdpchoice == "1": - strainlist = form_data.f1list + form_data.strainlist - #XZ, in HTML source code, it is "MDP Only" + strainlist = fd.f1list + fd.strainlist + #XZ, in HTML source code, it is "Non-BXD Only", "Non-BXD Only", etc elif mdpchoice == "2": strainlist = [] - strainlist2 = form_data.f1list + form_data.strainlist - for strain in form_data.allstrainlist: + strainlist2 = fd.f1list + fd.strainlist + for strain in fd.allstrainlist: if strain not in strainlist2: strainlist.append(strain) #So called MDP Panel if strainlist: - strainlist = form_data.f1list+form_data.parlist+strainlist + strainlist = fd.f1list + fd.parlist+strainlist #XZ, in HTML source code, it is "All Cases" else: - strainlist = form_data.allstrainlist - #XZ, 09/18/2008: put the trait data into dictionary form_data.allTraitData - form_data.readData(form_data.allstrainlist) + strainlist = fd.allstrainlist + #XZ, 09/18/2008: put the trait data into dictionary fd.allTraitData + fd.readData(fd.allstrainlist) else: mdpchoice = None - strainlist = form_data.strainlist - #XZ, 09/18/2008: put the trait data into dictionary form_data.allTraitData - form_data.readData() + strainlist = fd.strainlist + #XZ, 09/18/2008: put the trait data into dictionary fd.allTraitData + fd.readData() return strainlist -def get_mdp_choice(form_data): - if form_data.allstrainlist: - return form_data.formdata.getvalue("MDPChoice") - else: - return None - def get_species(fd, cursor): #XZ, 3/16/2010: variable RISet must be pass by the form @@ -277,7 +272,7 @@ class CorrelationPage(templatePage): RANK_ORDERS = {"1": 0, "2": 1, "3": 0, "4": 0, "5": 1} - def error(self, message, error="Error", heading = None): + def error(self, message, *args, **kw): heading = heading or self.PAGE_HEADING return templatePage.error(heading = heading, detail = [message], error=error) @@ -295,23 +290,30 @@ class CorrelationPage(templatePage): fd.readGenotype() sample_list = get_sample_data(fd) - mdp_choice = get_mdp_choice(fd) # No idea what this is yet + + # Whether the user chose BXD Only, Non-BXD Only, or All Strains + # (replace BXD with whatever the group/inbredset name is) + # "mdp" stands for "mouse diversity panel" This is outdated; it now represents any + # cases/strains from the non-primary group + mdp_choice = fd.MDPChoice if fd.allstrainlist else None + self.species = get_species(fd, self.cursor) #XZ, 09/18/2008: get all information about the user selected database. - target_db_name = fd.formdata.getvalue('database') - self.target_db_name = target_db_name + #target_db_name = fd.corr_dataset + self.target_db_name = fd.corr_dataset - try: - self.db = webqtlDataset(target_db_name, self.cursor) - except: - detail = ["The database you just requested has not been established yet."] - self.error(detail) - return + #try: + #print("target_db_name is:", target_db_name) + self.db = webqtlDataset(self.target_db_name, self.cursor) + #except: + # detail = ["The database you just requested has not been established yet."] + # self.error(detail) + # return # Auth if needed try: - auth_user_for_db(self.db, self.cursor, target_db_name, self.privilege, self.userName) + auth_user_for_db(self.db, self.cursor, self.target_db_name, self.privilege, self.userName) except AuthException, e: detail = [e.message] return self.error(detail) @@ -322,7 +324,7 @@ class CorrelationPage(templatePage): #CF - If less than a minimum number of strains/cases in common, don't calculate anything if len(self.sample_names) < self.corrMinInformative: detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, fd.RISet)] - self.error(heading=PAGE_HEADING,detail=detail) + self.error(heading=None, detail=detail) self.method = get_correlation_method_key(fd) @@ -330,15 +332,16 @@ class CorrelationPage(templatePage): rankOrder = self.RANK_ORDERS[self.method] # CF - Number of results returned - self.returnNumber = int(fd.formdata.getvalue('criteria')) + self.returnNumber = int(fd.criteria) self.record_count = 0 myTrait = get_custom_trait(fd, self.cursor) - # We will not get Literature Correlations if there is no GeneId because there is nothing to look against - self.gene_id = int(fd.formdata.getvalue('GeneId') or 0) + # We will not get Literature Correlations if there is no GeneId because there is nothing + # to look against + self.gene_id = int(fd.GeneId) # We will not get Tissue Correlations if there is no gene symbol because there is nothing to look against self.trait_symbol = myTrait.symbol @@ -359,7 +362,8 @@ class CorrelationPage(templatePage): TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee') mainfmName = webqtlUtil.genRandStr("fm_") - form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden')) + form = HT.Form(cgi = os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), + enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden')) hddn = {'FormID': 'showDatabase', 'ProbeSetID': '_', 'database': self.target_db_name, @@ -369,9 +373,9 @@ class CorrelationPage(templatePage): 'identification': fd.identification} if myTrait: - hddn['fullname']=fd.formdata.getvalue('fullname') + hddn['fullname'] = fd.fullname if mdp_choice: - hddn['MDPChoice']=mdp_choice + hddn['MDPChoice']= mdp_choice #XZ, 09/18/2008: pass the trait data to next page by hidden parameters. diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee index 3ea90b9c..7bfa6d01 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee @@ -170,10 +170,14 @@ $ -> console.log("in beginning of on_corr_submit") values = $('#trait_data_form').serialize() console.log("in on_corr_submit, values are:", values) - $.ajax "/corr_compute", - type: 'GET' - dataType: 'html' - data: values + + params = $.param(values) + window.location.href = "/corr_compute?" + params + + #$.ajax "/corr_compute", + # type: 'GET' + # dataType: 'html' + # data: values $('#corr_compute').click(on_corr_submit) diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js index 0de0297b..e1f870d9 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js @@ -200,15 +200,12 @@ }; $('select[name=corr_method]').change(on_corr_method_change); on_corr_submit = function() { - var values; + var params, values; console.log("in beginning of on_corr_submit"); values = $('#trait_data_form').serialize(); console.log("in on_corr_submit, values are:", values); - return $.ajax("/corr_compute", { - type: 'GET', - dataType: 'html', - data: values - }); + params = $.param(values); + return window.location.href = "/corr_compute?" + params; }; $('#corr_compute').click(on_corr_submit); /* -- cgit v1.2.3 From a979480aae5eaa056c84dc3cb05a5e2443c4fbd0 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Thu, 13 Sep 2012 16:10:27 -0500 Subject: Worked on improving readData and informativeStrains functions in webqtlFormData while trying to get correlation page running --- misc/notes.txt | 6 ++ wqflask/base/webqtlFormData.py | 72 ++++++++++++++-------- wqflask/wqflask/correlation/CorrelationPage.py | 18 +++--- .../new/javascript/trait_data_and_analysis.coffee | 28 ++++----- .../new/javascript/trait_data_and_analysis.js | 11 +--- .../wqflask/templates/trait_data_and_analysis.html | 12 +--- wqflask/wqflask/views.py | 6 +- 7 files changed, 83 insertions(+), 70 deletions(-) (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index be023c1d..9d31fb5d 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -36,6 +36,12 @@ coffee --help (for information about setting options) =========================================== +Unset ASKPASS when trying to git push + +unset SSH_ASKPASS + +=========================================== + Python stuff: Classes should always inherit "object" \ No newline at end of file diff --git a/wqflask/base/webqtlFormData.py b/wqflask/base/webqtlFormData.py index 177c72a2..9f58d437 100755 --- a/wqflask/base/webqtlFormData.py +++ b/wqflask/base/webqtlFormData.py @@ -63,7 +63,7 @@ class webqtlFormData: for item in start_vars: self.__dict__[item] = start_vars[item] - #print(" Now self.dict is:", pf(self.__dict__)) + print(" Now self.dict is:", pf(self.__dict__)) #Todo: This can't be good below...rework try: @@ -216,6 +216,13 @@ class webqtlFormData: #### Todo: Rewrite below when we get to someone submitting their own trait ##### + def to_float(item): + try: + return float(item) + except ValueError: + return None + + print("bottle strainlist is:", strainlist) if traitfiledata: tt = traitfiledata.split() values = map(webqtlUtil.StringAsFloat, tt) @@ -223,12 +230,17 @@ class webqtlFormData: tt = traitpastedata.split() values = map(webqtlUtil.StringAsFloat, tt) else: - values = map(self.FormDataAsFloat, strainlist) + print("mapping formdataasfloat") + #values = map(self.FormDataAsFloat, strainlist) + values = [to_float(getattr(self, key)) for key in strainlist] + print("rocket values is:", values) + if len(values) < len(strainlist): values += [None] * (len(strainlist) - len(values)) elif len(values) > len(strainlist): values = values[:len(strainlist)] + print("now values is:", values) if variancefiledata: @@ -257,40 +269,48 @@ class webqtlFormData: self.allTraitData = {} for i, _strain in enumerate(strainlist): if values[i] != None: - self.allTraitData[_strain] = webqtlCaseData(values[i], variances[i], nstrains[i]) + self.allTraitData[_strain] = webqtlCaseData( + _strain, values[i], variances[i], nstrains[i]) + print("allTraitData is:", pf(self.allTraitData)) - def informativeStrains(self, strainlst=[], incVars = 0): - '''if readData was called, use this to output the informative strains - (strain with values)''' - if not strainlst: - strainlst = self.strainlist + def informativeStrains(self, strainlist=None, include_variances = None): + '''if readData was called, use this to output informative strains (strain with values)''' + + if not strainlist: + strainlist = self.strainlist + strains = [] - vals = [] - vars = [] - for _strain in strainlst: - if self.allTraitData.has_key(_strain): - _val, _var = self.allTraitData[_strain].val, self.allTraitData[_strain].var + values = [] + variances = [] + + #print("self.allTraitData is:", pf(self.allTraitData)) + + for strain in strainlist: + if strain in self.allTraitData: + _val, _var = self.allTraitData[strain].value, self.allTraitData[strain].variance if _val != None: - if incVars: + if include_variances: if _var != None: - strains.append(_strain) - vals.append(_val) - vars.append(_var) + strains.append(strain) + values.append(_val) + variances.append(_var) else: - strains.append(_strain) - vals.append(_val) - vars.append(None) - return strains, vals, vars, len(strains) + strains.append(strain) + values.append(_val) + variances.append(None) + + return strains, values, variances, len(strains) - def FormDataAsFloat(self, key): - try: - return float(self.formdata.getfirst(key)) - except: - return None + #def FormDataAsFloat(self, key): + # + # #try: + # # return float(self.key) + # #except: + # # return None def FormVarianceAsFloat(self, key): diff --git a/wqflask/wqflask/correlation/CorrelationPage.py b/wqflask/wqflask/correlation/CorrelationPage.py index 62e4c4ab..0af5297a 100644 --- a/wqflask/wqflask/correlation/CorrelationPage.py +++ b/wqflask/wqflask/correlation/CorrelationPage.py @@ -157,7 +157,7 @@ def get_correlation_method_key(form_data): #XZ, 09/28/2008: if user select "4", then display 1, 3 and 4. #XZ, 09/28/2008: if user select "5", then display 2, 3 and 5. - method = form_data.formdata.getvalue("method") + method = form_data.method if method not in ["1", "2", "3" ,"4", "5"]: return "1" @@ -166,7 +166,7 @@ def get_correlation_method_key(form_data): def get_custom_trait(form_data, cursor): """Pulls the custom trait, if it exists, out of the form data""" - trait_name = form_data.formdata.getvalue('fullname') + trait_name = form_data.fullname if trait_name: trait = webqtlTrait(fullname=trait_name, cursor=cursor) @@ -178,7 +178,7 @@ def get_custom_trait(form_data, cursor): #XZ, 09/18/2008: get the information such as value, variance of the input strain names from the form. def get_sample_data(fd): - print("fd is:", pf(fd.__dict__)) + #print("fd is:", pf(fd.__dict__)) if fd.allstrainlist: mdpchoice = fd.MDPChoice #XZ, in HTML source code, it is "BXD Only", "BXH Only", and so on @@ -277,10 +277,10 @@ class CorrelationPage(templatePage): return templatePage.error(heading = heading, detail = [message], error=error) def __init__(self, fd): - print("in CorrelationPage __init__ fd is:", pf(fd.__dict__)) + #print("in CorrelationPage __init__ fd is:", pf(fd.__dict__)) # Call the superclass constructor templatePage.__init__(self, fd) - print("in CorrelationPage __init__ now fd is:", pf(fd.__dict__)) + #print("in CorrelationPage __init__ now fd is:", pf(fd.__dict__)) # Connect to the database if not self.openMysql(): return @@ -290,6 +290,7 @@ class CorrelationPage(templatePage): fd.readGenotype() sample_list = get_sample_data(fd) + print("sample_list is", pf(sample_list)) # Whether the user chose BXD Only, Non-BXD Only, or All Strains # (replace BXD with whatever the group/inbredset name is) @@ -321,17 +322,18 @@ class CorrelationPage(templatePage): #XZ, 09/18/2008: filter out the strains that have no value. self.sample_names, vals, vars, N = fd.informativeStrains(sample_list) - #CF - If less than a minimum number of strains/cases in common, don't calculate anything + print("samplenames is:", pf(self.sample_names)) + #CF - If less than a minimum number of strains/cases in common, don't calculate anything if len(self.sample_names) < self.corrMinInformative: detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, fd.RISet)] self.error(heading=None, detail=detail) - self.method = get_correlation_method_key(fd) + self.method = fd.method correlation_method = self.CORRELATION_METHODS[self.method] rankOrder = self.RANK_ORDERS[self.method] - # CF - Number of results returned + # CF - Number of results returned self.returnNumber = int(fd.criteria) self.record_count = 0 diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee index 7bfa6d01..5c153ccb 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee @@ -166,20 +166,20 @@ $ -> $('select[name=corr_method]').change(on_corr_method_change) - on_corr_submit = -> - console.log("in beginning of on_corr_submit") - values = $('#trait_data_form').serialize() - console.log("in on_corr_submit, values are:", values) - - params = $.param(values) - window.location.href = "/corr_compute?" + params - - #$.ajax "/corr_compute", - # type: 'GET' - # dataType: 'html' - # data: values - - $('#corr_compute').click(on_corr_submit) + #on_corr_submit = -> + # console.log("in beginning of on_corr_submit") + # values = $('#trait_data_form').serialize() + # console.log("in on_corr_submit, values are:", values) + # + # params = $.param(values) + # window.location.href = "/corr_compute?" + params + # + # #$.ajax "/corr_compute", + # # type: 'GET' + # # dataType: 'html' + # # data: values + # + #$('#corr_compute').click(on_corr_submit) ### End Calculate Correlations Code diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js index e1f870d9..55acbada 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js @@ -10,7 +10,7 @@ }; $(function() { - var edit_data_change, hide_tabs, make_table, on_corr_method_change, on_corr_submit, process_id, show_hide_outliers, stats_mdp_change, update_stat_values; + var edit_data_change, hide_tabs, make_table, on_corr_method_change, process_id, show_hide_outliers, stats_mdp_change, update_stat_values; hide_tabs = function(start) { var x, _i, _results; _results = []; @@ -199,15 +199,6 @@ return $('#' + corr_method + "_r_desc").show().effect("highlight"); }; $('select[name=corr_method]').change(on_corr_method_change); - on_corr_submit = function() { - var params, values; - console.log("in beginning of on_corr_submit"); - values = $('#trait_data_form').serialize(); - console.log("in on_corr_submit, values are:", values); - params = $.param(values); - return window.location.href = "/corr_compute?" + params; - }; - $('#corr_compute').click(on_corr_submit); /* End Calculate Correlations Code */ diff --git a/wqflask/wqflask/templates/trait_data_and_analysis.html b/wqflask/wqflask/templates/trait_data_and_analysis.html index a5d0e05c..c25db7c4 100644 --- a/wqflask/wqflask/templates/trait_data_and_analysis.html +++ b/wqflask/wqflask/templates/trait_data_and_analysis.html @@ -8,7 +8,7 @@
{{ loop.index }} - + - {{ strain.strain_name }} + {{ strain.name }} - -
-
+ {# @@ -683,13 +683,7 @@

  Calculate Correlations

-

+

@@ -771,7 +765,7 @@ "sample_method" value="spearman">

-

+

The Sample Correlation diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py index 114ec458..677c7f43 100644 --- a/wqflask/wqflask/views.py +++ b/wqflask/wqflask/views.py @@ -75,10 +75,10 @@ def showDatabaseBXD(): print("showDatabaseBXD template_vars:", pf(template_vars.__dict__)) return render_template("trait_data_and_analysis.html", **template_vars.__dict__) -@app.route("/corr_compute") +@app.route("/corr_compute", methods=('POST',)) def corr_compute(): - print("In corr_compute") - fd = webqtlFormData.webqtlFormData(request.args) + #print("In corr_compute, request.args is:", pf(request.form)) + fd = webqtlFormData.webqtlFormData(request.form) print("Have fd") template_vars = CorrelationPage.CorrelationPage(fd) print("Made it to rendering") -- cgit v1.2.3 From 6e89a31cf7e654fb1b99d6fa86e7cdabb68cb243 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Wed, 19 Sep 2012 16:07:41 -0500 Subject: Created correlation page template file, changed the form value name for pearson/spearman option, and put fd in CorrelationPage.py as self.__dict__ --- misc/todo.txt | 2 + wqflask/wqflask/correlation/CorrelationPage.py | 17 +- wqflask/wqflask/templates/correlation_page.html | 1358 ++++++++++++++++++++ .../wqflask/templates/trait_data_and_analysis.html | 9 +- 4 files changed, 1378 insertions(+), 8 deletions(-) create mode 100644 misc/todo.txt create mode 100644 wqflask/wqflask/templates/correlation_page.html (limited to 'misc') diff --git a/misc/todo.txt b/misc/todo.txt new file mode 100644 index 00000000..d5374490 --- /dev/null +++ b/misc/todo.txt @@ -0,0 +1,2 @@ +- Fix issue with parents appearing twice in primary samples table +- Check standard deviation value against GeneNetwork's standard deviation value \ No newline at end of file diff --git a/wqflask/wqflask/correlation/CorrelationPage.py b/wqflask/wqflask/correlation/CorrelationPage.py index 0af5297a..1f7f2553 100644 --- a/wqflask/wqflask/correlation/CorrelationPage.py +++ b/wqflask/wqflask/correlation/CorrelationPage.py @@ -279,11 +279,16 @@ class CorrelationPage(templatePage): def __init__(self, fd): #print("in CorrelationPage __init__ fd is:", pf(fd.__dict__)) # Call the superclass constructor + + # Put everything in fd into self + self.__dict__.update(fd.__dict__) + templatePage.__init__(self, fd) + #print("in CorrelationPage __init__ now fd is:", pf(fd.__dict__)) # Connect to the database if not self.openMysql(): - return + returnt # Read the genotype from a file if not fd.genotype: @@ -315,7 +320,7 @@ class CorrelationPage(templatePage): # Auth if needed try: auth_user_for_db(self.db, self.cursor, self.target_db_name, self.privilege, self.userName) - except AuthException, e: + except AuthException as e: detail = [e.message] return self.error(detail) @@ -327,9 +332,11 @@ class CorrelationPage(templatePage): if len(self.sample_names) < self.corrMinInformative: detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, fd.RISet)] self.error(heading=None, detail=detail) - - - self.method = fd.method + + for key, value in self.__dict__.items(): + if key.startswith("corr"): + print("[red] %s - %s" % (key, value)) + correlation_method = self.CORRELATION_METHODS[self.method] rankOrder = self.RANK_ORDERS[self.method] diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html new file mode 100644 index 00000000..40c14aaa --- /dev/null +++ b/wqflask/wqflask/templates/correlation_page.html @@ -0,0 +1,1358 @@ + {% extends "base.html" %} + {% block title %}Correlation{% endblock %} + {% block content %} + + + + + + + + {% endblock %} \ No newline at end of file diff --git a/wqflask/wqflask/templates/trait_data_and_analysis.html b/wqflask/wqflask/templates/trait_data_and_analysis.html index 59dfedb3..05fd457a 100644 --- a/wqflask/wqflask/templates/trait_data_and_analysis.html +++ b/wqflask/wqflask/templates/trait_data_and_analysis.html @@ -760,9 +760,12 @@ -
+ + + + +

Correlation Table

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Values of Record 1436869_at in the Hippocampus Consortium M430v2 (Jun06) PDNN database were compared to all 45101 records in the Hippocampus Consortium M430v2 (Jun06) PDNN database. The top 500 correlations ranked by the Genetic Correlation (Pearson's r) are displayed. You can resort this list using the small arrowheads in the top row.
Click the correlation values to generate scatter plots. Select the Record ID to open the Trait Data and Analysis form. Select the symbol to open NCBI Entrez.



Select AllSelect NoneInvert SelectionAdd To Collection   Gene WeaverGCATGene Set Analysis Toolkit
 SelectDeselect Invert   AddGene Weaver  GCATGene Set
Network GraphsCorrelation Matrix and PCAPartial CorrelationCompare CorrelatesMultiple Interval MappingQTL Heat Map and Clustering
 Graph Matrix PartialCompareQTL MapHeat Map
 
+
      +
 
 
Record
ID
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Symbol

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Description

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Location
Chr and Mb
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Mean
Expr
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Max
LRS
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Max LRS Location
Chr and Mb
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Sample
r ?
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N
Cases
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Sample
p(r) ?
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Lit
Corr ?
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Tissue
r ?
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Tissue
p(r) ?
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1 +1436869_atShhsonic hedgehog; mid distal 3' UTRChr5: 28.7836959.27912.8Chr1: 193.7319961.000710.00e+001.0001.000--
2 +1434987_atAldh2aldehyde dehydrogenase 2, mitochondrial; last two exonsChr5: 122.0180108.45311.7Chr3: 10.3271010.973710.00e+000.2370.1440.483
3 +1420293_atAytl2acyltransferase like 2; distal 3' UTRChr13: 73.6536748.49212.2Chr15: 13.1492480.959710.00e+000.2270.1640.424
4 +1459623_atA630075K04RikA630075K04Rik; intergenic sequenceChr2: 156.6281617.41813.3Chr15: 13.1492480.958710.00e+00---0.1900.352
5 +1449781_atAA517650AA517650Chr13: 47.4548377.33111.4Chr15: 13.1492480.955710.00e+00------
6 +1434988_x_atAldh2aldehyde dehydrogenase 2, mitochondrialChr5: 122.0177609.37211.9Chr3: 10.3271010.954710.00e+000.2370.1440.483
7 +1458048_at6720406K03hypothetical protein 6720406K03Chr11: 31.9955367.82511.3ChrX: 112.6373530.954710.00e+00------
8 +1455707_atC130037N17RikRIKEN cDNA C130037N17 geneChr15: 8.9964908.98811.3Chr1: 193.7319960.953710.00e+00------
9 +1454251_at1700034J04RikRIKEN cDNA 1700034J04 geneChr12: 11.2283148.10811.5ChrX: 112.6373530.951710.00e+00------
10 +1415862_atTyrp1tyrosinase-related protein 1 (brown locus, glaucoma-associated pigment epithelium catalase); last three exons and proximal 3' UTRChr4: 80.4925607.42414.3Chr15: 13.1492480.948710.00e+000.310-0.0270.894
11 +1444984_atC77649expressed sequence C77649Chr13: 32.8335027.00311.1ChrX: 112.6373530.946710.00e+00------
12 +1457982_at1700052M18RikRIKEN cDNA 1700052M18 gene; non-coding, well expressed (hippocampus) antisense sequence in promoter of Gm1564Chr11: 102.5263369.59410.4ChrX: 112.6373530.945710.00e+00------
13 +1421186_atCcr2chemokine (C-C motif) receptor 2Chr9: 124.0226997.93911.6ChrX: 112.6373530.944710.00e+000.327-0.1210.555
14 +1438549_a_atSrrserine racemase; last exonChr11: 74.72174110.78812.0ChrX: 112.6373530.944710.00e+000.331-0.1130.582
15 +1420604_atHesx1homeo box gene expressed in ES cellsChr14: 27.8146167.07313.9Chr15: 13.1492480.943710.00e+000.471-0.1230.549
16 +1444022_at1110054O05RikRIKEN cDNA 1110054O05; putative exon (from ESTs)Chr14: 14.9437319.38012.0Chr15: 13.1492480.943710.00e+000.1560.4440.023
17 +1439404_x_atZfxzinc finger protein X-linked; exons 5 and 6ChrX: 91.3261099.49912.1ChrX: 112.6373530.942710.00e+000.3170.1000.626
18 +1420228_atAsh2lash2 (absent, small, or homeotic) 2-like (histone H3-K4 methylation); antisense in distal 3' UTRChr8: 26.9274318.34913.8Chr3: 10.3271010.942710.00e+000.227-0.2140.294
19 +1424612_at9330161F08RikRIKEN cDNA 9330161F08 geneChr15: 34.5047228.35611.9Chr1: 193.7319960.942710.00e+000.145----
20 +1425815_a_atHmmrhyaluronan mediated motility receptor (RHAMM)Chr11: 40.5149738.4759.8Chr19: 53.4592780.941710.00e+000.374-0.2220.277
21 +1421367_at4930549C01RikRIKEN cDNA 4930549C01 geneChr4: 136.1664397.94411.6Chr15: 13.1492480.941710.00e+00---0.1210.558
22 +1431884_at1110019B22RikRIKEN cDNA 1110019B22 geneChr10: 95.1811607.74212.1Chr15: 13.1492480.939710.00e+00---0.0890.666
23 +1432588_at5830468K08RikRIKEN cDNA 5830468K08 thymus EST; 3' end of non-coding sequence from AK018043 (antisense in last intron of Ythdf3)Chr3: 16.1104157.42013.2Chr15: 13.1492480.938710.00e+00------
24 +1444789_atF730011B02ESTsChr15: 99.0800207.96711.1Chr15: 13.1492480.938710.00e+00------
25 +1441483_atSlitrk2Slitrk2 SLIT and NTRK-like family, member 2ChrX: 63.9086796.88412.1Chr1: 193.7319960.937710.00e+000.226-0.1180.566
26 +1424148_a_atStap2signal transducing adaptor family member 2; exonChr17: 56.1372038.39811.5ChrX: 112.6373530.937710.00e+000.231-0.1220.554
27 +1454463_at3110047M22RikRIKEN cDNA 3110047M22 geneChr6: 50.5177278.09110.9Chr3: 149.0891950.937710.00e+00------
28 +1442589_atTrpc5transient receptor potential cation channel, subfamily C, member 5ChrX: 140.8524326.56111.7Chr3: 10.3271010.936710.00e+000.222-0.1070.604
29 +1430193_at5730505K17RikRIKEN cDNA 5730505K17 geneChr2: 118.9232287.60613.6Chr15: 13.1492480.935710.00e+00------
30 +1435040_atIrak3interleukin-1 receptor-associated kinase 3Chr10: 119.5789866.39812.9Chr1: 193.7319960.934710.00e+000.236-0.1710.403
31 +1456734_atTxn1thioredoxin 1; last exon and proximal 3' UTRChr4: 57.9566337.38814.7Chr3: 10.3271010.933710.00e+000.314-0.1970.336
32 +1420143_atD930043C02Rikmembrane-associated nucleic acid binding proteinChr6: 116.1023018.4989.2Chr15: 13.1492480.932710.00e+000.188----
33 +1446615_atD230007K08RikRIKEN cDNA D230007K08ChrX: 6.1146458.82211.5Chr15: 13.1492480.932710.00e+000.222----
34 +1451774_atOTTMUSG00000002196similar to keratin associated protein 4-10 (predicted gene, OTTMUSG00000002196) protein coding; keratin associated protein 4.10Chr11: 99.7120428.05112.5Chr15: 13.1492480.932710.00e+00---0.1170.568
35 +1443940_atLrrc22leucine rich repeat containing 22 (retina-restricted expression, 10q23.1, candidate retinopathy gene); mid 3' UTRChr14: 37.8863327.90212.4ChrX: 112.6373530.931710.00e+00------
36 +1447191_at5430401O09RikESTs, Weakly similar to RIKEN cDNA 5730493B19 [] [M.musculus]Chr15: 99.9730238.14110.6Chr1: 193.7319960.931710.00e+00------
37 +1457757_atLOC269389embyronic retinal HMG-box protein; exons 6, 7, and 8Chr2: 163.0735317.14012.1Chr3: 149.0891950.930710.00e+000.239----
38 +1441289_atC1orf54human chromosome 1 open reading frame 54Chr3: 95.6971958.08011.6Chr15: 13.1492480.930710.00e+00------
39 +1456561_s_atZfp393zinc finger protein 393Chr4: 117.4292807.21917.2Chr15: 13.1492480.930710.00e+000.256----
40 +1425493_atBmpr1abone morphogenetic protein receptor, type 1A; mid proximal 3' UTRChr14: 35.2268039.98411.0ChrX: 112.6373530.929710.00e+000.4300.1040.613
41 +1452986_atHgdhomogentisate 1, 2-dioxygenaseChr16: 37.6317617.26813.3Chr3: 10.3271010.929710.00e+000.258-0.0790.701
42 +1447563_atRps3ribosomal protein S3; last exon and 3' UTRChr7: 106.6273988.14110.0Chr2: 178.9427860.928710.00e+000.284-0.1130.581
43 +1436749_atMesdc2mesoderm development candiate 2Chr7: 91.0482808.40810.6Chr15: 13.1492480.928710.00e+000.253-0.1460.477
44 +1439245_atTnrc6trinucleotide repeat containing 6a; 3' UTRChr7: 130.3367897.82814.2Chr3: 10.3271010.927710.00e+000.292----
45 +1450353_atSlc25a17peroxisomal integral membrane protein 47; mid 3' UTRChr15: 81.1496437.94617.2Chr15: 13.1492480.927710.00e+000.158-0.0440.831
46 +1446656_atAngpt1angiopoietin 1Chr15: 42.4521598.05812.9ChrX: 127.0092660.927710.00e+000.334-0.0960.642
47 +1458006_atCpmcarboxypeptidase M; intron or possible short form 3' UTR from EST AK087584Chr10: 117.1137238.20916.6Chr15: 13.1492480.927710.00e+000.3230.3600.071
48 +1441754_atBB807463BB807463; antisense in BB807463Chr19: 6.9162227.90211.9ChrX: 112.6373530.927710.00e+00------
49 +1432266_at9430077C05RikRIKEN cDNA 9430077C05 geneChr2: 20.7053487.10314.1ChrX: 112.6373530.926710.00e+000.364----
50 +1451950_a_atCd80CD80 antigenChr16: 38.4739797.83611.3Chr15: 13.1492480.925710.00e+000.251-0.1190.564
51 +1448011_atVps13cvacuolar protein sorting 13C; antisense in 3' UTRChr9: 67.8434778.47210.5ChrX: 112.6373530.925710.00e+000.207-0.2320.254
52 +1419992_x_at1200014K04RikRIKEN cDNA 1200014K04 geneChr19: 10.1123217.28213.0Chr3: 10.3271010.924710.00e+00------
53 +1459589_atCryl1crystallin, lamda 1 (L-gulonate 3-dehydrogenase); last exon and proximal 3' UTRChr14: 57.89387210.40011.3Chr19: 53.4592780.923710.00e+000.2070.0010.996
54 +1443421_s_atPcdhb15protocadherin beta 15Chr18: 37.6355307.83412.1Chr2: 179.2574630.923710.00e+000.174-0.1550.451
55 +1444961_atC230066G23RikRIKEN cDNA C230066G23 geneChr4: 24.2977217.37713.8Chr15: 13.1492480.923710.00e+00---0.2580.203
56 +1422086_atTbx19T-box 19Chr1: 167.0684167.55219.8Chr15: 13.1492480.923710.00e+000.299-0.1920.346
57 +1433260_atPick1protein interacting with C kinase 1 (protein kinase C, alpha binding protein); 5' UTR of Pick1 (from AK015656)Chr15: 79.0589856.87610.7Chr15: 13.1492480.923710.00e+000.266-0.1680.411
58 +1429895_at2310010G23RikRIKEN cDNA 2310010G23 geneChrX: 34.3548777.95111.7Chr15: 13.1492480.922710.00e+00------
59 +1458550_atMyo1dmyosin IDChr11: 80.4870137.42211.8Chr1: 193.7319960.922710.00e+000.2260.0900.661
60 +1453673_atAtp13a4ATPase type 13A4Chr16: 29.5405457.63110.8Chr15: 13.1492480.922710.00e+000.2050.0690.738
61 +1433809_atDdx5DEAD (Asp-Glu-Ala-Asp) box polypeptide 5; last exon and 3' UTRChr11: 106.5980158.07613.9Chr15: 13.1492480.922710.00e+00--0.2280.263
62 +1431063_at5830445O15RikRIKEN cDNA 5830445O15 geneChr2: 120.2864967.79413.4ChrX: 112.6373530.921710.00e+00------
63 +1444682_atOxct3-oxoacid CoA transferase 1 (succinyl-CoA:3-ketoacid-coenzyme A transferase 1, mitochondrial precursor); distal 3' UTRChr15: 3.9750217.36113.2Chr3: 10.3271010.921710.00e+00------
64 +1459654_atGlceglucocorticoid induced transcript 1Chr9: 61.9601297.82313.6Chr2: 179.2574630.921710.00e+000.2560.2330.251
65 +1445285_atC630041L24Riknon-coding RNA RIKEN cDNA C630041L24 exclusively hippocampus CA1 expressed; non-coding sequence antisense in Hrh3 promoterChr2: 179.8405037.76413.9Chr3: 10.3271010.921710.00e+000.193----
66 +1425038_atSlc22a19solute carrier family 22 (organic anion transporter), member 19Chr19: 7.7477046.91714.1Chr15: 13.1492480.920710.00e+000.194----
67 +1458634_atCd47CD47 antigen (Rh-related antigen, integrin-associated signal transducer); last intronChr16: 49.8566757.29913.4Chr2: 180.8255810.920710.00e+000.3220.1890.355
68 +1418065_atRag2recombination activating gene 2Chr2: 101.4708127.43113.3Chr3: 10.3271010.920710.00e+000.293-0.1270.538
69 +1451661_atAkap4A kinase (PRKA) anchor protein 4ChrX: 6.6547357.82013.2ChrX: 112.6373530.920710.00e+000.243-0.1140.580
70 +1431561_a_atDhx34DEAH (Asp-Glu-Ala-His) box polypeptide 34; last three exonsChr7: 16.7826528.50614.7Chr15: 13.1492480.919710.00e+000.2010.0460.823
71 +1435887_atSerpina11serine (or cysteine) proteinase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 11; last three exons and proximal 3' UTRChr12: 105.2184827.61712.0ChrX: 112.6373530.919710.00e+000.117-0.0980.634
72 +1440846_atScaiScai suppressor of cancer cell invasion; exons 7, 8, and 9Chr2: 38.9624588.58610.4Chr1: 193.7319960.919710.00e+000.191----
73 +1444938_at1700081L11RikRIKEN cDNA 1700081L11 geneChr17: 39.8399557.62210.4ChrX: 127.0092660.919710.00e+000.142-0.2960.142
74 +1440937_atD330023I21RikRIKEN cDNA D330023I21 geneChrX: 136.4790017.43111.7Chr3: 10.3271010.919710.00e+00------
75 +1430767_a_at4930543E12RikMus musculus transcribed sequencesChr7: 120.2437236.74810.9Chr15: 13.1492480.919710.00e+00---0.2510.217
76 +1458193_atFabp9fatty acid binding protein 9, testisChr3: 10.1800508.23710.3Chr3: 10.3271010.919710.00e+00---0.0920.656
77 +1439121_atH2-T17histocompatibility 2, T region locus 17Chr17: 36.1757438.95110.4ChrX: 112.6373530.918710.00e+00--0.0800.698
78 +1419991_at1200014K04RikRIKEN cDNA 1200014K04 gene--7.17012.7Chr3: 10.3271010.918710.00e+00------
79 +1419295_atCreb3l1cAMP responsive element binding protein 3-like; 3' UTRChr2: 91.8225458.65811.3Chr2: 179.2574630.918710.00e+000.283-0.0600.771
80 +1444697_at4732490B19RikRIKEN cDNA 4732490B19 geneChr11: 113.0646907.39513.9Chr3: 10.3271010.918710.00e+00------
81 +1459705_atOlfm3olfactomedin 3; intron (from BE980857)Chr3: 114.6479587.24511.6Chr2: 178.9427860.918710.00e+000.2100.0660.749
82 +1448092_x_atSerpina4-ps1clone IMAGE:4194299, mRNAChr12: 105.3248227.62611.8ChrX: 127.0092660.918710.00e+000.311-0.0160.938
83 +1433302_atCdh10cadherin 10Chr15: 18.7488218.19910.9ChrX: 112.6373530.918710.00e+000.2820.2670.187
84 +1431904_at4933427G17RikRIKEN cDNA 4933427G17 geneChr7: 128.1424157.49910.0Chr15: 13.1492480.917710.00e+00---0.1180.566
85 +1441303_at0610031G08RikESTsChr2: 144.3814657.04612.4ChrX: 112.6373530.917710.00e+00---0.0340.869
86 +1427807_at4930448N21RikRIKEN cDNA 4930448N21 geneChr8: 48.5002747.73611.2Chr3: 10.3271010.917710.00e+00--0.0610.767
87 +1444479_at5730437C12RikRIKEN cDNA 5730437C12 geneChr9: 76.3360056.85514.3Chr15: 13.1492480.916710.00e+00------
88 +1458835_atA330033J07RikRIKEN cDNA A330033J07 geneChr12: 32.4702098.13310.8Chr15: 13.1492480.916710.00e+00------
89 +1438825_atCalm3calmodulin 3; poor probe set specificity (3' UTR)Chr7: 17.5020746.87814.5Chr3: 10.3271010.916710.00e+00--0.1290.531
90 +1439055_at2210409E12RikRIKEN cDNA 2210409E12 geneChr11: 88.8260527.28214.5Chr15: 13.1492480.916710.00e+00---0.2480.221
91 +1457171_atC530043A13RikRIKEN cDNA C530043A13 geneChr7: 86.6631528.4809.4Chr3: 10.3271010.915710.00e+00---0.0070.974
92 +1451856_atTnrc15trinucleotide repeat containing 15Chr1: 89.2250468.50110.9ChrX: 127.0092660.915710.00e+000.238-0.0350.864
93 +1454460_atSerpine2serpin peptidase inhibitor, clade E (plasminogen activator inhibitor type 1), member 2 (protease nexin 1)Chr1: 79.8355407.49410.5Chr15: 13.1492480.915710.00e+000.4010.0230.910
94 +1425999_atCfhl1complement component factor h-related like 1; locally repetitive 5' elementChr1: 141.5943627.16211.1ChrX: 112.6373530.915710.00e+000.194----
95 +1430963_atGcnt3glucosaminyl (N-acetyl) transferase 3, mucin typeChr9: 69.8810297.07211.9Chr15: 13.1492480.915710.00e+000.289-0.0500.809
96 +1431347_atD730039F16RikRIKEN cDNA D730039F16 geneChr2: 34.7273836.95313.5ChrX: 112.6373530.914710.00e+00--0.0400.848
97 +1446038_atXtrp3s1extra-toes spottingChr9: 123.5868918.42811.1ChrX: 112.6373530.914710.00e+00---0.1410.493
98 +1436288_at1700049M11RikRIKEN cDNA 1700049M11 geneChr2: 164.0246389.14110.4ChrX: 112.6373530.914710.00e+00------
99 +1457608_atGimap8GTPase, IMAP family member 8Chr6: 48.6055168.97010.1Chr1: 55.1843220.914710.00e+000.241-0.0300.886
100 +1458329_x_at1110051B16RikRIKEN cDNA 1110051B16 gene--6.84212.7ChrX: 112.6373530.914710.00e+000.255-0.1810.376
101 +1458270_atKcnb1potassium voltage gated channel, Shab-related subfamily, member 1; 5' UTRChr2: 167.0157567.24914.4Chr15: 13.1492480.913710.00e+000.3010.2180.285
102 +1438387_x_atTop3btopoisomerase (DNA) III betaChr16: 16.8929698.20913.8Chr3: 10.3271010.913710.00e+000.248-0.1180.567
103 +1452568_atWbscr17Williams-Beuren syndrome chromosome region 17 homolog (human)Chr5: 131.4350046.64412.2ChrX: 112.6373530.913710.00e+000.2040.0260.900
104 +1453264_atMarveld3MARVEL (membrane-associating) domain containing 3Chr8: 112.4769226.88510.4Chr3: 10.3271010.913710.00e+00--0.0680.740
105 +1459301_atMrg1myeloid ecotropic viral integration site-related gene 1Chr2: 115.8611688.78710.6ChrX: 127.0092660.913710.00e+000.4780.3870.051
106 +1422370_atOlfr49olfactory receptor 49Chr14: 54.9017316.73810.6Chr2: 66.8059560.913710.00e+00---0.2640.192
107 +1460105_atB3galnt2UDP-GalNAc:betaGlcNAc beta 1,3-galactosaminyltransferase, polypeptide 2Chr13: 14.0689867.06111.7Chr2: 67.6943500.912710.00e+000.169-0.2440.230
108 +1450636_s_atAkp5alkaline phosphatase 5Chr1: 88.9833617.35211.1Chr2: 179.2574630.912710.00e+000.3910.2490.219
109 +1460067_atCcr2chemokine (C-C motif) receptor 2 (from EST AK046579); putative far 3' UTRChr9: 124.0277089.49611.2Chr2: 179.2574630.912710.00e+000.327-0.1210.555
110 +1442958_atMpgN-methylpurine-DNA glycosylaseChr13: 32.6248747.58215.0Chr15: 13.1492480.911710.00e+000.3040.3970.045
111 +1458881_atBC034664cDNA sequence BC034664Chr13: 36.2025377.94510.0Chr15: 13.1492480.911710.00e+000.178----
112 +1458350_atOpa3optic atrophy 3 (autosomal recessive, with chorea and spastic paraplegia); antisense in far 3' end of Opa3 UTRChr7: 19.8331337.96413.2Chr15: 13.1492480.911710.00e+000.219-0.1930.345
113 +1428965_at1700007N18RikRIKEN cDNA 1700007N18 geneChr16: 50.5902007.27315.5Chr15: 13.1492480.911710.00e+000.221----
114 +1433318_at5430416B10RikRIKEN cDNA 5430416B10 geneChr2: 113.4123126.9149.2Chr19: 53.4592780.910710.00e+00------
115 +1446413_at4930430F21RikRIKEN cDNA 4930430F21 geneChr10: 86.7606047.71614.4Chr15: 13.1492480.910710.00e+00------
116 +1459427_atStamsignal transducing adaptor molecule (SH3 domain and ITAM motif) 1Chr2: 14.0638067.76415.1Chr15: 13.1492480.910710.00e+000.2420.1000.626
117 +1454355_at1810021M19RikRIKEN cDNA 1810021M19 geneChr2: 165.7131417.56410.1Chr15: 13.1492480.910710.00e+00------
118 +1458804_atSlco4c1solute carrier organic anion transporter family, member 4C1Chr1: 98.7423007.60211.1Chr2: 179.2574630.910710.00e+000.139-0.1830.371
119 +1459383_atBC049807cDNA sequence BC049807Chr17: 21.7055878.04212.6ChrX: 112.6373530.910710.00e+00---0.1120.587
120 +1437703_atFbxw14F-box and WD-40 domain protein 14 (F-box protein EG382156); last two exonsChr9: 109.2813869.24410.2Chr2: 179.2574630.910710.00e+00---0.1570.443
121 +1458191_atFoxp2forkhead box P2 (neocortex layer 6 signature gene); last exon and 3' UTR of short formChr6: 15.3613978.24210.8ChrX: 112.6373530.909710.00e+000.4040.3970.045
122 +1441042_atFgf1fibroblast growth factor 1Chr18: 39.0063467.65711.6ChrX: 112.6373530.909710.00e+000.4400.2150.292
123 +1419090_x_atKlk26kallikrein 26Chr7: 51.2713477.37114.5ChrX: 112.6373530.909710.00e+000.255----
124 +1429672_atArhgap15Rho GTPase activating protein 15Chr2: 43.8503617.30213.6Chr3: 10.3271010.909710.00e+000.220-0.2090.306
125 +1440733_at1200009O22RikRIKEN cDNA 1200009O22 geneChr6: 53.8177639.17511.2ChrX: 112.6373530.909710.00e+00--0.4240.031
126 +1450797_a_atCbx1chromobox homolog 1 (Drosophila HP1 beta)Chr11: 96.6695167.51312.7Chr15: 13.1492480.908710.00e+000.243-0.2150.292
127 +1442976_atC81072expressed sequence C81072Chr3: 75.2570167.08212.1Chr3: 10.3271010.908710.00e+00------
128 +1441116_atAU024404AU024404 EST; antisense in first intron of PamChr1: 99.9567387.84413.5Chr19: 53.4592780.908710.00e+00------
129 +1444023_atAnk2ankyrin 2, brainChr3: 126.7278066.86711.0Chr3: 10.3271010.908710.00e+000.2910.0280.892
130 +1444238_atD030060M11RikESTsChr6: 134.5553048.12012.5Chr15: 13.1492480.908710.00e+00------
131 +1459529_atE230016K23RikRIKEN cDNA E230016K23 geneChr11: 83.4369296.97112.8Chr3: 10.3271010.908710.00e+00---0.4150.035
132 +1457950_atLOC193217ESTsChr5: 82.2432426.61211.7Chr3: 10.3271010.908710.00e+00------
133 +1444888_atAU022852expressed sequence AU022852Chr15: 9.7364237.51112.2Chr2: 66.8059560.907710.00e+00------
134 +1432974_at4933421H12RikRIKEN cDNA 4933421H12 geneChr19: 37.5408727.31214.8Chr15: 13.1492480.907710.00e+00------
135 +1421983_s_atHnf4ahepatic nuclear factor 4, alpha; mid 3' UTRChr2: 163.3968637.33915.1Chr15: 13.1492480.907710.00e+000.293-0.0750.715
136 +1449317_atCflarCASP8 and FADD-like apoptosis regulatorChr1: 58.7895837.98812.1ChrX: 112.6373530.907710.00e+000.324-0.0820.692
137 +1432345_at1700048F04RikRIKEN cDNA 1700048F04 geneChr5: 126.4325018.96110.1ChrX: 112.6373530.907710.00e+00---0.1060.605
138 +1419836_atAU040583AU040583 EST; well expressed sequence (putative non-coding)Chr17: 46.2334799.68310.1Chr15: 13.1492480.907710.00e+00------
139 +1437867_atC330014B19RikRIKEN cDNA C330014B19 geneChr13: 3.3953307.57112.1Chr3: 10.3271010.906710.00e+00------
140 +1456578_x_atLasp1LIM and SH3 protein 1; distal 3' UTRChr11: 97.6999858.91310.1Chr3: 10.3271010.906710.00e+000.3360.2130.297
141 +1415851_a_atImpdh2inosine 5'-phosphate dehydrogenase 2Chr9: 108.46767611.56911.4Chr15: 13.1492480.906710.00e+000.282-0.3070.127
142 +1458923_atC230066G23RikESTsChr14: 65.0375067.26411.7Chr14: 118.8616530.906710.00e+00---0.2580.203
143 +1415809_atTpbpatrophoblast specific protein alphaChr13: 61.0399316.59016.8Chr3: 10.3271010.906710.00e+000.335-0.3010.135
144 +1439745_atCacng7calcium channel, voltage-dependent, gamma subunit 7 (type II transmembrane AMPA receptor regulatory protein); mid distal 3' UTRChr6: 91.98449010.34210.3Chr2: 65.7042710.905710.00e+000.1830.1120.586
145 +1446874_at0610009O04RikAU040128 EST; non-codingChr15: 31.7750788.4099.7Chr15: 13.1492480.905710.00e+00------
146 +1445019_atCcr2chemokine (C-C motif) receptor 2Chr1: 175.5375346.9629.2Chr5: 84.2651080.905710.00e+000.327-0.1210.555
147 +1441250_at9330101J02RikESTsChr15: 80.2822657.45813.1ChrX: 112.6373530.905710.00e+00---0.1350.510
148 +1445482_atGle1lGLE1 RNA export mediator-like (yeastChr2: 29.8154148.12711.7ChrX: 112.6373530.905710.00e+000.253-0.0430.834
149 +1421521_at4930430A15RikRIKEN cDNA 4930430A15 geneChr2: 111.0334536.49311.6Chr15: 13.1492480.905710.00e+00---0.1580.440
150 +1454005_atFmo2flavin containing monooxygenase 2; intron 3 or rare short form 3' UTRChr1: 164.8183127.68810.4ChrX: 112.6373530.904710.00e+000.2480.3180.114
151 +1442762_atBC042775RIKEN cDNA 4930595O22 geneChr4: 155.4177417.57513.9Chr15: 13.1492480.904710.00e+000.243----
152 +1419970_atSlc35a5solute carrier family 35, member A5 (probable UDP-sugar transporter protein); mid 3' UTRChr16: 45.1417557.25812.2ChrX: 127.0092660.904710.00e+00--0.3870.051
153 +1446710_atGtf2a2general transcription factor II A, 2Chr9: 69.8726567.03914.6Chr15: 13.1492480.904710.00e+000.177-0.1980.332
154 +1459425_at9430089E08RikESTsChr6: 81.3952108.02711.7Chr2: 178.9427860.904710.00e+00------
155 +1444038_atAU015836expressed sequence AU015836ChrX: 91.2204687.26510.4Chr5: 128.2649740.904710.00e+00---0.4110.037
156 +1440683_atA930004D18RikRIKEN cDNA A930004D18 geneChr2: 17.9569916.74210.7ChrX: 112.6373530.904710.00e+00--0.0110.958
157 +1457394_at2900002K06RikRIKEN cDNA 2900002K06 geneChrX: 7.7236187.15511.6Chr15: 13.1492480.904710.00e+00------
158 +1459598_atHip1huntingtin interacting protein 1; highly expressed sequence in last intron of Hip1 (putative)Chr5: 135.8879289.71711.4Chr15: 13.1492480.903710.00e+000.3150.3310.099
159 +1447680_atCpd1cerebellar postnatal development protein 1--7.05712.7Chr15: 13.1492480.903710.00e+00------
160 +1454726_s_atPtpdc1protein tyrosine phosphatase domain containing 1; mid distal 3' UTRChr13: 48.6735529.90913.8Chr1: 193.7319960.903710.00e+000.1120.1980.331
161 +1447694_x_atNeo1neogenin; distal 3' UTRChr9: 58.7227458.1609.9Chr3: 10.3271010.903710.00e+000.3670.1720.400
162 +1422941_atWnt16wingless-related MMTV integration site 16Chr6: 22.2479847.24711.6ChrX: 112.6373530.903710.00e+000.334-0.3620.070
163 +1447157_atBB751612BB751612Chr14: 70.1452718.72110.8Chr15: 13.1492480.903710.00e+00------
164 +1427292_atIgl-V1immunoglobulin lambda chain, variable 1Chr16: 19.0633487.55710.3Chr19: 53.4592780.903710.00e+000.206-0.0930.651
165 +1425675_s_atCeacam1CEA-related cell adhesion molecule 1; mid proximal 3' UTRChr7: 26.2477747.82610.9Chr2: 179.2574630.903710.00e+000.383-0.1710.403
166 +1418751_atSitsucrase isomaltase, structuralChr4: 43.4952967.50412.1Chr15: 13.1492480.902710.00e+00------
167 +1421532_atLgr8leucine-rich repeat-containing G protein-coupled receptor 8Chr5: 150.8727717.87312.4ChrX: 132.1209310.902710.00e+000.337----
168 +1421078_atTcf23transcription factor 23Chr5: 31.2788518.45610.0ChrX: 112.6373530.902710.00e+000.221-0.0960.641
169 +1447247_atPpp1r9bprotein phosphatase 1, regulatory subunit 9B; far 3' UTRChr11: 94.8690328.68510.3Chr15: 13.1492480.902710.00e+000.2650.0550.790
170 +1440525_atAcvr2bactivin A receptor, type IIB; distal 3' UTRChr9: 119.3472318.17612.1Chr15: 13.1492480.902710.00e+000.3790.2160.290
171 +1444401_atC80913expressed sequence C80913Chr7: 38.7872258.73611.0ChrX: 112.6373530.902710.00e+000.262-0.0670.745
172 +1419481_atSellselectin, lymphocyteChr1: 166.0098168.31913.1Chr3: 10.3271010.902710.00e+000.288-0.1940.343
173 +1458930_at9030022M04ESTsChr9: 99.5222137.86710.0Chr15: 13.1492480.902710.00e+00------
174 +1425996_a_atSmarca3SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 3; last two exons of short form message (exons 20 and 21)Chr3: 20.0064959.92810.2Chr19: 53.4592780.901710.00e+000.290----
175 +1438302_atZfZhangfei HCF-binding transcription protein (zinc finger, DHHC domain containing 9, retinal bipolar cell expression signature); antisense in 3' UTR and last exonChr7: 97.5922037.30213.0ChrX: 112.6373530.901710.00e+000.239----
176 +1433074_atDlg2discs, large 2 (postsynaptic density protein 93 kD, chapsyn 110); intron 10 or 11Chr7: 99.2305896.92413.3Chr15: 13.1492480.901710.00e+000.2240.1950.340
177 +1449667_atBB255841BB255841Chr5: 105.2541557.23710.6Chr3: 10.3271010.901710.00e+00------
178 +1421782_a_atSmr2submaxillary gland androgen regulated protein 2Chr5: 88.5375727.32512.3ChrX: 112.6373530.901710.00e+000.285-0.1270.536
179 +1460732_a_atPplperiplakin; 3' UTRChr16: 5.0868467.96314.1Chr1: 193.7319960.901710.00e+000.2820.0660.748
180 +1425559_a_atSahSA rat hypertension-associated homologChr7: 126.9243737.51113.7Chr2: 66.8059560.901710.00e+000.243----
181 +1459688_at2610510H03RikESTsChr2: 128.1709307.24310.9ChrX: 112.6373530.900710.00e+00---0.0660.747
182 +1446564_atD18Ertd169eDNA segment, Chr 18, ERATO Doi 169, expressedChr18: 64.0699358.2409.9Chr2: 180.8255810.900710.00e+00------
183 +1458361_atDclre1cDNA cross-link repair 1C (Artemis protein, Athabascan-type severe combined immunodeficiency); distal half of 3' UTRChr2: 3.3766558.3319.9Chr3: 10.3271010.900710.00e+000.264-0.1490.468
184 +1425003_atUroc1urocanase domain containing 1; mid distal 3' UTRChr6: 90.31399411.2668.7Chr15: 13.1492480.900710.00e+000.172-0.0370.857
185 +1446034_atB430305I03RikESTsChr14: 40.2890008.11112.9ChrX: 127.0092660.900710.00e+00------
186 +1443081_atGata6GATA binding protein 6; intron (from EST AK053151)Chr18: 11.0570318.2589.0Chr2: 179.2574630.900710.00e+000.4320.3060.128
187 +1455926_atLsm6RIKEN cDNA 2410088K19 geneChr11: 77.9630608.33712.3Chr15: 12.9720880.899710.00e+00--0.0240.909
188 +1433057_atIgsf4dimmunoglobulin superfamily, member 4Chr16: 66.8928127.03915.8Chr3: 10.3271010.899710.00e+000.210----
189 +1447411_atUgdhUDP-glucose dehydrogenaseChr5: 65.8213867.45314.0Chr3: 10.3271010.899710.00e+000.312-0.1550.450
190 +1445614_at1190002B21RikESTs--7.59310.6Chr15: 13.1492480.899710.00e+00------
191 +1446198_atD130064H19RikRIKEN cDNA D130064H19 geneChr13: 112.4822526.87610.4Chr15: 13.1492480.899710.00e+00------
192 +1436097_x_atArhgap9Rho GTPase activating protein 9Chr10: 126.7659378.21412.3Chr15: 13.1492480.899710.00e+000.165-0.1870.360
193 +1453148_atSema3dsema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3D; distal end of last exon and proximal 3' UTR (transQTL on chr 4 in BXD Eye Data)Chr5: 12.5851927.30813.6Chr15: 3.2291280.899710.00e+000.214-0.2230.274
194 +1454575_atUtyubiquitously transcribed tetratricopeptide repeat gene, Y chromosomeChrY: 0.3778137.0858.8Chr15: 13.1492480.899710.00e+000.2730.0200.923
195 +1422400_a_atHemt1hematopoietic cell transcript 1Chr15: 74.6544267.27310.8Chr15: 13.1492480.899710.00e+000.146-0.1410.491
196 +1458279_atFoxo3forkhead box O3; intron 2 (from AK157015)Chr10: 41.9279927.62412.8Chr3: 10.3271010.899710.00e+000.325-0.2530.212
197 +1438638_x_atFam116bfamily with sequence similarity 116, member B (protein LOC414918); distal 3' UTRChr15: 89.01266611.4239.6Chr2: 179.2574630.898710.00e+00------
198 +1455457_atCyp2c54cytochrome P450, family 2, subfamily c, polypeptide 54 (xenobiotic metabolism, phenytoin, tolbutamide, ibuprofen, warfarin, similar to human CYP2C9 and CYP2C19, also see Cyp2c37 and Cyp2c50); putative 3' UTRChr19: 40.1126247.59410.4Chr15: 13.1492480.898710.00e+000.162-0.1100.591
199 +1418753_atGfpt2glutamine fructose-6-phosphate transaminase 2Chr11: 49.6515258.63013.2Chr3: 10.3271010.898710.00e+000.2750.1380.500
200 +1453829_at2310007J06RikRIKEN cDNA 2310007J06 geneChr14: 51.5096758.7179.8ChrX: 112.6373530.898710.00e+00------
201 +1419846_atTaf15TAF15 RNA polymerase II, TATA box binding protein (TBP)-associated factorChr11: 83.3141258.19011.4Chr3: 10.3271010.898710.00e+000.238-0.1390.499
202 +1439876_atVti1avesicle transport through interaction with t-SNAREs homolog 1A (yeast)Chr19: 55.5209808.46310.8Chr15: 13.1492480.898710.00e+000.149-0.0590.773
203 +1444616_x_at8430439C15RikRIKEN cDNA 8430439C15 geneChr13: 23.8152737.98713.2Chr3: 10.3271010.897710.00e+00------
204 +1443942_atGabpb2GA repeat binding protein, beta 2Chr3: 94.9980207.37311.2Chr2: 180.8255810.897710.00e+000.2630.1540.453
205 +1431889_x_atPsg21pregnancy-specific glycoprotein 21Chr7: 19.2322658.1209.4ChrX: 112.6373530.897710.00e+000.228-0.2490.221
206 +1458241_atHmga2high mobility group AT-hook 2Chr10: 119.8786947.49010.5Chr15: 13.1492480.897710.00e+000.419-0.2330.252
207 +1437786_atC80008expressed sequence C80008Chr5: 97.8112986.22316.0ChrX: 112.6373530.897710.00e+000.190----
208 +1449260_atRab3dRAB3D, member RAS oncogene familyChr9: 21.7112637.34215.4Chr15: 13.1492480.897710.00e+000.2780.0510.805
209 +1436068_atZbtb10zinc finger and BTB domain containing 10Chr3: 9.2816047.88013.1Chr15: 13.1492480.897710.00e+000.205-0.2800.165
210 +1430724_atAbhd9RIKEN cDNA 2310063B19 geneChr17: 32.3216558.13513.9ChrX: 112.6373530.896710.00e+000.192-0.1610.433
211 +1433097_at4930473M17RikRIKEN cDNA 4930473M17 gene--6.61210.7Chr15: 13.1492480.896710.00e+00------
212 +1459184_atBG071075BG071075Chr12: 49.9565906.67712.5Chr3: 10.3271010.896710.00e+00------
213 +1443193_atTtll1tubulin tyrosine ligase-like 1Chr15: 83.3224087.71314.0ChrX: 112.6373530.895710.00e+000.1620.0270.897
214 +1427800_atKrtap16-2keratin associated protein 16-2Chr16: 88.8693317.70213.5Chr15: 13.1492480.895710.00e+00------
215 +1431969_at4930402D18RikRIKEN cDNA 4930402D18 geneChr15: 60.7595166.56310.5Chr1: 55.1843220.895710.00e+00------
216 +1451880_atBC006743BC006743 protein; exon and 3' UTRChr14: 75.1524487.29313.1Chr3: 10.3271010.895710.00e+00------
217 +1435979_a_atE330039G21RikRIKEN cDNA E330039G21 geneChr11: 115.7479628.01212.2Chr15: 13.1492480.895710.00e+000.215----
218 +1454574_atFndc3bfibronectin type III domain containing 3BChr3: 27.3140777.32711.2Chr15: 13.1492480.895710.00e+000.2780.3380.092
219 +1437431_atCux1cut-like homeobox 1 (CCAAT displacement protein)Chr5: 136.9589937.32715.0Chr1: 193.7319960.894710.00e+000.436-0.1520.459
220 +1458587_at2310047D07RikRIKEN cDNA 2310047D07 geneChr5: 150.0488508.18812.9Chr15: 13.1492480.894710.00e+00---0.2830.161
221 +1457465_atTnks2tankyrase, TRF1-interacting ankyrin-related ADP-ribose polymerase 2ChrX: 6.2139668.51912.5ChrX: 127.0092660.894710.00e+000.2280.0540.795
222 +1444284_atA430106G13RikRIKEN cDNA A430106G13 geneChr1: 139.8273496.96313.1Chr3: 10.3271010.894710.00e+00--0.0140.946
223 +1427516_a_atBocbiregional cell adhesion molecule-related/down-regulated by oncogenes (Cdon) binding protein; last exonChr16: 44.4857279.5219.2Chr2: 179.2574630.894710.00e+000.499-0.2000.327
224 +1457043_atB3galtlbeta 1,3-galactosyltransferase-like; far 3' UTR (from AK083491)Chr5: 150.5646719.82214.2Chr1: 193.7319960.894710.00e+000.2630.1290.529
225 +1427497_at2610015P09RikRIKEN cDNA 2610015P09 geneChr16: 43.9639397.37616.3Chr2: 179.2574630.893710.00e+00------
226 +1420041_atSnrpbMus musculus transcribed sequence with strong similarity to protein pir:T08738 (H.sapiens) T08738 hypothetical protein DKFZp586E0518.1 - human (fragment)Chr12: 56.0092037.07912.9Chr1: 193.7319960.893710.00e+00---0.2070.309
227 +1425295_atEar11eosinophil-associated, ribonuclease A family, member 11Chr14: 51.8749717.34413.6ChrX: 112.6373530.893710.00e+000.1840.0070.974
228 +1432122_at4933403H06RikRIKEN cDNA 4933403G17 geneChr3: 154.8003628.02317.5ChrX: 112.6373530.893710.00e+00------
229 +1432306_atRapgef5Rap guanine nucleotide exchange factor (GEF) 5Chr12: 118.8416567.29410.7Chr15: 13.1492480.893710.00e+000.2090.2630.193
230 +1431919_atRttnrotatinChr18: 89.2836907.45911.5Chr15: 13.1492480.893710.00e+000.313-0.1390.497
231 +1425438_atSlc26a8clone MGC:36892 IMAGE:4935116, Ntrk2 intron--7.59412.8ChrX: 132.1209310.893710.00e+00---0.0810.696
232 +1446970_atGemin5gem (nuclear organelle) associated protein 5Chr11: 57.9362637.76610.8ChrX: 112.6373530.893710.00e+000.199-0.2400.237
233 +1437523_s_atSgcgsarcoglycan, gamma (35kDa dystrophin-associated glycoprotein, limb-girdle muscular dystrophy, type 2C); mid 3' UTRChr14: 61.8399166.5048.9Chr14: 120.0214650.893710.00e+000.243-0.2740.175
234 +1427787_atSp6trans-acting transcription factor 6Chr11: 96.8843417.18813.9Chr2: 179.2574630.893710.00e+000.358-0.0480.816
235 +1457967_atA030003K21RikRIKEN cDNA A030003K21; 3' end of ESTChr1: 82.9391938.08710.0Chr1: 193.7319960.892710.00e+00---0.1860.363
236 +1418609_atIl1f6interleukin 1 family, member 6Chr2: 24.0715776.90312.7ChrX: 112.6373530.892710.00e+000.239-0.2300.257
237 +1458857_atAV125803ESTsChr15: 36.8777478.28510.9Chr2: 67.6943500.892710.00e+00------
238 +1458681_atTm9sf3transmembrane protein 9 superfamily member 3; intron 1 (AW823484)Chr19: 41.3375809.12412.4Chr14: 118.8616530.892710.00e+000.204-0.3050.130
239 +1443354_atTrim59tripartite motif-containing 59Chr3: 68.8427208.08411.9Chr15: 13.1492480.892710.00e+000.202-0.3650.066
240 +1428925_atSenp1SUMO1/sentrin specific protease 1; putative far 3' UTRChr15: 97.8695527.64211.1Chr1: 193.7319960.892710.00e+000.243-0.0890.664
241 +1417074_atCeacam10CEA-related cell adhesion molecule 10Chr7: 25.5659217.06311.4ChrX: 112.6373530.891710.00e+000.2710.1180.564
242 +1444940_atC76411expressed sequence C76411Chr13: 6.2176117.80910.8Chr15: 13.1492480.891710.00e+00------
243 +1438865_atH13histocompatibility 13Chr2: 152.5338227.74912.8Chr15: 13.1492480.891710.00e+000.2850.1870.361
244 +1430627_atPak2p21 (CDKN1A)-activated kinase 2Chr16: 32.0332257.46113.8Chr15: 13.1492480.891710.00e+000.2880.1200.558
245 +1430540_at5330414O08RikRIKEN cDNA 5330414O08 geneChr7: 107.6885837.64010.4Chr3: 10.3271010.891710.00e+000.183----
246 +1443798_atPik3cdphosphatidylinositol 3-kinase catalytic delta polypeptide; distal 3' UTRChr4: 149.02328011.2499.8Chr2: 179.2574630.891710.00e+000.322-0.1980.333
247 +1432784_at4930556A17RikRIKEN cDNA 4930556A17 geneChr3: 37.7375836.99714.2Chr15: 13.1492480.891710.00e+00---0.2590.201
248 +1455427_atAngpt4angiopoietin 4Chr2: 151.7704997.79611.2Chr15: 13.1492480.891710.00e+000.2530.2670.187
249 +1432687_at4833406M21RikRIKEN cDNA 4833406M21 geneChr13: 110.4403516.45612.3Chr2: 65.7042710.891710.00e+00------
250 +1459943_atA030012G06RikRIKEN cDNA A030012G06 geneChr9: 97.1909116.52311.4Chr15: 13.1492480.891710.00e+00------
251 +1429636_at1700010D01RikRIKEN cDNA 1700010D01 geneChrX: 89.7354346.53615.9Chr15: 13.1492480.891710.00e+00---0.1550.449
252 +1459824_atSmarcc1SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily c, member 1Chr9: 110.1397607.34416.8Chr15: 13.1492480.891710.00e+000.2860.0860.678
253 +1436565_atCeacam10CEA-related cell adhesion molecule 10Chr7: 25.5691066.72612.1Chr15: 13.1492480.891710.00e+000.2710.1180.564
254 +1442459_atAdamts19a disintegrin-like and metalloprotease (reprolysin type) with thrombospondin type 1 motif, 19Chr18: 59.1509146.94314.1ChrX: 112.6373530.890710.00e+000.197-0.0910.660
255 +1450828_atSynpo2synaptopodin 2; exon 1 and intron 1 (transQTL on Chr 4 in BXD eye data)Chr3: 122.8151966.89211.8Chr15: 13.1492480.890710.00e+000.2690.1380.501
256 +1429126_atNudt5nudix (nucleoside diphosphate linked moiety X)-type motif 5Chr2: 5.7913858.92710.0Chr15: 13.1492480.890710.00e+000.1880.1340.514
257 +1444590_at2010305K11RikRIKEN cDNA 2010305K11 geneChr8: 26.8626606.26914.9Chr3: 10.3271010.890710.00e+000.216----
258 +1425229_a_atTcf7l2transcription factor 7-like 2, T-cell specific, HMG-box; 3' UTRChr19: 56.0062579.30911.6ChrX: 127.0092660.890710.00e+000.3130.1070.602
259 +1444229_atNr2f2nuclear receptor subfamily 2, group F, member 2; intron 2 (from EST AK135306)Chr7: 77.5006237.24011.6ChrX: 112.6373530.890710.00e+000.4470.4400.025
260 +1428518_atMlf1ipmyeloid leukemia factor 1 interacting proteinChr8: 47.6636077.48415.9Chr19: 53.4592780.890710.00e+000.245----
261 +1420701_atKlk1kallikrein 1Chr7: 51.2256246.91217.3Chr15: 13.1492480.890710.00e+000.262-0.2760.173
262 +1418705_atCrxcone-rod homeobox containing gene (rod-cone dystrophy 2); distal 3' UTRChr7: 16.4513156.78410.3Chr19: 53.4592780.890710.00e+000.3000.0400.848
263 +1444916_atGm1305gene model 1305; antisense in intronChr1: 181.6662086.73611.0Chr15: 13.1492480.890710.00e+00---0.1010.625
264 +1452564_atOfaoncofetal antigen--6.37014.8ChrX: 112.6373530.890710.00e+00------
265 +1445509_atAtf7activating transcription factor 7Chr15: 102.3785417.54114.9Chr1: 193.7319960.890710.00e+000.2530.0840.682
266 +1432338_at4833419O12RikRIKEN cDNA 4833419O12 geneChr16: 20.8005106.68911.3Chr2: 178.9427860.890710.00e+00------
267 +1418402_atAdam19a disintegrin and metalloproteinase domain 19 (meltrin beta, dendritic cell marker); distal 3' UTRChr11: 45.9602708.93911.3Chr1: 193.7319960.890710.00e+000.3050.0520.800
268 +1456432_atA630035D09RikRIKEN cDNA A630035D09 geneChr15: 80.4043746.80012.8Chr15: 13.1492480.889710.00e+00------
269 +1421130_atZfp111zinc finger protein 111Chr7: 24.9819697.38913.5Chr15: 13.1492480.889710.00e+000.2190.1120.586
270 +1453574_atHba-a1hemoglobin alpha, adult chain 1--7.29714.0ChrX: 112.6373530.889710.00e+000.246-0.0210.918
271 +1431546_at4930509K18RikRIKEN cDNA 4930509K18 geneChr4: 40.2641706.90013.6Chr15: 13.1492480.889710.00e+00------
272 +1452311_atDmgdhdimethylglycine dehydrogenase precursorChr13: 94.5222107.31413.0ChrX: 112.6373530.889710.00e+000.248-0.0850.679
273 +1447672_x_atAW539964hypothetical protein E130013P03Chr8: 108.4555419.2437.9ChrX: 112.6373530.889710.00e+00------
274 +1444809_atRnf181ESTs, Weakly similar to similar to RIKEN cDNA 1810006A16 gene [Homo sapiens] [H.sapiens]Chr8: 112.4839887.03310.2Chr1: 193.7319960.889710.00e+000.1510.4180.034
275 +1430768_atHoxa13homeo box A13Chr6: 52.2065226.28817.7Chr3: 10.3271010.889710.00e+000.4820.4020.042
276 +1450567_a_atCol2a1procollagen, type II, alpha 1Chr15: 97.8064718.51111.7Chr15: 13.1492480.888710.00e+000.393-0.1560.446
277 +1430461_at1700120E14RikRIKEN cDNA 1700120E14 geneChr18: 74.6555956.65215.8Chr15: 13.1492480.888710.00e+00------
278 +1443474_atD7Ertd495eDNA segment, Chr 7, ERATO Doi 495, expressedChr7: 132.6406747.2318.7ChrX: 112.6373530.888710.00e+00------
279 +1449242_s_atHrghistidine-rich glycoprotein; last exonChr16: 22.9612837.73411.0ChrX: 112.6373530.888710.00e+000.277-0.0960.640
280 +1417413_atCuzd1CUB and zona pellucida-like domains 1Chr7: 138.4520977.48811.3Chr3: 149.0891950.888710.00e+000.3060.1030.615
281 +1452493_s_atHoxb8homeo box B8Chr11: 96.1461107.4599.7Chr15: 13.1492480.888710.00e+000.4200.2490.221
282 +1432048_at6330565B04RikRIKEN cDNA 6330565B04 geneChr1: 28.6269317.67316.6Chr3: 10.3271010.888710.00e+00---0.0140.947
283 +1433091_at6430514K02RikRIKEN cDNA 6430514K02 geneChr8: 50.8242637.25614.6Chr1: 193.7319960.888710.00e+00------
284 +1457554_atApobapolipoprotein BChr12: 8.0116887.43011.1Chr2: 178.9427860.888710.00e+000.225-0.0110.958
285 +1433366_atMapk8mitogen activated protein kinase 8Chr14: 34.2344817.77612.9ChrX: 112.6373530.888710.00e+000.2950.0620.762
286 +1420231_atZfp59zinc finger protein 59--7.0039.6Chr3: 10.3271010.888710.00e+00---0.2500.218
287 +1454279_atOaz3ornithine decarboxylase antizyme 3Chr3: 94.2393267.34010.9ChrX: 112.6373530.887710.00e+000.234-0.0360.860
288 +1417828_atAqp8aquaporin 8Chr7: 130.6110067.65513.3Chr15: 13.1492480.887710.00e+000.272-0.1780.383
289 +1428020_at2310066N05RikRIKEN cDNA 2310066I18 geneChr3: 146.1696207.21216.2Chr3: 12.5656160.887710.00e+000.150----
290 +1421485_atTtbk2tau tubulin kinase 2; three exonsChr2: 120.5816197.96611.8Chr15: 13.1492480.887710.00e+000.166-0.1360.509
291 +1428378_atZc3hav1zinc finger CCCH type, antiviral 1Chr6: 38.2657537.89111.9Chr15: 13.1492480.887710.00e+000.236-0.0510.804
292 +1432204_at4930570B17RikRIKEN cDNA 4930570B17 geneChr15: 30.5280007.05412.4Chr9: 79.9914910.887710.00e+00------
293 +1421795_s_atKlrc3killer cell lectin-like receptor subfamily C, member 3Chr6: 129.5932127.33713.4Chr15: 13.1492480.887710.00e+000.140-0.1690.409
294 +1421513_atTdrd1tudor domain containing 1Chr19: 56.9388097.5678.9ChrX: 112.6373530.887710.00e+000.216-0.1440.483
295 +1428773_s_atBcorBcl6 interacting corepressorChrX: 11.6144848.57612.6ChrX: 127.0092660.886710.00e+000.310-0.0320.877
296 +1457126_atMyl4myosin, light polypeptide 4; alternate 3' UTRChr11: 104.4472227.94511.4Chr3: 10.3271010.886710.00e+000.3100.0250.905
297 +1440865_atIfitm6interferon induced transmembrane protein 6Chr7: 148.2016457.04615.9Chr15: 13.1492480.886710.00e+000.242-0.1630.426
298 +1441449_atKdm5clysine (K)-specific demethylase 5C (X-linked mental retardation, jumonji, AT rich interactive domain 1C); intron 9ChrX: 148.6809467.83111.0Chr15: 13.1492480.886710.00e+000.291----
299 +1451898_a_atSema6csema domain, transmembrane domain (TM), and cytoplasmic domain, (semaphorin) 6C; 3' UTRChr3: 94.97729110.20712.3Chr15: 13.1492480.886710.00e+000.242-0.0060.978
300 +1454215_atXrcc4X-ray repair complementing defective repair in Chinese hamster cells 4--6.61310.7Chr15: 13.1492480.886710.00e+000.258-0.1490.469
301 +1421775_atFcer1aFc receptor, IgE, high affinity I, alpha polypeptideChr1: 175.1515097.09916.3ChrX: 112.6373530.886710.00e+000.287-0.1450.481
302 +1446232_atLtbp1latent transforming growth factor beta binding protein 1Chr17: 75.7484657.67212.5ChrX: 112.6373530.886710.00e+000.3140.1610.432
303 +1421206_atLifleukemia inhibitory factorChr11: 4.1739427.05613.1Chr15: 13.1492480.886710.00e+000.374-0.1500.465
304 +1428480_atCdca8cell division cycle associated 8Chr4: 124.5961739.19412.4ChrX: 112.6373530.886710.00e+000.219-0.3840.053
305 +1423541_at4930511I11RikRIKEN cDNA 4930511I11 geneChr17: 28.6745236.8169.6Chr8: 74.9353530.886710.00e+00---0.1360.507
306 +1429811_atC12orf11sarcoma antigen NY-SAR-95, human chromosome 12 open reading frame 11; last 2 exons and last 2 intronsChr6: 146.4985027.86610.8Chr1: 193.7319960.886710.00e+000.237----
307 +1419923_atTrpm3transient receptor potential cation channel, subfamily M, member 3Chr19: 22.7997387.93214.1Chr15: 13.1492480.886710.00e+000.2090.1960.338
308 +1447778_x_atBrcc3BRCA1/BRCA2-containing complex, subunit 3; mid 3' UTRChrX: 72.6979989.61210.4ChrX: 112.6373530.886710.00e+000.234-0.0800.699
309 +1441357_atKirrel3kin of IRRE like 3 (Drosophila)Chr9: 34.3176147.76510.5Chr3: 12.5656160.886710.00e+000.250-0.0760.710
310 +1431740_atSlc7a13solute carrier family 7, (cationic amino acid transporter, y+ system) member 13 (kidney male-specific transporter); intron 1 (from AK002431, male kidney EST)Chr4: 19.7469346.9068.5ChrX: 112.6373530.885710.00e+000.156-0.1360.509
311 +1427825_atSlco1b2solute carrier organic anion transporter family, member 1b2; exon and intronChr6: 141.5966917.52413.7Chr15: 13.1492480.885710.00e+000.2420.0480.818
312 +1456254_atInpp4binositol polyphosphate-4-phosphatase, type 2; intron 5 or antisense in AK140223 ESTChr8: 84.3980988.09511.2ChrX: 112.6373530.885710.00e+000.232-0.1980.332
313 +1445470_atCol6a4procollagen type 6, alpha 4 (putative); 5' UTR and first exonChr9: 105.9735358.65412.3Chr15: 13.1492480.885710.00e+000.188----
314 +1439992_atMov10l1Moloney leukemia virus 10-like 1Chr9: 114.4673747.35519.0Chr15: 13.1492480.885710.00e+000.262-0.2300.257
315 +1430895_at2010109A12RikRIKEN cDNA 2010109A12 geneChr5: 93.6357146.57211.3ChrX: 112.6373530.885710.00e+00---0.3450.084
316 +1446422_atMarch5membrane-associated ring finger (C3HC4) 5, mitochondrial E3 ubiquitin-protein ligaseChr18: 56.9903586.95813.7Chr15: 13.1492480.885710.00e+000.174-0.0220.913
317 +1431476_at4933407I05RikRIKEN cDNA 4933407I05 geneChr9: 51.7235437.1829.6Chr15: 13.1492480.885710.00e+00------
318 +1442086_atMta3metastasis associated 3; intron 4Chr17: 84.1510688.25011.7Chr3: 10.3271010.885710.00e+000.2840.0350.864
319 +1456221_atMGC40768ESTs, Moderately similar to protein phosphatase 4, regulatory subunit 1 [Rattus norvegicus] [R.norvegicus]Chr8: 101.6716317.13911.3Chr2: 180.8255810.885710.00e+00------
320 +1459079_at5430405C01RikESTsChr5: 142.6934736.46611.6ChrX: 112.6373530.885710.00e+00------
321 +1444592_atOsgepO-sialoglycoprotein endopeptidaseChr14: 51.5342618.14210.3Chr15: 3.2291280.885710.00e+000.2620.0450.826
322 +1451750_atIrak4interleukin-1 receptor-associated kinase 4 (NF-kappaB activation); distal 3' UTRChr15: 94.3981787.40311.2Chr15: 13.1492480.885710.00e+000.2540.0150.942
323 +1442554_s_atKalrnkalirin, RhoGEF kinase (huntingtin-associated protein interacting protein duo); two central exonsChr16: 34.00995111.95711.4Chr15: 13.1492480.885710.00e+000.2840.0330.874
324 +1435958_atPigbphosphatidylinositol glycan, class BChr9: 72.8624778.14811.2Chr9: 79.9914910.884710.00e+000.2040.3000.137
325 +1425916_atCapn8calpain 8Chr1: 184.5358628.37713.3ChrX: 112.6373530.884710.00e+000.232-0.1370.504
326 +1445752_at9430089E08RikESTs, Highly similar to L1 repeat, Tf subfamily, member 18 [] [M.musculus]Chr9: 11.8952406.98415.1ChrX: 112.6373530.884710.00e+00------
327 +1446669_atEbf1early B-cell factor 1Chr11: 44.4775527.02710.4Chr9: 79.9914910.884710.00e+000.365-0.0670.746
328 +1459410_atAV304616AV304616 EST; well expressed (hippocampus) non-coding sequenceChr9: 23.6228269.62111.0Chr15: 13.1492480.884710.00e+00------
329 +1446116_atB230378P21RikRIKEN cDNA B230378P21 geneChr6: 32.4649956.73210.6Chr15: 13.1492480.884710.00e+00------
330 +1422313_a_atIgfbp5insulin-like growth factor binding protein 5 (hippocampal DG expression signature); mid 3' UTRChr1: 72.9072989.2869.5Chr2: 179.2574630.884710.00e+000.381-0.0050.980
331 +1446037_atC230081A13RikRIKEN cDNA C230081A13 geneChr9: 56.1304568.88811.7Chr2: 67.6943500.884710.00e+000.1650.1210.555
332 +1446408_atHoxa11homeo box A11Chr6: 52.1891846.43112.3ChrX: 132.1209310.883710.00e+000.4310.0650.751
333 +1446923_atC630010D02RikESTsChr16: 35.1097128.90712.7ChrX: 112.6373530.883710.00e+00------
334 +1459178_atD8Ertd503eDNA segment, Chr 8, ERATO Doi 503, expressedChr8: 120.4255737.06411.7Chr3: 10.3271010.883710.00e+00------
335 +1441786_at1700020L24RikESTsChr18: 5.5657808.0509.5ChrX: 112.6373530.883710.00e+00---0.2010.326
336 +1420470_atSult1c1sulfotransferase family, cytosolic, 1C, member 1Chr17: 54.1017366.97119.3Chr1: 172.9808200.883710.00e+000.255-0.2890.152
337 +1447531_x_atCugbp2CUG triplet repeat, RNA binding protein 2; putative intron 1Chr2: 6.7571679.23613.5Chr1: 193.7319960.883710.00e+000.2980.0180.929
338 +1443416_atC79741expressed sequence C79741; exon 3Chr13: 51.0425087.8119.7Chr3: 10.3271010.883710.00e+00------
339 +1448841_atPfplpore forming protein-likeChr19: 12.5062627.38214.3Chr3: 10.3271010.883710.00e+00---0.2150.292
340 +1423348_atFzd8frizzled homolog 8 (Drosophila)Chr18: 9.2150728.70713.8Chr15: 13.1492480.882710.00e+000.3230.3670.065
341 +1431868_atAkap4A kinase (PRKA) anchor protein 4Chr15: 39.9990767.64112.1Chr2: 66.8059560.882710.00e+000.243-0.1140.580
342 +1421788_x_atKlk26kallikrein 13; last three exonsChr7: 51.2717487.76212.0Chr15: 13.1492480.882710.00e+000.255----
343 +1434830_atMadMax dimerization proteinChr6: 86.5970839.16912.9Chr15: 13.1492480.882710.00e+000.303-0.2500.218
344 +1458094_at6130401L20Rik15 days embryo male testis cDNA, RIKEN full-length enriched library, clone:8030405F19 product:Y box protein 1, pseudogene 2, full insert sequence.Chr18: 84.6198376.99713.0Chr3: 10.3271010.882710.00e+00--0.0850.681
345 +1420183_atLorloricrin (keratinocyte); 3' UTRChr3: 91.88422411.83310.9ChrX: 112.6373530.882710.00e+000.386-0.1420.490
346 +1432758_at2900011L18RikRIKEN cDNA 2900011L18 geneChr13: 108.6066798.75612.4Chr2: 65.7042710.882710.00e+00--0.0700.736
347 +1453653_at6330576A10RikRIKEN cDNA 6330576A10 geneChr14: 100.8781546.75113.0Chr15: 13.1492480.882710.00e+00------
348 +1433124_at4930534I15RikRIKEN cDNA 4930534I15 geneChr2: 22.8823578.02112.2Chr2: 65.7042710.882710.00e+00------
349 +1457653_atA630042L21RikRIKEN cDNA A630042L21 geneChr18: 61.3345238.4409.6ChrX: 112.6373530.882710.00e+000.1760.1570.445
350 +1437836_x_at0610011L14RikRIKEN cDNA 0610011L14 geneChr2: 156.3937638.40410.9Chr1: 193.7319960.882710.00e+000.170-0.2770.171
351 +1430157_at1700095J03RikRIKEN cDNA 1700095J03 geneChr7: 116.5833577.28311.4Chr15: 13.1492480.881710.00e+00---0.1600.434
352 +1458027_atMrpl17mitochondrial ribosomal protein L17; distal 3' UTRChr7: 112.9518098.06113.5ChrX: 112.6373530.881710.00e+000.1710.1360.509
353 +1449621_s_atThsd1thrombospondin, type I, domain 1Chr8: 23.3699527.97517.5ChrX: 112.6373530.881710.00e+000.3010.1430.485
354 +1427584_atAmotangiomotinChrX: 141.8835427.80913.8Chr1: 193.7319960.881710.00e+000.346-0.2910.149
355 +1445985_atTraf3ip2Traf3 interacting protein 2Chr10: 39.3369726.79910.1Chr14: 118.8616530.881710.00e+000.2250.1170.569
356 +1443871_atA430018A01RikESTs, Weakly similar to S57243 collagen alpha 1(I) chain precursor - mouse [M.musculus]Chr3: 100.4910567.12513.7Chr15: 13.1492480.881710.00e+00------
357 +1425279_atPdik1lPDLIM1 interacting kinase 1 likeChr4: 133.8326188.16713.1Chr15: 12.9720880.881710.00e+000.196-0.2860.157
358 +1459555_atRy1ESTsChr14: 64.4708827.69714.1Chr2: 67.6943500.880710.00e+00------
359 +1428576_atHif1anhypoxia-inducible factor 1, alpha subunit inhibitor; distal 3' UTRChr19: 44.6501839.55012.6ChrX: 127.0092660.880710.00e+00---0.2550.208
360 +1443709_atBE979452ESTsChr11: 86.7696647.21014.7Chr15: 13.1492480.880710.00e+00------
361 +1433344_at4930448K20RikRIKEN cDNA 4930448K20; exon 1Chr4: 9.8439968.14510.9Chr3: 10.3271010.880710.00e+00--0.1490.468
362 +1449586_atPkp1plakophilin 1 (ectodermal dysplasia/skin fragility syndrome, corneal epithelium); distal 3' UTRChr1: 137.7680148.04112.3Chr15: 13.1492480.880710.00e+000.259-0.2880.154
363 +1446133_atD2Ertd295eDNA segment, Chr 2, ERATO Doi 295, expressedChr2: 52.7778067.1828.9Chr14: 34.6092560.880710.00e+00------
364 +1445645_atH3104E01non-coding SINE sequence associated with H3104E01; putative 5' end of H3104E01 (antisense in intron of Adam22)Chr5: 8.2773848.02412.1ChrX: 112.6373530.879710.00e+00------
365 +1435244_atVav2Vav2 oncogene; distal 3' UTRChr2: 27.1179818.3169.3Chr1: 193.7319960.879710.00e+000.2840.2390.239
366 +1450538_s_atCma2chymase 2, mast cellChr14: 56.5916028.83712.1Chr14: 118.8616530.879710.00e+00---0.1110.589
367 +1431846_at4930546C10RikRIKEN cDNA 4930546C10 geneChr18: 69.0500547.15511.4Chr15: 13.1492480.879710.00e+00---0.2540.210
368 +1440711_atC630001G18RikRIKEN cDNA C630001G18 geneChr3: 84.1103497.22211.7ChrX: 112.6373530.879710.00e+00------
369 +1432210_at4933401H06RikRIKEN cDNA 4933401H06 geneChr3: 135.4969418.16914.5Chr19: 53.4592780.879710.00e+00------
370 +1417620_atRac2RAS-related C3 botulinum substrate 2; proximal 3' UTRChr15: 78.3917248.76211.8Chr2: 179.2574630.879710.00e+000.291-0.1870.359
371 +1433416_atRfx2regulatory factor X, 2 (influences HLA class II expression)Chr17: 56.9691536.52014.1Chr15: 13.1492480.879710.00e+000.265-0.0720.728
372 +1442532_atMov10l1Moloney leukemia virus 10-like 1Chr19: 28.0868616.72313.2Chr3: 10.3271010.879710.00e+000.262-0.2300.257
373 +1456964_atCpne1copine IChr2: 155.9193047.86510.2ChrX: 112.6373530.878710.00e+000.276-0.1470.474
374 +1426639_a_atTcf7l2transcription factor 7-like 2, T-cell specific, HMG-boxChr19: 55.8922657.75812.1Chr15: 13.1492480.878710.00e+000.3130.1070.602
375 +1444636_atNt5c1b5'-nucleotidase, cytosolic IBChr18: 83.0878326.92712.7Chr15: 13.1492480.878710.00e+000.165-0.1010.623
376 +1420219_atDnajc21DnaJ (Hsp40) homolog subfamily C member 21; last exon and 3' UTRChr15: 10.3910348.77213.3Chr15: 13.1492480.878710.00e+000.136-0.3000.137
377 +1418745_atOmdosteomodulinChr13: 49.6857437.64410.2Chr15: 13.1492480.878710.00e+000.303-0.0400.848
378 +1422904_atFmo2flavin containing monooxygenase 2; last exonChr1: 164.8069868.18216.1Chr15: 13.1492480.878710.00e+000.2480.3180.114
379 +1442588_at9530060I07adult male urinary bladder cDNA, RIKEN full-length enriched library, clone:9530060I07 product:unclassifiable, full insert sequence.Chr1: 98.7772617.48510.3Chr15: 13.1492480.878710.00e+00------
380 +1432550_at4930572D21RikRIKEN cDNA 4930572D21 geneChr7: 4.7354106.71013.7Chr19: 53.4592780.878710.00e+00------
381 +1439199_atPpp2caprotein phosphatase 2a, catalytic subunit, alpha isoformChr11: 51.9108268.55511.7Chr15: 13.1492480.878710.00e+000.292-0.0160.939
382 +1454165_at1700025K04RikRIKEN cDNA 1700025K04 geneChr10: 125.8115286.81511.1Chr15: 13.1492480.877710.00e+00------
383 +1430189_atNol4nucleolar protein 4Chr18: 23.1993248.00913.3Chr15: 13.1492480.877710.00e+000.1760.0510.803
384 +1456780_atBtrcbeta-transducin repeat containing proteinChr19: 45.5984607.22812.4Chr3: 10.3271010.877710.00e+000.3370.1070.603
385 +1453763_atTxndc11thioredoxin domain containing 11Chr16: 11.0875756.80815.1ChrX: 112.6373530.877710.00e+000.1270.0110.959
386 +1445988_atIl17dinterleukin 17D; intron 1 (from BG064506)Chr14: 58.1454448.25315.3Chr15: 13.1492480.877710.00e+000.1790.1940.343
387 +1427397_at2810046L04RikRIKEN cDNA 2810046L04 geneChr3: 53.2832457.84010.4Chr1: 193.7319960.877710.00e+000.1000.0600.771
388 +1424906_atE030024M05RikRIKEN cDNA E030024M05 geneChr12: 16.9957787.31610.0Chr3: 10.3271010.877710.00e+00------
389 +1447213_atSp100nuclear antigen Sp100Chr1: 87.5849378.02611.4Chr2: 180.8255810.877710.00e+000.255-0.1310.523
390 +1449529_s_atPrlpeprolactin-like protein EChr13: 27.7275596.50015.5Chr15: 13.1492480.876710.00e+000.235----
391 +1432400_atEpha1Eph receptor A1Chr6: 42.3170866.60611.6Chr15: 13.1492480.876710.00e+000.3480.0380.854
392 +1446843_atB930007P11RikRIKEN cDNA B930007P11 geneChr12: 48.5094047.09617.3Chr15: 13.1492480.876710.00e+00------
393 +1436806_atTrim62tripartite motif-containing 62; distal 3' UTRChr4: 128.5882949.80211.5Chr2: 179.2574630.876710.00e+000.2290.2090.305
394 +1432244_at4930507D10RikRIKEN cDNA 4930507D10 geneChr11: 80.6640286.91411.4Chr3: 10.3271010.876710.00e+00------
395 +1453234_at1300002K09RikRIKEN cDNA 1300002K09 geneChr4: 45.8992906.39012.1ChrX: 112.6373530.876710.00e+00---0.0560.785
396 +1431695_atRph3alrabphilin 3A-like (without C2 domains)Chr11: 75.7133318.24011.2Chr15: 13.1492480.875710.00e+000.214-0.2000.328
397 +1436360_atHkr2GLI-Kruppel family member HKR2; putative far 3' UTRChr7: 13.4939338.90912.3Chr15: 3.2291280.875710.00e+000.306----
398 +1460423_x_atIgk-V5immunoglobulin kappa chain variable 8 (V8)Chr6: 68.0716286.84712.8Chr15: 13.1492480.875710.00e+000.155----
399 +1426196_atLOC56304recombinant antineuraminidase single chain Ig VH and VL domains--6.77616.5Chr15: 13.1492480.875710.00e+00---0.0800.696
400 +1447742_atLaptm5lysosomal-associated protein transmembrane 5; distal 3' UTRChr4: 130.4918137.47011.8Chr19: 53.4592780.875710.00e+000.3190.0020.992
401 +1450596_atOlfr66olfactory receptor 66Chr7: 111.0298277.48114.2Chr2: 179.2574630.875710.00e+000.150-0.1190.563
402 +1440663_atXpo4exportin 4Chr14: 58.2587157.62813.0Chr15: 13.1492480.875710.00e+000.162-0.0970.637
403 +1435670_atTcfap2btranscription factor AP-2 beta (activating enhancer binding protein 2 beta, Char syndrome); distal 3' UTRChr1: 19.2280797.77211.6ChrX: 127.0092660.875710.00e+000.3730.1270.536
404 +1445681_atCdca7cell division cycle associated 7Chr2: 72.3171967.14812.8Chr3: 10.3271010.874710.00e+000.273-0.2550.208
405 +1444854_at1700023E05RikRIKEN cDNA 1700023E05 geneChr10: 81.6421028.7899.7Chr14: 118.8616530.874710.00e+00--0.2290.260
406 +1437512_x_atEbna1bp2EBNA1 binding protein 2Chr4: 118.2985127.67910.3Chr15: 13.1492480.874710.00e+000.271-0.2090.305
407 +1440931_atAW060659expressed sequence AW060659Chr2: 151.9209638.05012.6Chr15: 13.1492480.874710.00e+00------
408 +1444751_atA230106N23hypothetical protein A230106N23Chr15: 100.4293247.36113.5Chr2: 179.2574630.874710.00e+000.213----
409 +1447095_atC86942ESTsChr9: 103.4491907.49812.4ChrX: 112.6373530.874710.00e+00------
410 +1422957_atCcr3chemokine (C-C motif) receptor 3Chr9: 123.9443806.63012.1Chr15: 13.1492480.874710.00e+000.266-0.2380.242
411 +1459825_x_atIgf2bp1insulin-like growth factor 2 mRNA binding; far 3' UTRChr11: 95.8186309.1848.9Chr15: 13.1492480.874710.00e+00--0.0370.859
412 +1436149_atCox5bcytochrome c oxidase, subunit Vb; last intronChr1: 36.7499449.18911.5Chr2: 67.6943500.874710.00e+000.289-0.2520.215
413 +1458127_at6030446J10RikESTsChr9: 35.2485038.07214.4Chr15: 13.1492480.873710.00e+00------
414 +1445063_atRims1regulating synaptic membrane exocytosis 1; antisense in intronChr1: 22.2973727.67312.1ChrX: 112.6373530.873710.00e+000.2510.3620.069
415 +1429669_atGnao1guanine nucleotide binding protein, alpha o; intron 2Chr8: 96.3356678.24211.2ChrX: 112.6373530.873710.00e+000.2910.3160.116
416 +1457101_at2610017K16Rik0 day neonate thymus cDNA, RIKEN full-length enriched library, clone:A430035F24 product:unclassifiable, full insert sequence.Chr10: 19.4361007.19910.2Chr19: 53.4592780.873710.00e+00------
417 +1437373_atIsg20l1interferon stimulated exonuclease gene 20-like 1; far 3' UTRChr7: 86.0555369.20510.9Chr2: 179.2574630.873710.00e+000.162-0.1240.545
418 +1432651_at2510019K15RikRIKEN cDNA 2510019K15 geneChr2: 154.4264287.20713.7Chr19: 53.4592780.873710.00e+00------
419 +1431277_atPla2g6phospholipase A2, group VI; 5' UTRChr15: 79.1581348.9359.9Chr2: 178.9427860.873710.00e+000.305-0.1170.569
420 +1458995_atAA408296ESTsChr1: 194.9554806.82614.9ChrX: 112.6373530.873710.00e+000.243-0.0840.682
421 +1447324_atA230106N23ESTs--7.48311.4Chr3: 10.3271010.873710.00e+00------
422 +1425377_atWnt1wingless-related MMTV integration site 1; mid and distal 3' UTRChr15: 98.6236697.56115.9Chr15: 13.1492480.873710.00e+000.498-0.2970.140
423 +1449609_atAA189214Mus musculus transcribed sequencesChr4: 125.6431036.97312.0Chr15: 13.1492480.873710.00e+00------
424 +1437516_atLOC168850hypothetical protein LOC168850; 3' UTR (or last intron)Chr6: 28.1922617.86111.5ChrX: 127.0092660.873710.00e+00------
425 +1420144_x_atD930043C02Rikmembrane-associated nucleic acid binding proteinChr2: 37.2691017.37313.9Chr15: 13.1492480.873710.00e+000.188----
426 +1459225_atGnl3lguanine nucleotide binding protein-like 3 (nucleolar)-likeChrX: 147.4450327.70313.3Chr15: 13.1492480.872710.00e+000.2180.1510.461
427 +1437846_x_atBace2beta-site APP-cleaving enzyme 2; proximal 3' UTR (short probe set target sequence)Chr16: 97.6585837.12411.8Chr3: 10.3271010.872710.00e+000.2360.3290.100
428 +1433836_a_atC10orf10fasting-induced protein (decidual protein induced by progesterone); distal 3' UTRChr6: 116.60258911.81210.6Chr19: 53.4592780.872710.00e+000.252----
429 +1438453_atRad51cRad51 homolog c; proximal 3' UTRChr11: 87.1921217.80811.5Chr15: 13.1492480.872710.00e+000.197-0.2810.164
430 +1436171_atArhgap30Rho GTPase activating protein 30; mid to distal 3' UTRChr1: 173.3397748.02714.5Chr15: 13.1492480.872710.00e+00---0.1620.429
431 +1453982_at4933417N07RikRIKEN cDNA 4933417N07 geneChr1: 134.8583248.05211.8Chr15: 13.1492480.872710.00e+00------
432 +1460537_atEbf2early B-cell factor 2Chr14: 67.9603297.00210.8Chr15: 13.1492480.872710.00e+000.3930.0970.637
433 +1425579_atGfra2glial cell line derived neurotrophic factor family receptor alpha 2Chr14: 71.2900148.15710.3ChrX: 112.6373530.871710.00e+000.299-0.2160.289
434 +1457019_s_atNt5c1b5'-nucleotidase, cytosolic IBChr12: 10.3997588.19312.3Chr3: 10.3271010.871710.00e+000.165-0.1010.623
435 +1425150_atC730036D15RikRIKEN cDNA C730036D15 geneChr4: 49.3929847.08511.6Chr15: 13.1492480.871710.00e+000.069-0.1430.484
436 +1419703_atCol5a3procollagen, type V, alpha 3Chr9: 20.5745157.84311.2Chr5: 128.2649740.871710.00e+000.2780.1700.408
437 +1449451_atSerpinb11serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 11Chr1: 109.2764947.29416.0Chr15: 13.1492480.871710.00e+000.180-0.3700.063
438 +1431384_atLOC374768LOC374768 protein; last exonChr11: 69.6346887.55511.2Chr15: 13.1492480.871710.00e+00------
439 +1432089_at4930550C17RikRIKEN cDNA 4930550C17 geneChr13: 56.3239917.65312.9Chr15: 13.1492480.871710.00e+00------
440 +1444781_atXab1XPA binding protein 1Chr5: 31.8141617.79310.8Chr19: 53.4592780.871710.00e+000.186-0.0380.855
441 +1421760_atPtcrapre T-cell antigen receptor alphaChr17: 46.8929157.00011.2Chr15: 3.2291280.871710.00e+000.316-0.2250.270
442 +1432570_at6030458E02RikRIKEN cDNA 6030458E02 geneChr19: 60.9906686.82615.0Chr3: 10.3271010.871710.00e+00------
443 +1449077_atCenppcentromere protein P; intron 4Chr13: 49.6340108.14612.7Chr3: 149.0891950.871710.00e+00--0.2960.142
444 +1421759_a_atFoxk1forkhead box K1Chr5: 142.9246779.22313.1Chr15: 13.1492480.871710.00e+000.3750.2540.211
445 +1444370_atC77058expressed sequence C77058Chr11: 77.0108697.96011.2ChrX: 112.6373530.871710.00e+00------
446 +1452553_atA330035P11RikRIKEN cDNA A330035P11 geneChr5: 34.2910367.14616.1Chr3: 149.0891950.871710.00e+00---0.0290.888
447 +1459148_atPax3paired box gene 3Chr1: 78.1046256.36014.1Chr15: 13.1492480.871710.00e+000.478-0.3920.048
448 +1447076_atNell1NEL-like 1, protein kinase C-binding protein NELL1; putative far 3' UTR (from EST AK079065)Chr7: 58.1202067.00213.8Chr15: 13.1492480.871710.00e+000.404-0.0850.679
449 +1430866_at4921537D05RikRIKEN cDNA 4921537D05 geneChr10: 94.2049327.19210.6Chr6: 89.2984460.871710.00e+00------
450 +1453528_atLta4hleukotriene A4 hydrolase (inflammatory response to infection, heart attack risk-associated); intron 1Chr10: 92.9165348.42510.1ChrX: 112.6373530.871710.00e+000.255-0.2990.137
451 +1457752_atAU014876expressed sequence AU014876Chr16: 16.5780376.94911.6Chr3: 10.3271010.870710.00e+00------
452 +1456237_x_atHeatr2HEAT repeat containing 2; mid 3' UTRChr5: 139.6620486.94112.4Chr15: 13.1492480.870710.00e+00------
453 +1430584_s_atCar3carbonic anhydrase 3Chr3: 14.8653777.38010.8Chr2: 179.2574630.870710.00e+000.3270.1470.473
454 +1458807_atEpb4.1erythrocyte protein band 4.1Chr4: 131.4901977.6959.8Chr3: 10.3271010.870710.00e+000.332-0.1640.424
455 +1432805_at8030431A06RikRIKEN cDNA 8030431A06 geneChr14: 33.6778808.84311.6Chr15: 13.1492480.870710.00e+00------
456 +1458553_atPlatplasminogen activator, tissueChr8: 23.8865087.30312.8Chr15: 13.1492480.869710.00e+000.3100.3080.125
457 +1446698_atLppLIM domain containing preferred translocation partner in lipomaChr16: 24.9725737.47314.3Chr3: 10.3271010.869710.00e+000.3130.2620.195
458 +1453951_a_at4930463G05RikRIKEN cDNA 4930463G05 geneChr19: 47.9048737.87811.8Chr2: 178.9427860.869710.00e+00------
459 +1443825_x_atSpaca3sperm acrosome associated 3; mid 3' UTRChr11: 80.6812477.16514.3Chr15: 13.1492480.869710.00e+000.241-0.1580.442
460 +1451719_atCrsp6cofactor required for Sp1 transcriptional activation, subunit 6Chr9: 15.0649067.75814.8Chr15: 13.1492480.869710.00e+000.211----
461 +1440939_atSept6septin 6ChrX: 34.4514057.62112.9Chr15: 13.1492480.869710.00e+000.2930.1080.600
462 +1424700_atTmem38btransmembrane protein 38B (trimeric intracellular cation channel type B); last exonChr4: 53.87287011.69511.9Chr2: 67.6943500.869710.00e+000.183-0.0230.912
463 +1424262_atIba2ionized calcium binding adapter 2; mid distal 3' UTRChr2: 31.82836711.3448.6Chr3: 10.3271010.869710.00e+000.174----
464 +1425233_at2210407C18RikRIKEN cDNA 2210407C18 geneChr11: 58.4217917.93111.2Chr2: 67.6943500.869710.00e+00---0.0430.837
465 +1439921_atAldh1a2aldehyde dehydrogenase 1 family, member A2; putative short transcript expressed from antisense of promoterChr9: 71.0597657.20111.5ChrX: 112.6373530.869710.00e+00--0.1780.384
466 +1452541_atEpb4.1l2erythrocyte protein band 4.1-like 2Chr10: 25.2195818.5489.4Chr15: 13.1492480.868710.00e+000.2790.0630.760
467 +1422935_x_atCbx1chromobox homolog 1 (Drosophila HP1 beta); last exon and 3' UTRChr11: 96.6679747.8679.3ChrX: 112.6373530.868710.00e+000.243-0.2150.292
468 +1443647_atRoraretinoic acid receptor-related orphan receptor alpha; antisense in intron 1Chr9: 68.5035967.46911.9Chr15: 13.1492480.868710.00e+000.387-0.0150.942
469 +1427727_x_atPsg19pregnancy specific glycoprotein 19Chr7: 19.3099358.22112.5Chr3: 10.3271010.868710.00e+000.233-0.3890.050
470 +1446347_atEplinESTs, Weakly similar to RIKEN cDNA 5730493B19 [] [M.musculus]Chr2: 92.8806648.85112.0Chr19: 53.4592780.868710.00e+00------
471 +1425244_a_atThegtesticular haploid expressed geneChr10: 79.0465087.86214.2Chr15: 13.1492480.868710.00e+000.197-0.0990.629
472 +1451823_atClca4chloride channel calcium activated 4Chr3: 144.4857666.69111.0ChrX: 112.6373530.868710.00e+000.280-0.0870.674
473 +1426101_atKlhl2kelch-like 2, Mayven (Drosophila)Chr8: 67.3315497.31211.1Chr15: 13.1492480.868710.00e+000.2600.2510.215
474 +1436730_atMclcmyeloid cell leukemia sequence 1, related sequence 1Chr3: 108.4811938.33014.3Chr15: 13.1492480.867710.00e+000.154----
475 +1444191_atBB2104610 day neonate lung cDNA, RIKEN full-length enriched library, clone:E030029G01 product:unclassifiable, full insert sequence.Chr12: 100.7863867.43114.1Chr15: 13.1492480.867710.00e+00------
476 +1456594_atBB728372polymorphic LTR from sequence in BB728372 (distal Chr 15 in BXD Eye data)Chr15: 103.2023976.75714.8Chr3: 10.3271010.867710.00e+00------
477 +1436785_a_at1110069O07RikRIKEN cDNA 1110069O07 geneChr11: 3.9788079.18611.9Chr15: 13.1492480.867710.00e+00--0.0820.689
478 +1447530_atF8afactor 8-associated gene A; proximal 3' UTRChrX: 70.47480512.01510.5Chr15: 13.1492480.867710.00e+000.2130.1650.421
479 +1427169_at2810455B10RikRIKEN cDNA 2810455B10 geneChr1: 62.3224568.06713.5Chr19: 53.4592780.867710.00e+000.200----
480 +1425333_at1810048P08RikRIKEN cDNA 1810048P08 geneChr6: 87.7388779.03811.4Chr1: 193.7319960.867710.00e+000.177----
481 +1442985_atHmcn1hemicentin (fibulin 6, extracellular matrix EGF-domain protein linked to macular degeneration); antisense in intron 7 from EST AU046291Chr1: 152.6600407.30111.5Chr15: 13.1492480.867710.00e+00--0.0410.841
482 +1456731_x_atPolr3kpolymerase (RNA) III (DNA directed) polypeptide KChr2: 181.6041308.92610.8Chr1: 193.7319960.867710.00e+000.249-0.1470.473
483 +1459063_at4833412C05RikESTsChr18: 61.3389776.53311.1Chr3: 10.3271010.866710.00e+00--0.4150.035
484 +1432888_at4930455M05RikRIKEN cDNA 4930455M05 geneChr13: 59.5207806.72814.8Chr3: 10.3271010.866710.00e+00------
485 +1447759_x_atCcdc22coiled-coil domain containing 22; 3' UTRChrX: 7.17093610.0088.9Chr3: 10.3271010.866710.00e+00------
486 +1452889_atLhppphospholysine phosphohistidine inorganic pyrophosphate phosphatase; mid and distal 3' UTRChr7: 139.89757210.04912.4ChrX: 112.6373530.866710.00e+000.202----
487 +1443490_atTpm3tropomyosin 3, gammaChr3: 89.8861638.45511.9Chr15: 13.1492480.866710.00e+000.3180.1960.337
488 +1430055_at1700080G18RikRIKEN cDNA 1700080G18 geneChr6: 29.9974407.97115.6Chr15: 13.1492480.866710.00e+00--0.1350.512
489 +1432682_at5530400K19RikRIKEN cDNA 5530400K19 geneChr1: 155.5631947.02414.8ChrX: 112.6373530.866710.00e+00------
490 +1418767_atCyp4f13cytochrome P450, family 4, subfamily f, polypeptide 13; last 5 exons and 3' UTRChr17: 33.0616469.00813.1Chr1: 193.7319960.866710.00e+000.2630.3320.098
491 +1418661_atAbhd2abhydrolase domain containing 2Chr7: 86.5007317.74617.9Chr15: 13.1492480.866710.00e+000.2430.1040.613
492 +1442360_atA930012M21RikRIKEN cDNA A930012M21 geneChr5: 132.1727686.77212.2Chr15: 13.1492480.866710.00e+00------
493 +1430715_at2310014L17RikRIKEN cDNA 2310014L17 geneChr7: 13.5147707.29613.5Chr15: 13.1492480.865710.00e+00---0.1340.513
494 +1444281_atCamk2dcalcium/calmodulin-dependent protein kinase II, delta; intron 2Chr3: 126.3068046.78912.4Chr1: 193.7319960.865710.00e+000.3170.3250.105
495 +1427911_at2610307O08RikRIKEN cDNA 2610307O08 geneChr18: 35.8939488.33312.0Chr15: 13.1492480.865710.00e+000.193----
496 +1444928_atSlc35f1solute carrier family 35, member F1; antisense in intron 1 (from AK141358)Chr10: 52.4143137.39611.6Chr15: 13.1492480.865710.00e+000.1120.2060.312
497 +1443318_atE130016E03RikRIKEN cDNA E130016E03 geneChr4: 11.5114657.71912.2ChrX: 112.6373530.865710.00e+00---0.3460.083
498 +1443687_x_atH2-DMb1histocompatibility 2, class II, locus Mb1Chr17: 33.7620287.79812.4Chr14: 118.8616530.865710.00e+000.2470.0770.708
499 +1425540_atOtcornithine transcarbamylaseChrX: 9.8934727.97612.5ChrX: 112.6373530.864710.00e+000.339-0.0770.710
500 +1420943_atZfp185zinc finger protein 185ChrX: 70.2750247.52211.0Chr2: 178.9427860.864710.00e+000.2390.0940.648

+ + + +

+

+

- Pearson    Spearman Rank
+
+
+ Pearson +     + Spearman Rank +



-- cgit v1.2.3 From 73fe24131cbd89cdeb6c4c1027c75ca0b6bba3d5 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Tue, 25 Sep 2012 15:44:51 -0500 Subject: Fixed issue with parent strains appearing twice and began replacing variable names (strain -> sample, for example) --- misc/notes.txt | 14 +- wqflask/base/webqtlFormData.py | 90 ++--- wqflask/base/webqtlTrait.py | 34 +- wqflask/wqflask/show_trait/DataEditingPage.py | 373 +++++++++------------ .../new/javascript/trait_data_and_analysis.coffee | 4 +- .../new/javascript/trait_data_and_analysis.js | 4 +- .../wqflask/templates/trait_data_and_analysis.html | 30 +- 7 files changed, 245 insertions(+), 304 deletions(-) (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index 9d31fb5d..76962804 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -6,6 +6,9 @@ source ~/ve27/bin/activate To set WQFLASK_SETTINGS environment variable: export WQFLASK_SETTINGS=~/gene/wqflask/cfg/zach_settings.py (or wherever file is located) +To change screen environment variable (if man not working or to get color, for example): +export TERM=screen + To search for commands in history if necessary: history | grep "(whatever is being searched for)" @@ -44,4 +47,13 @@ unset SSH_ASKPASS Python stuff: -Classes should always inherit "object" \ No newline at end of file +Classes should always inherit "object" + +=========================================== + +htop: Gives information on processes, cpu/memory load, etc +dstat: Also gives various system information, resource usage, etc +df: Reports file system disk space usage + + + diff --git a/wqflask/base/webqtlFormData.py b/wqflask/base/webqtlFormData.py index a8aef2a5..eb1ebd5e 100755 --- a/wqflask/base/webqtlFormData.py +++ b/wqflask/base/webqtlFormData.py @@ -46,7 +46,7 @@ from utility import webqtlUtil class webqtlFormData: 'Represents data from a WebQTL form page, needed to generate the next page' - attrs = ('formID','RISet','genotype','strainlist','allstrainlist', + attrs = ('formID','RISet','genotype','samplelist','allsamplelist', 'suggestive','significance','submitID','identification', 'enablevariance', 'nperm','nboot','email','incparentsf1','genotype_1','genotype_2','traitInfo') @@ -116,12 +116,12 @@ class webqtlFormData: self.nboot = set_number(self.nboot) - #if self.allstrainlist: - # self.allstrainlist = map(string.strip, string.split(self.allstrainlist)) - print("self.allstrainlist is:", self.allstrainlist) - if self.allstrainlist: - self.allstrainlist = self.allstrainlist.split() - print("now self.allstrainlist is:", self.allstrainlist) + #if self.allsamplelist: + # self.allsamplelist = map(string.strip, string.split(self.allsamplelist)) + print("self.allsamplelist is:", self.allsamplelist) + if self.allsamplelist: + self.allsamplelist = self.allsamplelist.split() + print("now self.allsamplelist is:", self.allsamplelist) #self.readGenotype() #self.readData() @@ -183,7 +183,7 @@ class webqtlFormData: self.incparentsf1 = 0 self.genotype = self.genotype_1 - self.strainlist = list(self.genotype.prgy) + self.samplelist = list(self.genotype.prgy) self.f1list = [] self.parlist = [] @@ -193,7 +193,7 @@ class webqtlFormData: self.parlist = [_mat, _pat] - def readData(self, strainlist, incf1=None): + def readData(self, samplelist, incf1=None): '''read user input data or from trait data and analysis form''' if incf1 == None: @@ -201,11 +201,11 @@ class webqtlFormData: if not self.genotype: self.readGenotype() - if not strainlist: + if not samplelist: if incf1: - strainlist = self.f1list + self.strainlist + samplelist = self.f1list + self.samplelist else: - strainlist = self.strainlist + samplelist = self.samplelist #print("before traitfiledata self.traitfile is:", pf(self.traitfile)) @@ -223,7 +223,7 @@ class webqtlFormData: except ValueError: return None - print("bottle strainlist is:", strainlist) + print("bottle samplelist is:", samplelist) if traitfiledata: tt = traitfiledata.split() values = map(webqtlUtil.StringAsFloat, tt) @@ -232,15 +232,15 @@ class webqtlFormData: values = map(webqtlUtil.StringAsFloat, tt) else: print("mapping formdataasfloat") - #values = map(self.FormDataAsFloat, strainlist) - values = [to_float(getattr(self, key)) for key in strainlist] + #values = map(self.FormDataAsFloat, samplelist) + values = [to_float(getattr(self, key)) for key in samplelist] print("rocket values is:", values) - if len(values) < len(strainlist): - values += [None] * (len(strainlist) - len(values)) - elif len(values) > len(strainlist): - values = values[:len(strainlist)] + if len(values) < len(samplelist): + values += [None] * (len(samplelist) - len(values)) + elif len(values) > len(samplelist): + values = values[:len(samplelist)] print("now values is:", values) @@ -251,58 +251,58 @@ class webqtlFormData: tt = variancepastedata.split() variances = map(webqtlUtil.StringAsFloat, tt) else: - variances = map(self.FormVarianceAsFloat, strainlist) + variances = map(self.FormVarianceAsFloat, samplelist) - if len(variances) < len(strainlist): - variances += [None]*(len(strainlist) - len(variances)) - elif len(variances) > len(strainlist): - variances = variances[:len(strainlist)] + if len(variances) < len(samplelist): + variances += [None]*(len(samplelist) - len(variances)) + elif len(variances) > len(samplelist): + variances = variances[:len(samplelist)] if Nfiledata: tt = string.split(Nfiledata) - nstrains = map(webqtlUtil.IntAsFloat, tt) - if len(nstrains) < len(strainlist): - nstrains += [None]*(len(strainlist) - len(nstrains)) + nsamples = map(webqtlUtil.IntAsFloat, tt) + if len(nsamples) < len(samplelist): + nsamples += [None]*(len(samplelist) - len(nsamples)) else: - nstrains = map(self.FormNAsFloat, strainlist) + nsamples = map(self.FormNAsFloat, samplelist) - ##values, variances, nstrains is obsolete + ##values, variances, nsamples is obsolete self.allTraitData = {} - for i, _strain in enumerate(strainlist): + for i, _sample in enumerate(samplelist): if values[i] != None: - self.allTraitData[_strain] = webqtlCaseData( - _strain, values[i], variances[i], nstrains[i]) + self.allTraitData[_sample] = webqtlCaseData( + _sample, values[i], variances[i], nsamples[i]) print("allTraitData is:", pf(self.allTraitData)) - def informativeStrains(self, strainlist=None, include_variances = None): - '''if readData was called, use this to output informative strains (strain with values)''' + def informativeStrains(self, samplelist=None, include_variances = None): + '''if readData was called, use this to output informative samples (sample with values)''' - if not strainlist: - strainlist = self.strainlist + if not samplelist: + samplelist = self.samplelist - strains = [] + samples = [] values = [] variances = [] #print("self.allTraitData is:", pf(self.allTraitData)) - for strain in strainlist: - if strain in self.allTraitData: - _val, _var = self.allTraitData[strain].value, self.allTraitData[strain].variance + for sample in samplelist: + if sample in self.allTraitData: + _val, _var = self.allTraitData[sample].value, self.allTraitData[sample].variance if _val != None: if include_variances: if _var != None: - strains.append(strain) + samples.append(sample) values.append(_val) variances.append(_var) else: - strains.append(strain) + samples.append(sample) values.append(_val) variances.append(None) - return strains, values, variances, len(strains) + return samples, values, variances, len(samples) @@ -336,8 +336,8 @@ class webqtlFormData: self.identification = 'BXD : Coat color example by Lu Lu, et al' #self.readGenotype() #self.genotype.ReadMM('AXBXAforQTL') - #self.strainlist = map((lambda x, y='': '%s%s' % (y,x)), self.genotype.prgy) - #self.strainlist.sort() + #self.samplelist = map((lambda x, y='': '%s%s' % (y,x)), self.genotype.prgy) + #self.samplelist.sort() self.allTraitData = {'BXD29': webqtlCaseData(3), 'BXD28': webqtlCaseData(2), 'BXD25': webqtlCaseData(2), 'BXD24': webqtlCaseData(2), 'BXD27': webqtlCaseData(2), 'BXD21': webqtlCaseData(1), 'BXD20': webqtlCaseData(4), 'BXD23': webqtlCaseData(4), diff --git a/wqflask/base/webqtlTrait.py b/wqflask/base/webqtlTrait.py index 8240eafc..4d642ffe 100755 --- a/wqflask/base/webqtlTrait.py +++ b/wqflask/base/webqtlTrait.py @@ -160,20 +160,20 @@ class webqtlTrait: __str__ = getName __repr__ = __str__ - def exportData(self, strainlist, type="val"): + def exportData(self, samplelist, type="val"): """ - export data according to strainlist + export data according to samplelist mostly used in calculating correlation """ result = [] - for strain in strainlist: - if self.data.has_key(strain): + for sample in samplelist: + if self.data.has_key(sample): if type=='val': - result.append(self.data[strain].val) + result.append(self.data[sample].val) elif type=='var': - result.append(self.data[strain].var) + result.append(self.data[sample].var) elif type=='N': - result.append(self.data[strain].N) + result.append(self.data[sample].N) else: raise KeyError, `type`+' type is incorrect.' else: @@ -182,19 +182,19 @@ class webqtlTrait: def exportInformative(self, incVar=0): """ - export informative strain + export informative sample mostly used in qtl regression """ - strains = [] + samples = [] vals = [] vars = [] - for strain, value in self.data.items(): + for sample, value in self.data.items(): if value.val != None: if not incVar or value.var != None: - strains.append(strain) + samples.append(sample) vals.append(value.val) vars.append(value.var) - return strains, vals, vars + return samples, vals, vars # @@ -225,10 +225,10 @@ class webqtlTrait: - def retrieveData(self, strainlist=None): + def retrieveData(self, samplelist=None): - if strainlist == None: - strainlist = [] + if samplelist == None: + samplelist = [] assert self.db and self.cursor if self.db.type == 'Temp': @@ -334,10 +334,10 @@ class webqtlTrait: if results: self.mysqlid = results[0][-1] - #if strainlist: + #if samplelist: for item in results: #name, value, variance, num_cases = item - if not strainlist or (strainlist and name in strainlist): + if not samplelist or (samplelist and name in samplelist): #if value != None: # num_cases = None # if self.db.type in ('Publish', 'Temp'): diff --git a/wqflask/wqflask/show_trait/DataEditingPage.py b/wqflask/wqflask/show_trait/DataEditingPage.py index 13de0b40..01541e9e 100755 --- a/wqflask/wqflask/show_trait/DataEditingPage.py +++ b/wqflask/wqflask/show_trait/DataEditingPage.py @@ -108,9 +108,9 @@ class DataEditingPage(templatePage): bootCheck = None, permCheck = None, applyVarianceSE = None, - strainNames = '_', - strainVals = '_', - strainVars = '_', + sampleNames = '_', + sampleVals = '_', + sampleVars = '_', otherStrainNames = '_', otherStrainVals = '_', otherStrainVars = '_', @@ -196,8 +196,8 @@ class DataEditingPage(templatePage): # self.dispTraitValues(fd, varianceDataPage, nCols, thisTrait) # - if fd.allstrainlist: - hddn['allstrainlist'] = string.join(fd.allstrainlist, ' ') + if fd.allsamplelist: + hddn['allsamplelist'] = string.join(fd.allsamplelist, ' ') # We put isSE into hddn if nCols == 6 and fd.varianceDispName != 'Variance': @@ -892,87 +892,87 @@ class DataEditingPage(templatePage): ########################################## def dispBasicStatistics(self, fd, thisTrait): - #XZ, June 22, 2011: The definition and usage of primary_strains, other_strains, specialStrains, all_strains are not clear and hard to understand. But since they are only used in this function for draw graph purpose, they will not hurt the business logic outside. As of June 21, 2011, this function seems work fine, so no hurry to clean up. These parameters and code in this function should be cleaned along with fd.f1list, fd.parlist, fd.strainlist later. + #XZ, June 22, 2011: The definition and usage of primary_samples, other_samples, specialStrains, all_samples are not clear and hard to understand. But since they are only used in this function for draw graph purpose, they will not hurt the business logic outside. As of June 21, 2011, this function seems work fine, so no hurry to clean up. These parameters and code in this function should be cleaned along with fd.f1list, fd.parlist, fd.samplelist later. #stats_row = HT.TR() #stats_cell = HT.TD() if fd.genotype.type == "riset": - strainlist = fd.f1list + fd.strainlist + samplelist = fd.f1list + fd.samplelist else: - strainlist = fd.f1list + fd.parlist + fd.strainlist + samplelist = fd.f1list + fd.parlist + fd.samplelist - other_strains = [] #XZ: strain that is not of primary group - specialStrains = [] #XZ: This might be replaced by other_strains / ZS: It is just other strains without parent/f1 strains. - all_strains = [] - primary_strains = [] #XZ: strain of primary group, e.g., BXD, LXS + other_samples = [] #XZ: sample that is not of primary group + specialStrains = [] #XZ: This might be replaced by other_samples / ZS: It is just other samples without parent/f1 samples. + all_samples = [] + primary_samples = [] #XZ: sample of primary group, e.g., BXD, LXS #self.MDP_menu = HT.Select(name='stats_mdp', Class='stats_mdp') self.MDP_menu = [] # We're going to use the same named data structure as in the old version # but repurpose it for Jinja2 as an array - for strain in thisTrait.data.keys(): - strainName = strain.replace("_2nd_", "") - if strain not in strainlist: - if thisTrait.data[strainName].value != None: - if strain.find('F1') < 0: - specialStrains.append(strain) - if (thisTrait.data[strainName].value != None) and (strain not in (fd.f1list + fd.parlist)): - other_strains.append(strain) #XZ: at current stage, other_strains doesn't include parent strains and F1 strains of primary group + for sample in thisTrait.data.keys(): + sampleName = sample.replace("_2nd_", "") + if sample not in samplelist: + if thisTrait.data[sampleName].value != None: + if sample.find('F1') < 0: + specialStrains.append(sample) + if (thisTrait.data[sampleName].value != None) and (sample not in (fd.f1list + fd.parlist)): + other_samples.append(sample) #XZ: at current stage, other_samples doesn't include parent samples and F1 samples of primary group else: - if (thisTrait.data[strainName].value != None) and (strain not in (fd.f1list + fd.parlist)): - primary_strains.append(strain) #XZ: at current stage, the primary_strains is the same as fd.strainlist / ZS: I tried defining primary_strains as fd.strainlist instead, but in some cases it ended up including the parent strains (1436869_at BXD) - - if len(other_strains) > 3: - other_strains.sort(key=webqtlUtil.natsort_key) - primary_strains.sort(key=webqtlUtil.natsort_key) - primary_strains = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + primary_strains #XZ: note that fd.f1list and fd.parlist are added. - all_strains = primary_strains + other_strains - other_strains = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + other_strains #XZ: note that fd.f1list and fd.parlist are added. + if (thisTrait.data[sampleName].value != None) and (sample not in (fd.f1list + fd.parlist)): + primary_samples.append(sample) #XZ: at current stage, the primary_samples is the same as fd.samplelist / ZS: I tried defining primary_samples as fd.samplelist instead, but in some cases it ended up including the parent samples (1436869_at BXD) + + if len(other_samples) > 3: + other_samples.sort(key=webqtlUtil.natsort_key) + primary_samples.sort(key=webqtlUtil.natsort_key) + primary_samples = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + primary_samples #XZ: note that fd.f1list and fd.parlist are added. + all_samples = primary_samples + other_samples + other_samples = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + other_samples #XZ: note that fd.f1list and fd.parlist are added. print("ac1") # This is the one used for first sall3 self.MDP_menu.append(('All Cases','0')) self.MDP_menu.append(('%s Only' % fd.RISet, '1')) self.MDP_menu.append(('Non-%s Only' % fd.RISet, '2')) else: - if (len(other_strains) > 0) and (len(primary_strains) + len(other_strains) > 3): + if (len(other_samples) > 0) and (len(primary_samples) + len(other_samples) > 3): print("ac2") self.MDP_menu.append(('All Cases','0')) self.MDP_menu.append(('%s Only' % fd.RISet,'1')) self.MDP_menu.append(('Non-%s Only' % fd.RISet,'2')) - all_strains = primary_strains - all_strains.sort(key=webqtlUtil.natsort_key) - all_strains = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + all_strains - primary_strains = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + primary_strains + all_samples = primary_samples + all_samples.sort(key=webqtlUtil.natsort_key) + all_samples = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + all_samples + primary_samples = map(lambda X:"_2nd_"+X, fd.f1list + fd.parlist) + primary_samples else: print("ac3") - all_strains = strainlist + all_samples = samplelist - other_strains.sort(key=webqtlUtil.natsort_key) - all_strains = all_strains + other_strains + other_samples.sort(key=webqtlUtil.natsort_key) + all_samples = all_samples + other_samples - if (len(other_strains)) > 0 and (len(primary_strains) + len(other_strains) > 4): - #One set of vals for all, selected strain only, and non-selected only + if (len(other_samples)) > 0 and (len(primary_samples) + len(other_samples) > 4): + #One set of vals for all, selected sample only, and non-selected only vals1 = [] vals2 = [] vals3 = [] - #Using all strains/cases for values - #for strain_type in (all_strains, primary_strains, other_strains): - for strainNameOrig in all_strains: - strainName = strainNameOrig.replace("_2nd_", "") + #Using all samples/cases for values + #for sample_type in (all_samples, primary_samples, other_samples): + for sampleNameOrig in all_samples: + sampleName = sampleNameOrig.replace("_2nd_", "") #try: print("* type of thisTrait:", type(thisTrait)) print(" name:", thisTrait.__class__.__name__) print(" thisTrait:", thisTrait) - print(" type of thisTrait.data[strainName]:", type(thisTrait.data[strainName])) - print(" name:", thisTrait.data[strainName].__class__.__name__) - print(" thisTrait.data[strainName]:", thisTrait.data[strainName]) - thisval = thisTrait.data[strainName].value + print(" type of thisTrait.data[sampleName]:", type(thisTrait.data[sampleName])) + print(" name:", thisTrait.data[sampleName].__class__.__name__) + print(" thisTrait.data[sampleName]:", thisTrait.data[sampleName]) + thisval = thisTrait.data[sampleName].value print(" thisval:", thisval) - thisvar = thisTrait.data[strainName].variance + thisvar = thisTrait.data[sampleName].variance print(" thisvar:", thisvar) - thisValFull = [strainName, thisval, thisvar] + thisValFull = [sampleName, thisval, thisvar] print(" thisValFull:", thisValFull) #except: # continue @@ -980,33 +980,33 @@ class DataEditingPage(templatePage): vals1.append(thisValFull) - #vals1 = [[strainNameOrig.replace("_2nd_", ""), - # thisTrait.data[strainName].val, - # thisTrait.data[strainName].var] - # for strainNameOrig in all_strains]] + #vals1 = [[sampleNameOrig.replace("_2nd_", ""), + # thisTrait.data[sampleName].val, + # thisTrait.data[sampleName].var] + # for sampleNameOrig in all_samples]] # - #Using just the RISet strain - for strainNameOrig in primary_strains: - strainName = strainNameOrig.replace("_2nd_", "") + #Using just the RISet sample + for sampleNameOrig in primary_samples: + sampleName = sampleNameOrig.replace("_2nd_", "") #try: - thisval = thisTrait.data[strainName].value - thisvar = thisTrait.data[strainName].variance - thisValFull = [strainName,thisval,thisvar] + thisval = thisTrait.data[sampleName].value + thisvar = thisTrait.data[sampleName].variance + thisValFull = [sampleName,thisval,thisvar] #except: # continue vals2.append(thisValFull) - #Using all non-RISet strains only - for strainNameOrig in other_strains: - strainName = strainNameOrig.replace("_2nd_", "") + #Using all non-RISet samples only + for sampleNameOrig in other_samples: + sampleName = sampleNameOrig.replace("_2nd_", "") #try: - thisval = thisTrait.data[strainName].value - thisvar = thisTrait.data[strainName].variance - thisValFull = [strainName,thisval,thisvar] + thisval = thisTrait.data[sampleName].value + thisvar = thisTrait.data[sampleName].variance + thisValFull = [sampleName,thisval,thisvar] #except: # continue @@ -1017,14 +1017,14 @@ class DataEditingPage(templatePage): else: vals = [] - #Using all strains/cases for values - for strainNameOrig in all_strains: - strainName = strainNameOrig.replace("_2nd_", "") + #Using all samples/cases for values + for sampleNameOrig in all_samples: + sampleName = sampleNameOrig.replace("_2nd_", "") #try: - thisval = thisTrait.data[strainName].value - thisvar = thisTrait.data[strainName].variance - thisValFull = [strainName,thisval,thisvar] + thisval = thisTrait.data[sampleName].value + thisvar = thisTrait.data[sampleName].variance + thisValFull = [sampleName,thisval,thisvar] #except: # continue @@ -1041,10 +1041,10 @@ class DataEditingPage(templatePage): stats_script_text = """$(function() { $("#stats_tabs").tabs();});""" #stats_cell.append(stats_container) break - elif (i == 1 and len(primary_strains) < 4): + elif (i == 1 and len(primary_samples) < 4): stats_container = HT.Div(id="stats_tabs%s" % i, Class="ui-tabs") stats_container.append(HT.Div(HT.Italic("Fewer than 4 " + fd.RISet + " case data were entered. No statistical analysis has been attempted."))) - elif (i == 2 and len(other_strains) < 4): + elif (i == 2 and len(other_samples) < 4): stats_container = HT.Div(id="stats_tabs%s" % i, Class="ui-tabs") stats_container.append(HT.Div(HT.Italic("Fewer than 4 non-" + fd.RISet + " case data were entered. No statistical analysis has been attempted."))) stats_script_text = """$(function() { $("#stats_tabs0").tabs(); $("#stats_tabs1").tabs(); $("#stats_tabs2").tabs();});""" @@ -1609,14 +1609,15 @@ class DataEditingPage(templatePage): print("in dispTraitValues") if fd.genotype.type == "riset": - allstrainlist_neworder = fd.f1list + fd.strainlist + allsamplelist_neworder = fd.f1list + fd.samplelist else: - allstrainlist_neworder = fd.f1list + fd.parlist + fd.strainlist + allsamplelist_neworder = fd.f1list + fd.parlist + fd.samplelist attribute_ids = [] attribute_names = [] #try: - #ZS: Id values for this trait's extra attributes; used to create "Exclude" dropdown and query for attribute values and create + #ZS: Id values for this trait's extra attributes; + #used to create "Exclude" dropdown and query for attribute values and create self.cursor.execute("""SELECT CaseAttribute.Id, CaseAttribute.Name FROM CaseAttribute, CaseAttributeXRef WHERE CaseAttributeXRef.ProbeSetFreezeId = %s AND @@ -1624,150 +1625,78 @@ class DataEditingPage(templatePage): group by CaseAttributeXRef.CaseAttributeId""", (str(thisTrait.db.id),)) - #exclude_menu = HT.Select(name="exclude_menu") - #dropdown_menus = [] #ZS: list of dropdown menus with the distinct values of each attribute (contained in DIVs so the style parameter can be edited and they can be hidden) - - #for attribute in self.cursor.fetchall(): - # #attribute_ids.append(attribute[0]) - # #attribute_names.append(attribute[1]) - # pass for this_attr_name in attribute_names: - #exclude_menu.append((this_attr_name.capitalize(), this_attr_name)) # Todo: Needs testing still! self.cursor.execute("""SELECT DISTINCT CaseAttributeXRef.Value FROM CaseAttribute, CaseAttributeXRef WHERE CaseAttribute.Name = %s AND CaseAttributeXRef.CaseAttributeId = CaseAttribute.Id""", (this_attr_name,)) - #try: + distinct_values = self.cursor.fetchall() - #attr_value_menu_div = HT.Div(style="display:none;", Class="attribute_values") #container used to show/hide dropdown menus - #attr_value_menu = HT.Select(name=this_attr_name) - #attr_value_menu.append(("None", "show_all")) - #for value in distinct_values: - # #attr_value_menu.append((str(value[0]), value[0])) - # pass - #attr_value_menu_div.append(attr_value_menu) - #dropdown_menus.append(attr_value_menu_div) - #except: - # pass - #except: - # pass - - #for strain in thisTrait.data.keys(): - # if strain not in allstrainlist_neworder: - # pass - # #other_strains.append(strain) - # - #if other_strains: - # #blockMenu.append(('%s Only' % fd.RISet,'1')) - # #blockMenu.append(('Non-%s Only' % fd.RISet,'0')) - # #blockMenuSpan.append(blockMenu) - # pass - #else: - # pass - - #showHideOutliers = HT.Input(type='button', name='showHideOutliers', value=' Hide Outliers ', Class='button') - #showHideMenuOptions = HT.Span(Id="showHideOptions", style="line-height:225%;") - #if other_strains: - # pass - #showHideMenuOptions.append(HT.Bold("  Block samples by index:    "), blockSamplesField, "   ", blockMenuSpan, "   ", blockSamplesButton, HT.BR()) - #else: - # pass - #showHideMenuOptions.append(HT.Bold("  Block samples by index:    "), blockSamplesField, "   ", blockSamplesButton, HT.BR()) - #exportButton = HT.Input(type='button', name='export', value=' Export ', Class='button') - #if len(attribute_names) > 0: - # excludeButton = HT.Input(type='button', name='excludeGroup', value=' Block ', Class='button') - #showHideMenuOptions.append(HT.Bold("  Block samples by group:"), " "*5, exclude_menu, " "*5) - #for menu in dropdown_menus: - # pass - #showHideMenuOptions.append(menu) - #showHideMenuOptions.append(" "*5, excludeButton, HT.BR()) - #showHideMenuOptions.append(HT.Bold("  Options:"), " "*5, showHideNoValue, " "*5, showHideOutliers, " "*5, resetButton, " "*5, exportButton) - - #traitTableOptions.append(showHideMenuOptions,HT.BR(),HT.BR()) - #traitTableOptions.append(HT.Span("  Outliers highlighted in ", HT.Bold(" red ", style="background-color:red;"), " can be hidden using the ", - # HT.Strong(" Hide Outliers "), " button,",HT.BR(),"  and samples with no value (x) can be hidden by clicking ", - # HT.Strong(" Hide No Value "), "."), HT.BR()) - - - #dispintro = HT.Paragraph("Edit or delete values in the Trait Data boxes, and use the ", HT.Strong("Reset"), " option as needed.",Class="fs12", style="margin-left:20px;") - # - #table = HT.TableLite(cellspacing=0, cellpadding=0, width="100%", Class="target5") #Everything needs to be inside this table object in order for the toggle to work - #container = HT.Div() #This will contain everything and be put into a cell of the table defined above - # - #container.append(dispintro, traitTableOptions, HT.BR()) - - #primary_table = HT.TableLite(cellspacing=0, cellpadding=0, Id="sortable1", Class="tablesorter") - #primary_header = self.getTableHeader(fd=fd, thisTrait=thisTrait, nCols=nCols, attribute_names=attribute_names) #Generate header for primary table object - - #other_strainsExist = False - this_trait_strains = set(thisTrait.data.keys()) - #ZS - Checks if there are any strains in this_trait_strains that aren't in allstrainlist_neworder - other_strainsExist = this_trait_strains - set(allstrainlist_neworder) - - #for strain in thisTrait.data.keys(): - # print("hjl - strain is:", strain) - # if strain not in allstrainlist_neworder: - # other_strainsExist = True - # break + this_trait_samples = set(thisTrait.data.keys()) + #ZS - Checks if there are any samples in this_trait_samples that aren't in allsamplelist_neworder + other_samplesExist = this_trait_samples - set(allsamplelist_neworder) mainForm = None # Just trying to get things working - primary_strainlist = fd.parlist + allstrainlist_neworder + primary_samplelist = allsamplelist_neworder + + print("primary_samplelist is:", pf(primary_samplelist)) - primary_strains = self.create_strain_objects(fd=fd, + primary_samples = self.create_sample_objects(fd=fd, varianceDataPage=varianceDataPage, - strainlist=primary_strainlist, + samplelist=primary_samplelist, mainForm=mainForm, thisTrait=thisTrait, - other_strainsExist=other_strainsExist, + other_samplesExist=other_samplesExist, attribute_ids=attribute_ids, attribute_names=attribute_names, - strains='primary') - + samples='primary') + - other_strains = [] - for strain in thisTrait.data.keys(): - print("hjk - strain is:", strain) - if strain not in allstrainlist_neworder + fd.f1list + fd.parlist: - allstrainlist_neworder.append(strain) - other_strains.append(strain) + other_samples = [] + for sample in thisTrait.data.keys(): + print("hjk - sample is:", sample) + if sample not in allsamplelist_neworder: + allsamplelist_neworder.append(sample) + other_samples.append(sample) - if other_strains: + if other_samples: unappended_par_f1 = fd.f1list + fd.parlist - par_f1_strains = ["_2nd_" + strain for strain in unappended_par_f1] + par_f1_samples = ["_2nd_" + sample for sample in unappended_par_f1] - other_strains.sort() #Sort other strains - other_strains = par_f1_strains + other_strains + other_samples.sort() #Sort other samples + other_samples = par_f1_samples + other_samples - other_strains = self.create_strain_objects(fd=fd, + other_samples = self.create_sample_objects(fd=fd, varianceDataPage=varianceDataPage, - strainlist=other_strains, + samplelist=other_samples, mainForm=mainForm, thisTrait=thisTrait, attribute_ids=attribute_ids, attribute_names=attribute_names, - strains='other') + samples='other') #TODO: Figure out why this if statement is written this way - Zach - if (other_strains or (fd.f1list and thisTrait.data.has_key(fd.f1list[0])) + if (other_samples or (fd.f1list and thisTrait.data.has_key(fd.f1list[0])) or (fd.f1list and thisTrait.data.has_key(fd.f1list[1]))): print("hjs") - fd.allstrainlist = allstrainlist_neworder + fd.allsamplelist = allsamplelist_neworder - self.primary_strains = dict(header = "%s Only" % (fd.RISet), - strains = primary_strains,) + self.primary_samples = dict(header = "%s Only" % (fd.RISet), + samples = primary_samples,) - self.other_strains = dict(header = "Non-%s" % (fd.RISet), - strains = other_strains,) + self.other_samples = dict(header = "Non-%s" % (fd.RISet), + samples = other_samples,) + - def create_strain_objects(self, fd, varianceDataPage, strainlist, mainForm, thisTrait, - other_strainsExist=None, attribute_ids=None, - attribute_names=None, strains='primary'): + def create_sample_objects(self, fd, varianceDataPage, samplelist, mainForm, thisTrait, + other_samplesExist=None, attribute_ids=None, + attribute_names=None, samples='primary'): if attribute_ids == None: attribute_ids = [] @@ -1776,47 +1705,47 @@ class DataEditingPage(templatePage): attribute_names = [] #XZ, Aug 23, 2010: I commented the code related to the display of animal case - #strainInfo = thisTrait.has_key('strainInfo') and thisTrait.strainInfo - print("in create_strain_objects") + #sampleInfo = thisTrait.has_key('sampleInfo') and thisTrait.sampleInfo + print("in create_sample_objects") #table_body = [] ################### Only used to find upperBound and lowerBound #vals = [] - #for strainNameOrig in strainlist: - # strainName = strainNameOrig.replace("_2nd_", "") - # print("pen: %s - %s" % (strainNameOrig, strainName)) + #for sampleNameOrig in samplelist: + # sampleName = sampleNameOrig.replace("_2nd_", "") + # print("pen: %s - %s" % (sampleNameOrig, sampleName)) # try: - # thisval = thisTrait.data[strainName].value - # thisvar = thisTrait.data[strainName].variance - # thisValFull = [strainName, thisval, thisvar] + # thisval = thisTrait.data[sampleName].value + # thisvar = thisTrait.data[sampleName].variance + # thisValFull = [sampleName, thisval, thisvar] # # vals.append(thisValFull) # except KeyError: - # print("**x** Skipping:", strainName) + # print("**x** Skipping:", sampleName) # #upperBound, lowerBound = Plot.findOutliers(vals) # ZS: Values greater than upperBound or less than lowerBound are considered outliers. - the_strains = [] + the_samples = [] - for counter, strainNameOrig in enumerate(strainlist, 1): - strainName = strainNameOrig.replace("_2nd_", "") - strainNameAdd = '' - if fd.RISet == 'AXBXA' and strainName in ('AXB18/19/20','AXB13/14','BXA8/17'): - strainNameAdd = HT.Href(url='/mouseCross.html#AXB/BXA', text=HT.Sup('#'), Class='fs12', target="_blank") + for counter, sampleNameOrig in enumerate(samplelist, 1): + sampleName = sampleNameOrig.replace("_2nd_", "") + sampleNameAdd = '' + if fd.RISet == 'AXBXA' and sampleName in ('AXB18/19/20','AXB13/14','BXA8/17'): + sampleNameAdd = HT.Href(url='/mouseCross.html#AXB/BXA', text=HT.Sup('#'), Class='fs12', target="_blank") try: - strain = thisTrait.data[strainName] + sample = thisTrait.data[sampleName] except KeyError: - print("No strain %s, let's create it now" % strainName) - strain = webqtlCaseData.webqtlCaseData(strainName) - print("zyt - strainNameOrig:", strainNameOrig) + print("No sample %s, let's create it now" % sampleName) + sample = webqtlCaseData.webqtlCaseData(sampleName) + print("zyt - sampleNameOrig:", sampleNameOrig) - if strains == 'primary': - strain.this_id = "Primary_" + str(counter) + if samples == 'primary': + sample.this_id = "Primary_" + str(counter) else: - strain.this_id = "Other_" + str(counter) + sample.this_id = "Other_" + str(counter) #### For extra attribute columns; currently only used by two human datasets - Zach if thisTrait and thisTrait.db and thisTrait.db.type == 'ProbeSet': @@ -1828,9 +1757,9 @@ class DataEditingPage(templatePage): WHERE Strain.Name = '%s' and StrainXRef.StrainId = Strain.Id and InbredSet.Id = StrainXRef.InbredSetId and - InbredSet.Name = '%s'""" % (strainName, fd.RISet)) + InbredSet.Name = '%s'""" % (sampleName, fd.RISet)) - strain_id = self.cursor.fetchone()[0] + sample_id = self.cursor.fetchone()[0] attr_counter = 1 # This is needed so the javascript can know which attribute type to associate this value with for the exported excel sheet (each attribute type being a column). for attribute_id in attribute_ids: @@ -1841,7 +1770,7 @@ class DataEditingPage(templatePage): WHERE ProbeSetFreezeId = '%s' AND StrainId = '%s' AND CaseAttributeId = '%s' - group by CaseAttributeXRef.CaseAttributeId""" % (thisTrait.db.id, strain_id, str(attribute_id))) + group by CaseAttributeXRef.CaseAttributeId""" % (thisTrait.db.id, sample_id, str(attribute_id))) attributeValue = self.cursor.fetchone()[0] #Trait-specific attributes, if any @@ -1851,17 +1780,17 @@ class DataEditingPage(templatePage): except: pass - span_Id = strains+"_attribute"+str(attr_counter)+"_sample"+str(i+1) + span_Id = samples+"_attribute"+str(attr_counter)+"_sample"+str(i+1) attr_container = HT.Span(attributeValue, Id=span_Id) attr_className = str(attributeValue) + " " + className table_row.append(HT.TD(attr_container, align='right', Class=attr_className)) attr_counter += 1 - the_strains.append(strain) + the_samples.append(sample) #table_body.append(table_row) - do_outliers(the_strains) - print("*the_strains are [%i]: %s" % (len(the_strains), pf(the_strains))) - return the_strains + do_outliers(the_samples) + print("*the_samples are [%i]: %s" % (len(the_samples), pf(the_samples))) + return the_samples def getTableHeader(self, fd, thisTrait, nCols, attribute_names): @@ -1909,15 +1838,15 @@ class DataEditingPage(templatePage): -def do_outliers(strain_objects): - values = [strain.value for strain in strain_objects if strain.value != None] +def do_outliers(sample_objects): + values = [sample.value for sample in sample_objects if sample.value != None] upper_bound, lower_bound = Plot.find_outliers(values) - for strain in strain_objects: - if strain.value: - if upper_bound and strain.value > upper_bound: - strain.outlier = True - elif lower_bound and strain.value < lower_bound: - strain.outlier = True + for sample in sample_objects: + if sample.value: + if upper_bound and sample.value > upper_bound: + sample.outlier = True + elif lower_bound and sample.value < lower_bound: + sample.outlier = True else: - strain.outlier = False + sample.outlier = False diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee index 94ae0203..803045d5 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.coffee @@ -46,13 +46,13 @@ $ -> all_cases: new Stats([]) console.log("at beginning:", sample_sets) - values = $('#value_table').find(".edit_strain_value") + values = $('#value_table').find(".edit_sample_value") for value in values real_value = $(value).val() row = $(value).closest("tr") category = row[0].id - checkbox = $(row).find(".edit_strain_checkbox") + checkbox = $(row).find(".edit_sample_checkbox") checked = $(checkbox).attr('checked') if checked and is_number(real_value) and real_value != "" diff --git a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js index 9d7918a1..55bc1302 100644 --- a/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js +++ b/wqflask/wqflask/static/new/javascript/trait_data_and_analysis.js @@ -69,13 +69,13 @@ all_cases: new Stats([]) }; console.log("at beginning:", sample_sets); - values = $('#value_table').find(".edit_strain_value"); + values = $('#value_table').find(".edit_sample_value"); for (_i = 0, _len = values.length; _i < _len; _i++) { value = values[_i]; real_value = $(value).val(); row = $(value).closest("tr"); category = row[0].id; - checkbox = $(row).find(".edit_strain_checkbox"); + checkbox = $(row).find(".edit_sample_checkbox"); checked = $(checkbox).attr('checked'); if (checked && is_number(real_value) && real_value !== "") { real_value = parseFloat(real_value); diff --git a/wqflask/wqflask/templates/trait_data_and_analysis.html b/wqflask/wqflask/templates/trait_data_and_analysis.html index 3d2ec636..89ce7d46 100644 --- a/wqflask/wqflask/templates/trait_data_and_analysis.html +++ b/wqflask/wqflask/templates/trait_data_and_analysis.html @@ -14,10 +14,10 @@ - + - + @@ -32,11 +32,11 @@ - + - + @@ -1245,12 +1245,12 @@

- {% for strain_type in (primary_strains, other_strains) %} + {% for sample_type in (primary_samples, other_samples) %}
-

{{ strain_type.header }}

+

{{ sample_type.header }}

-
{# Slightly tortuous, but best way to get the id we need #} +
{# Slightly tortuous, but best way to get the id we need #} @@ -1266,21 +1266,21 @@ - {% for strain in strain_type.strains %} - + {% for sample in sample_type.samples %} + {# Todo: Add IDs #} @@ -1290,8 +1290,8 @@ {# Todo: Add IDs #} {% endfor %} -- cgit v1.2.3 From 2b3aaf9b9da45d6a4c4bb461edb4a2d4247ed8ea Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Tue, 9 Oct 2012 17:49:46 -0500 Subject: Used tidyp to improve/beautify index_page.html --- misc/notes.txt | 5 + wqflask/wqflask/templates/index_page.html | 548 +++++++++++++++--------------- 2 files changed, 288 insertions(+), 265 deletions(-) (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index 76962804..fab9fac6 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -55,5 +55,10 @@ htop: Gives information on processes, cpu/memory load, etc dstat: Also gives various system information, resource usage, etc df: Reports file system disk space usage +=========================================== + +tidyp - Improves/beautifies html code +tidyp -m -i -w 100 index_page.html + diff --git a/wqflask/wqflask/templates/index_page.html b/wqflask/wqflask/templates/index_page.html index d3839c09..a209f302 100644 --- a/wqflask/wqflask/templates/index_page.html +++ b/wqflask/wqflask/templates/index_page.html @@ -1,302 +1,320 @@ {% extends "base.html" %} {% block title %}GeneNetwork{% endblock %} -{% block content %} - - - + + + + {% endblock %} + -- cgit v1.2.3 From bbc5e266213e44af5713f7c9a4bbfd7f1035ad1d Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Fri, 19 Oct 2012 18:26:18 -0500 Subject: Finished gen_select_dataset.py which generates the json file that defines the data structure used for the index page select dropdowns --- misc/notes.txt | 7 +- wqflask/maintenance/gen_select_dataset.py | 724 +-- .../new/javascript/dataset_menu_structure.json | 6792 ++++++++++++++++++++ 3 files changed, 6917 insertions(+), 606 deletions(-) create mode 100644 wqflask/wqflask/static/new/javascript/dataset_menu_structure.json (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index fab9fac6..c12cd2bb 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -17,11 +17,13 @@ python runserver.py =========================================== -Start screen session +Start screen session: byobu -RD (to start) control-a then :multiuser on control-a then :acladd sam +control-a c to create channel + type: screen -list for sessions screen -r zas1024/25679.byobu @@ -31,6 +33,9 @@ screen -r zas1024/ =========================================== +Start up log: +Go to /tmp and tail -f flask_gn_log + Coffeescript Stuff: coffee -c (filename) diff --git a/wqflask/maintenance/gen_select_dataset.py b/wqflask/maintenance/gen_select_dataset.py index 7d2605c1..33813051 100644 --- a/wqflask/maintenance/gen_select_dataset.py +++ b/wqflask/maintenance/gen_select_dataset.py @@ -19,622 +19,136 @@ # # # This module is used by GeneNetwork project (www.genenetwork.org) -# -# Created by GeneNetwork Core Team 2010/08/10 -# -# Last updated by NL 2011/01/27 -# created by Ning Liu 07/01/2010 -# This script is to generate selectDatasetMenu.js file for cascade menu in the main search page http://www.genenetwork.org/. -# This script will be run automatically every one hour or manually when database has been changed . +# This script is to generate the data for the main menus on the home page +# It needs to be run manually when database has been changed . from __future__ import print_function, division -import sys, os +import sys -current_file_name = __file__ -pathname = os.path.dirname( current_file_name ) -abs_path = os.path.abspath(pathname) -sys.path.insert(0, abs_path + '/..') +sys.path.insert(0, "..") import MySQLdb -import os -import string -import time -import datetime - -from base import template -from base import webqtlConfig - -################################################################################# -# input: searchArray, targetValue -# function: retrieve index info of target value in designated array (searchArray) -# output: return index info -################################################################################## -def getIndex(searchArray=None, targetValue=None): - for index in range(len(searchArray)): - if searchArray[index][0]==targetValue: - return index - -# build MySql database connection -con = MySQLdb.Connect(db=webqtlConfig.DB_NAME,host=webqtlConfig.MYSQL_SERVER, user=webqtlConfig.DB_USER,passwd=webqtlConfig.DB_PASSWD) -cursor = con.cursor() -# create js_select.js file -fileHandler = open(webqtlConfig.HTMLPATH + 'javascript/selectDatasetMenu.js', 'w') +import simplejson as json -# define SpeciesString, GroupString, TypeString, DatabasingString, LinkageString for output -# outputSpeciesStr is for building Species Array(sArr) in js file; outputGroupStr is for Group Array(gArr) -# outputTypeStr is for Type Array(tArr); outputDatabaseStr is for Database Array(dArr) -# outputLinkStr is for Linkage Array(lArr) -outputTimeStr ="/* Generated Date : %s , Time : %s */ \n" % (datetime.date.today(),time.strftime("%H:%M ", time.localtime())) -outputTimeStr ="" -outputSpeciesStr ='var sArr = [\n{txt:\'\',val:\'\'},\n' -outputGroupStr ='var gArr = [\n{txt:\'\',val:\'\'},\n' -outputTypeStr ='var tArr = [\n{txt:\'\',val:\'\'},\n' -outputDatabaseStr ='var dArr = [\n{txt:\'\',val:\'\'},\n' -outputLinkStr ='var lArr = [\n null,\n' +from pprint import pformat as pf -# built speices array in js file for select menu in the main search page http://www.genenetwork.org/ -cursor.execute("select Name, MenuName from Species order by OrderId") -speciesResult = cursor.fetchall() -speciesTotalResult = list(speciesResult) -speciesResultsTotalNum = cursor.rowcount -if speciesResultsTotalNum >0: - for speciesItem in speciesResult: - speciesVal = speciesItem[0] - speciesTxt = speciesItem[1] - outputSpeciesStr += '{txt:\'%s\',val:\'%s\'},\n'%(speciesTxt,speciesVal) -# 'All Species' option for 'Species' select menu -outputSpeciesStr +='{txt:\'All Species\',val:\'All Species\'}];\n\n' -#speciesTotalResult is a list which inclues all species' options -speciesTotalResult.append(('All Species','All Species')) - -# built group array in js file for select menu in the main search page http://www.genenetwork.org/ -cursor.execute("select distinct InbredSet.Name, InbredSet.FullName from InbredSet, Species, ProbeFreeze, GenoFreeze, PublishFreeze where InbredSet.SpeciesId= Species.Id and InbredSet.Name != 'BXD300' and (PublishFreeze.InbredSetId = InbredSet.Id or GenoFreeze.InbredSetId = InbredSet.Id or ProbeFreeze.InbredSetId = InbredSet.Id) order by InbredSet.Name") -groupResults = cursor.fetchall() -groupTotalResults = list(groupResults) -groupResultsTotalNum = cursor.rowcount -if groupResultsTotalNum > 0: - for groupItem in groupResults: - groupVal = groupItem[0] - groupTxt = groupItem[1] - outputGroupStr += '{txt:\'%s\',val:\'%s\'},\n'%(groupTxt,groupVal) -# add 'All Groups' option for 'Group' select menu -outputGroupStr +='{txt:\'All Groups\',val:\'all groups\'}];\n\n' -# groupTotalResults is a list which inclues all groups' options -groupTotalResults.append(('all groups','All Groups')) - -# built type array in js file for select menu in the main search page http://www.genenetwork.org/ -cross = groupVal -cursor.execute("select distinct Tissue.Name, concat(Tissue.Name, ' mRNA') from ProbeFreeze, ProbeSetFreeze, InbredSet, Tissue where ProbeFreeze.TissueId = Tissue.Id and ProbeFreeze.InbredSetId = InbredSet.Id and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeSetFreeze.public > %d order by Tissue.Name" % (webqtlConfig.PUBLICTHRESH)) -typeResults = cursor.fetchall() -typeTotalResults = list(typeResults) -typeResultsTotalNum = cursor.rowcount -if typeResultsTotalNum > 0: - for typeItem in typeResults: - typeVal = typeItem[0] - typeTxt = typeItem[1] - outputTypeStr += '{txt:\'%s\',val:\'%s\'},\n'%(typeTxt,typeVal) -# add 'Phenotypes' and 'Genotypes' options for 'Type' select menu -outputTypeStr +='{txt:\'Phenotypes\',val:\'Phenotypes\'},\n' -outputTypeStr +='{txt:\'Genotypes\',val:\'Genotypes\'}];\n\n' -# typeTotalResults is a list which inclues all types' options -typeTotalResults.append(('Phenotypes','Phenotypes')) -typeTotalResults.append(('Genotypes','Genotypes')) - -# built dataset array in js file for select menu in the main search page http://www.genenetwork.org/ -tissue = typeVal -cursor.execute("select ProbeSetFreeze.Name, ProbeSetFreeze.FullName from ProbeSetFreeze, ProbeFreeze, InbredSet, Tissue where ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeFreeze.TissueId = Tissue.Id and ProbeFreeze.InbredSetId = InbredSet.Id and ProbeSetFreeze.public > %d order by ProbeSetFreeze.CreateTime desc" % (webqtlConfig.PUBLICTHRESH)) -datasetResults = cursor.fetchall() -datasetTotalResults = list(datasetResults) -datasetResultsTotalNum = cursor.rowcount -if datasetResultsTotalNum > 0: - for datasetItem in datasetResults: - datasetVal = datasetItem[0] - datasetTxt = datasetItem[1] - outputDatabaseStr += '{txt:\'%s\',val:\'%s\'},\n'%(datasetTxt,datasetVal) - -# This part is to built linkage array in js file, the linkage is among Species, Group, Type and Database. -# The format of linkage array is [speciesIndex, groupIndex, typeIndex, databaseIndex] -if speciesResultsTotalNum >0: - for speciesItem in speciesResult: - speciesVal = speciesItem[0] - sIndex = getIndex(searchArray=speciesTotalResult,targetValue=speciesVal)+1 - - # retrieve group info based on specie - cursor.execute("select distinct InbredSet.Name, InbredSet.FullName from InbredSet, Species, ProbeFreeze, GenoFreeze, PublishFreeze where InbredSet.SpeciesId= Species.Id and Species.Name='%s' and InbredSet.Name != 'BXD300' and (PublishFreeze.InbredSetId = InbredSet.Id or GenoFreeze.InbredSetId = InbredSet.Id or ProbeFreeze.InbredSetId = InbredSet.Id) order by InbredSet.Name" % speciesVal) - groupResults = cursor.fetchall() - groupResultsNum = cursor.rowcount +from base import webqtlConfig - if groupResultsNum > 0: - for groupItem in groupResults: - groupVal = groupItem[0] - gIndex = getIndex(searchArray=groupTotalResults, targetValue=groupVal)+1 - cross = groupVal - # if group also exists in PublishFreeze table, then needs to add related Published Phenotypes in Database Array(dArr) and Linkage Array(lArr) - # 'MDP' case is related to 'Mouse Phenome Database' - cursor.execute("select PublishFreeze.Id from PublishFreeze, InbredSet where PublishFreeze.InbredSetId = InbredSet.Id and InbredSet.Name = '%s'" % cross) - if (cursor.fetchall()): - typeVal = "Phenotypes" - if cross=='MDP': - datasetTxt = "Mouse Phenome Database" +# build MySql database connection +Con = MySQLdb.Connect(db=webqtlConfig.DB_NAME,host=webqtlConfig.MYSQL_SERVER, + user=webqtlConfig.DB_USER, + passwd=webqtlConfig.DB_PASSWD) +Cursor = Con.cursor() + + +def get_species(): + """Build species list""" + Cursor.execute("select Name, MenuName from Species order by OrderId") + species = list(Cursor.fetchall()) + return species + + +def get_groups(species): + """Build groups list""" + groups = {} + for species_name, _species_full_name in species: + Cursor.execute("""select InbredSet.Name, InbredSet.FullName from InbredSet, + Species, + ProbeFreeze, GenoFreeze, PublishFreeze where Species.Name = %s + and InbredSet.SpeciesId = Species.Id and InbredSet.Name != 'BXD300' and + (PublishFreeze.InbredSetId = InbredSet.Id + or GenoFreeze.InbredSetId = InbredSet.Id + or ProbeFreeze.InbredSetId = InbredSet.Id) + group by InbredSet.Name + order by InbredSet.Name""", (species_name)) + groups[species_name] = list(Cursor.fetchall()) + return groups + + +def get_types(groups): + """Build types list""" + types = {} + for species, group_dict in groups.iteritems(): + types[species] = {} + for group_name, _group_full_name in group_dict: + # make group an alias to shorten the code + group = types[species][group_name] = [("Phenotypes", "Phenotypes"), + ("Genotypes", "Genotypes")] + Cursor.execute("""select distinct Tissue.Name, concat(Tissue.Name, ' mRNA') from + ProbeFreeze, + ProbeSetFreeze, InbredSet, Tissue where ProbeFreeze.TissueId = Tissue.Id and + ProbeFreeze.InbredSetId = InbredSet.Id and + InbredSet.Name = %s and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and + ProbeSetFreeze.public > %s + order by Tissue.Name""", (group_name, webqtlConfig.PUBLICTHRESH)) + group += Cursor.fetchall() + return types + + +def get_datasets(types): + """Build datasets list""" + datasets = {} + for species, group_dict in types.iteritems(): + datasets[species] = {} + for group, type_list in group_dict.iteritems(): + datasets[species][group] = {} + for type_name, type_full_name in type_list: + dataset_text = dataset_value = None + if type_name == "Phenotypes": + dataset_value = "%sPublish" % group + if group == 'MDP': + dataset_text = "Mouse Phenome Database" else: - datasetTxt = "%s Published Phenotypes" % cross - datasetVal = "%sPublish" % cross - outputDatabaseStr += '{txt:\'%s\',val:\'%s\'},\n'% (datasetTxt,datasetVal) - datasetTotalResults.append(('%s'% datasetVal,'%s' % datasetTxt)) - - tIndex = getIndex(searchArray=typeTotalResults,targetValue=typeVal)+1 - dIndex = getIndex(searchArray=datasetTotalResults, targetValue=datasetVal)+1 - outputLinkStr +='[%d,%d,%d,%d],\n'%(sIndex,gIndex,tIndex,dIndex) - - # if group also exists in GenoFreeze table, then needs to add related Genotypes in database Array(dArr) - cursor.execute("select GenoFreeze.Id from GenoFreeze, InbredSet where GenoFreeze.InbredSetId = InbredSet.Id and InbredSet.Name = '%s'" % cross) - if (cursor.fetchall()): - typeVal = "Genotypes" - datasetTxt = "%s Genotypes" % cross - datasetVal = "%sGeno" % cross - outputDatabaseStr += '{txt:\'%s\',val:\'%s\'},\n'%(datasetTxt,datasetVal) - typeTotalResults.append(('Genotypes','Genotypes')) - datasetTotalResults.append(('%s'% datasetVal,'%s' % datasetTxt)) - - tIndex = getIndex(searchArray=typeTotalResults,targetValue=typeVal)+1 - dIndex = getIndex(searchArray=datasetTotalResults, targetValue=datasetVal)+1 - outputLinkStr +='[%d,%d,%d,%d],\n'%(sIndex,gIndex,tIndex,dIndex) - - # retrieve type(tissue) info based on group - # if cross is equal to 'BXD', then need to seach for 'BXD' and 'BXD300' InbredSet - if cross == "BXD": - cross2 = "BXD', 'BXD300" + dataset_text = "%s Published Phenotypes" % group + + elif type_name == "Genotypes": + dataset_value = "%sGeno" % group + dataset_text = "%s Genotypes" % group + + if dataset_value: + datasets[species][group][type_name] = [(dataset_value, dataset_text)] else: - cross2 = cross - cursor.execute("select distinct Tissue.Name, concat(Tissue.Name, ' mRNA') from ProbeFreeze, ProbeSetFreeze, InbredSet, Tissue where ProbeFreeze.TissueId = Tissue.Id and ProbeFreeze.InbredSetId = InbredSet.Id and InbredSet.Name in ('%s') and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeSetFreeze.public > %d order by Tissue.Name" % (cross2, webqtlConfig.PUBLICTHRESH)) - typeResults = cursor.fetchall() - typeResultsNum = cursor.rowcount - - if typeResultsNum > 0: - for typeItem in typeResults: - typeVal = typeItem[0] - tIndex = getIndex(searchArray=typeTotalResults, targetValue=typeVal)+1 - # retrieve database(dataset) info based on group(InbredSet) and type(Tissue) - tissue = typeVal - cursor.execute("select ProbeSetFreeze.Name, ProbeSetFreeze.FullName from ProbeSetFreeze, ProbeFreeze, InbredSet, Tissue where ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeFreeze.TissueId = Tissue.Id and ProbeFreeze.InbredSetId = InbredSet.Id and InbredSet.Name in ('%s') and Tissue.name = '%s' and ProbeSetFreeze.public > %d order by ProbeSetFreeze.CreateTime desc" % (cross2, tissue, webqtlConfig.PUBLICTHRESH)) - datasetResults = cursor.fetchall() - datasetResultsNum = cursor.rowcount - - if datasetResultsNum > 0: - for datasetItem in datasetResults: - datasetVal = datasetItem[0] - dIndex = getIndex(searchArray=datasetTotalResults, targetValue=datasetVal)+1 - outputLinkStr +='[%d,%d,%d,%d],\n'%(sIndex,gIndex,tIndex,dIndex) - -# add 'All Phenotypes' option for 'Database' select menu -# for 'All Species'option in 'Species' select menu, 'Database' select menu will show 'All Phenotypes' option -outputDatabaseStr += '{txt:\'%s\',val:\'%s\'}];\n\n'%('All Phenotypes','_allPublish') -datasetTotalResults.append(('_allPublish','All Phenotypes')) - -sIndex = getIndex(searchArray=speciesTotalResult,targetValue='All Species')+1 -gIndex = getIndex(searchArray=groupTotalResults, targetValue='all groups')+1 -tIndex = getIndex(searchArray=typeTotalResults,targetValue='Phenotypes')+1 -dIndex = getIndex(searchArray=datasetTotalResults, targetValue='_allPublish')+1 -outputLinkStr +='[%d,%d,%d,%d]];\n\n'%(sIndex,gIndex,tIndex,dIndex) - -# Combine sArr, gArr, tArr, dArr and lArr output string together -outputStr = outputTimeStr+outputSpeciesStr+outputGroupStr+outputTypeStr+outputDatabaseStr+outputLinkStr -outputStr +=''' - -/* -* function: based on different browser use, will have different initial actions; -* Once the index.html page is loaded, this function will be called -*/ -function initialDatasetSelection() -{ - defaultSpecies =getDefaultValue('species'); - defaultSet =getDefaultValue('cross'); - defaultType =getDefaultValue('tissue'); - defaultDB =getDefaultValue('database'); - - if (navigator.userAgent.indexOf('MSIE')>=0) - { - sOptions = fillOptionsForIE(null,defaultSpecies); - var menu0 =""; - document.getElementById('menu0').innerHTML = menu0; - - gOptions = fillOptionsForIE('species',defaultSet); - var menu1 =""; - document.getElementById('menu1').innerHTML =menu1; - - tOptions = fillOptionsForIE('cross',defaultType); - var menu2 =""; - document.getElementById('menu2').innerHTML =menu2; - - dOptions = fillOptionsForIE('tissue',defaultDB); - var menu3 =""; - document.getElementById('menu3').innerHTML =menu3; - - }else{ - fillOptions(null); - } - searchtip(); -} - -/* -* input: selectObjId (designated select menu, such as species, cross, etc... ) -* defaultValue (default Value of species, cross,tissue or database) -* function: special for IE browser,setting options value for select menu dynamically based on linkage array(lArr), -* output: options string -*/ -function fillOptionsForIE(selectObjId,defaultValue) -{ - var options=''; - if(selectObjId==null) - { - var len = sArr.length; - for (var i=1; i < len; i++) { - // setting Species' option - if( sArr[i].val==defaultValue){ - options =options+""; - }else{ - options =options+""; - } - } - }else if(selectObjId=='species') - { - var speciesObj = document.getElementById('species'); - var len = lArr.length; - var arr = []; - var idx = 0; - for (var i=1; i < len; i++) { - //get group(cross) info from lArr - if(lArr[i][0]==(getIndexByValue('species',speciesObj.value)).toString()&&!Contains(arr,lArr[i][1])) - { - arr[idx++]=lArr[i][1]; - } - } - idx=0; - len = arr.length; - removeOptions("cross"); - for (var i=0; i < len; i++) { - // setting Group's option - if( gArr[arr[i]].val==defaultValue){ - options =options+""; - }else{ - options =options+""; - } - - } - }else if(selectObjId=='cross') - { - var speciesObj = document.getElementById('species'); - var groupObj = document.getElementById('cross'); - var len = lArr.length; - var arr = []; - var idx = 0; - for (var i=1; i < len; i++) { - //get type(tissue) info from lArr - if(lArr[i][0]==(getIndexByValue('species',speciesObj.value)).toString()&&lArr[i][1]==(getIndexByValue('cross',groupObj.value)).toString()&&!Contains(arr,lArr[i][2])) - { - arr[idx++]=lArr[i][2]; - } - } - idx=0; - len = arr.length; - removeOptions("tissue"); - for (var i=0; i < len; i++) { - // setting Type's option - if( tArr[arr[i]].val==defaultValue){ - options =options+""; - }else{ - options =options+""; - } - } - - }else if(selectObjId=='tissue') - { - var speciesObj = document.getElementById('species'); - var groupObj = document.getElementById('cross'); - var typeObj = document.getElementById('tissue'); - - var len = lArr.length; - var arr = []; - var idx = 0; - for (var i=1; i < len; i++) { - //get dataset(database) info from lArr - if(lArr[i][0]==(getIndexByValue('species',speciesObj.value)).toString()&&lArr[i][1]==(getIndexByValue('cross',groupObj.value)).toString()&&lArr[i][2]==(getIndexByValue('tissue',typeObj.value)).toString()&&!Contains(arr,lArr[i][3])) - { - arr[idx++]=lArr[i][3]; - } - } - idx=0; - len = arr.length; - removeOptions("database"); - for (var i=0; i < len; i++) { - // setting Database's option - if( dArr[arr[i]].val==defaultValue){ - options =options+""; - }else{ - options =options+""; - } - } - } - return options; -} -/* -* input: selectObjId (designated select menu, such as species, cross, etc... ) -* function: setting options value for select menu dynamically based on linkage array(lArr) -* output: null -*/ -function fillOptions(selectObjId) -{ - if(selectObjId==null) - { - - var speciesObj = document.getElementById('species'); - var len = sArr.length; - for (var i=1; i < len; i++) { - // setting Species' option - speciesObj.options[i-1] = new Option(sArr[i].txt, sArr[i].val); - } - updateChocie('species'); - - }else if(selectObjId=='species') - { - var speciesObj = document.getElementById('species'); - var groupObj = document.getElementById('cross'); - var len = lArr.length; - var arr = []; - var idx = 0; - for (var i=1; i < len; i++) { - //get group(cross) info from lArr - if(lArr[i][0]==(getIndexByValue('species',speciesObj.value)).toString()&&!Contains(arr,lArr[i][1])) - { - arr[idx++]=lArr[i][1]; - } - } - idx=0; - len = arr.length; - removeOptions("cross"); - for (var i=0; i < len; i++) { - // setting Group's option - groupObj.options[idx++] = new Option(gArr[arr[i]].txt, gArr[arr[i]].val); - } - updateChocie('cross'); - - }else if(selectObjId=='cross') - { - var speciesObj = document.getElementById('species'); - var groupObj = document.getElementById('cross'); - var typeObj = document.getElementById('tissue'); - var len = lArr.length; - var arr = []; - var idx = 0; - for (var i=1; i < len; i++) { - //get type(tissue) info from lArr - if(lArr[i][0]==(getIndexByValue('species',speciesObj.value)).toString()&&lArr[i][1]==(getIndexByValue('cross',groupObj.value)).toString()&&!Contains(arr,lArr[i][2])) - { - arr[idx++]=lArr[i][2]; - } - } - idx=0; - len = arr.length; - removeOptions("tissue"); - for (var i=0; i < len; i++) { - // setting Type's option - typeObj.options[idx++] = new Option(tArr[arr[i]].txt, tArr[arr[i]].val); - } - updateChocie('tissue'); - - }else if(selectObjId=='tissue') - { - var speciesObj = document.getElementById('species'); - var groupObj = document.getElementById('cross'); - var typeObj = document.getElementById('tissue'); - var databaseObj = document.getElementById('database'); - - var len = lArr.length; - var arr = []; - var idx = 0; - for (var i=1; i < len; i++) { - //get dataset(database) info from lArr - if(lArr[i][0]==(getIndexByValue('species',speciesObj.value)).toString()&&lArr[i][1]==(getIndexByValue('cross',groupObj.value)).toString()&&lArr[i][2]==(getIndexByValue('tissue',typeObj.value)).toString()&&!Contains(arr,lArr[i][3])) - { - arr[idx++]=lArr[i][3]; - } - } - idx=0; - len = arr.length; - removeOptions("database"); - for (var i=0; i < len; i++) { - // setting Database's option - databaseObj.options[idx++] = new Option(dArr[arr[i]].txt, dArr[arr[i]].val); - } - updateChocie('database'); - } -} - -/* -* input: arr (targeted array); obj (targeted value) -* function: check whether targeted array contains targeted value or not -* output: return true, if array contains targeted value, otherwise return false -*/ -function Contains(arr,obj) { - var i = arr.length; - while (i--) { - if (arr[i] == obj) { - return true; - } - } - return false; -} - -/* -* input: selectObj (designated select menu, such as species, cross, etc... ) -* function: clear designated select menu's option -* output: null -*/ -function removeOptions(selectObj) { - if (typeof selectObj != 'object'){ - selectObj = document.getElementById(selectObj); - } - var len = selectObj.options.length; - for (var i=0; i < len; i++) { - // clear current selection - selectObj.options[0] = null; - } -} - -/* -* input: selectObjId (designated select menu, such as species, cross, etc... ) -* Value: target value -* function: retrieve Index info of target value in designated array -* output: index info -*/ -function getIndexByValue(selectObjId,val) -{ - if(selectObjId=='species') - { - for(var i=1;i=0){ - //setting option's selected status - Obj.options[idx].selected=true; - //update the following select menu - fillOptions(objId); - }else{ - Obj.options[0].selected=true; - fillOptions(objId); - } -} - -// setting option's selected status based on default setting or cookie setting for Species, Group, Type and Database select menu in the main search page http://www.genenetwork.org/ -function updateChocie(selectObjId){ - - if (selectObjId =='species') - { - defaultSpecies= getDefaultValue('species'); - //setting option's selected status - setChoice('species',defaultSpecies); - }else if (selectObjId =='cross') - { - defaultSet= getDefaultValue('cross'); - //setting option's selected status - setChoice('cross',defaultSet); - }else if (selectObjId =='tissue') - { - defaultType= getDefaultValue('tissue'); - //setting option's selected status - setChoice('tissue',defaultType); - }else if (selectObjId =='database') - { - defaultDB= getDefaultValue('database'); - //setting option's selected status - setChoice('database',defaultDB); - } -} - -//get default value;if cookie exists, then use cookie value, otherwise use default value -function getDefaultValue(selectObjId){ - //define default value - var defaultSpecies = 'mouse' - var defaultSet = 'BXD' - var defaultType = 'Hippocampus' - var defaultDB = 'HC_M2_0606_P' - - if (selectObjId =='species') - { - //if cookie exists, then use cookie value, otherwise use default value - var cookieSpecies = getCookie('defaultSpecies'); - if(cookieSpecies) - { - defaultSpecies= cookieSpecies; - } - return defaultSpecies; - }else if (selectObjId =='cross'){ - var cookieSet = getCookie('defaultSet'); - if(cookieSet){ - defaultSet= cookieSet; - } - return defaultSet; - }else if (selectObjId =='tissue'){ - var cookieType = getCookie('defaultType'); - if(cookieType){ - defaultType= cookieType; - } - return defaultType; - }else if (selectObjId =='database') - { - var cookieDB = getCookie('defaultDB'); - if(cookieDB){ - defaultDB= cookieDB; - } - return defaultDB; - } - -} - -//setting default value into cookies for the dropdown menus: Species,Group, Type, and Database -function setDefault(thisform){ - - setCookie('cookieTest', 'cookieTest', 1); - var cookieTest = getCookie('cookieTest'); - delCookie('cookieTest'); - if (cookieTest){ - var defaultSpecies = thisform.species.value; - setCookie('defaultSpecies', defaultSpecies, 10); - var defaultSet = thisform.cross.value; - setCookie('defaultSet', defaultSet, 10); - var defaultType = thisform.tissue.value; - setCookie('defaultType', defaultType, 10); - var defaultDB = thisform.database.value; - setCookie('defaultDB', defaultDB, 10); - updateChocie('species'); - updateChocie('cross'); - updateChocie('tissue'); - updateChocie('database'); - alert("The current settings are now your default"); - } - else{ - alert("You need to enable Cookies in your browser."); - } -} - -''' -# write all strings' info into selectDatasetMenu.js file -fileHandler.write(outputStr) -fileHandler.close() + Cursor.execute("""select ProbeSetFreeze.Name, ProbeSetFreeze.FullName from + ProbeSetFreeze, ProbeFreeze, InbredSet, Tissue where + ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and Tissue.Name = %s + and ProbeFreeze.TissueId = Tissue.Id and ProbeFreeze.InbredSetId = + InbredSet.Id and ProbeSetFreeze.public > %s order by + ProbeSetFreeze.CreateTime desc""", (type_name, + webqtlConfig.PUBLICTHRESH)) + datasets[species][group][type_name] = Cursor.fetchall() + + return datasets + + +def main(): + species = get_species() + groups = get_groups(species) + types = get_types(groups) + datasets = get_datasets(types) + + species.append(('All Species', 'All Species')) + groups['All Species'] = [('All Groups', 'All Groups')] + types['All Species'] = {} + types['All Species']['All Groups'] = [('Phenotypes', 'Phenotypes')] + datasets['All Species'] = {} + datasets['All Species']['All Groups'] = {} + datasets['All Species']['All Groups']['Phenotypes'] = [('All Phenotypes','All Phenotypes')] + + data = dict(species=species, + groups=groups, + types=types, + datasets=datasets, + ) + + output_file = """../wqflask/static/new/javascript/dataset_menu_structure.json""" + + with open(output_file, 'w') as fh: + json.dump(data, fh, indent=" ", sort_keys=True) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/wqflask/wqflask/static/new/javascript/dataset_menu_structure.json b/wqflask/wqflask/static/new/javascript/dataset_menu_structure.json new file mode 100644 index 00000000..d25d3cf5 --- /dev/null +++ b/wqflask/wqflask/static/new/javascript/dataset_menu_structure.json @@ -0,0 +1,6792 @@ +{ + "datasets": { + "All Species": { + "All Groups": { + "Phenotypes": [ + [ + "All Phenotypes", + "All Phenotypes" + ] + ] + } + }, + "arabidopsis": { + "BayXSha": { + "Genotypes": [ + [ + "BayXShaGeno", + "BayXSha Genotypes" + ] + ], + "Phenotypes": [ + [ + "BayXShaPublish", + "BayXSha Published Phenotypes" + ] + ] + }, + "ColXBur": { + "Genotypes": [ + [ + "ColXBurGeno", + "ColXBur Genotypes" + ] + ], + "Phenotypes": [ + [ + "ColXBurPublish", + "ColXBur Published Phenotypes" + ] + ] + }, + "ColXCvi": { + "Genotypes": [ + [ + "ColXCviGeno", + "ColXCvi Genotypes" + ] + ], + "Phenotypes": [ + [ + "ColXCviPublish", + "ColXCvi Published Phenotypes" + ] + ] + } + }, + "barley": { + "QSM": { + "Genotypes": [ + [ + "QSMGeno", + "QSM Genotypes" + ] + ], + "Leaf": [ + [ + "B1LI0809R", + "Barley1 Leaf INOC TTKS (Aug09) RMA" + ], + [ + "B1LI0809M5", + "Barley1 Leaf INOC TTKS (Aug09) MAS5" + ], + [ + "B1MI0809M5", + "Barley1 Leaf MOCK TTKS (Aug09) MAS5" + ], + [ + "B1MI0809R", + "Barley1 Leaf MOCK TTKS (Aug09) RMA" + ], + [ + "B30_K_1206_M", + "Barley1 Leaf MAS 5.0 SCRI (Dec06)" + ], + [ + "B30_K_1206_R", + "Barley1 Leaf gcRMA SCRI (Dec06)" + ], + [ + "B30_K_1206_Rn", + "Barley1 Leaf gcRMAn SCRI (Dec06)" + ] + ], + "Phenotypes": [ + [ + "QSMPublish", + "QSM Published Phenotypes" + ] + ] + }, + "SXM": { + "Embryo": [ + [ + "B139_K_1206_R", + "Barley1 Embryo gcRMA SCRI (Dec06)" + ], + [ + "B139_K_1206_M", + "Barley1 Embryo MAS 5.0 SCRI (Dec06)" + ], + [ + "B150_K_0406_R", + "Barley1 Embryo0 gcRMA SCRI (Apr06)" + ] + ], + "Genotypes": [ + [ + "SXMGeno", + "SXM Genotypes" + ] + ], + "Leaf": [ + [ + "B1LI0809R", + "Barley1 Leaf INOC TTKS (Aug09) RMA" + ], + [ + "B1LI0809M5", + "Barley1 Leaf INOC TTKS (Aug09) MAS5" + ], + [ + "B1MI0809M5", + "Barley1 Leaf MOCK TTKS (Aug09) MAS5" + ], + [ + "B1MI0809R", + "Barley1 Leaf MOCK TTKS (Aug09) RMA" + ], + [ + "B30_K_1206_M", + "Barley1 Leaf MAS 5.0 SCRI (Dec06)" + ], + [ + "B30_K_1206_R", + "Barley1 Leaf gcRMA SCRI (Dec06)" + ], + [ + "B30_K_1206_Rn", + "Barley1 Leaf gcRMAn SCRI (Dec06)" + ] + ], + "Phenotypes": [ + [ + "SXMPublish", + "SXM Published Phenotypes" + ] + ] + } + }, + "drosophila": { + "DGRP": { + "Genotypes": [ + [ + "DGRPGeno", + "DGRP Genotypes" + ] + ], + "Phenotypes": [ + [ + "DGRPPublish", + "DGRP Published Phenotypes" + ] + ], + "Whole Body": [ + [ + "NCSU_DrosWB_LC_RMA_0111", + "NCSU Drosophila Whole Body (Jan11) RMA" + ], + [ + "UAB_DrosWB_LC_RMA_1009", + "UAB Whole body D.m. mRNA control (Oct09) RMA" + ], + [ + "UAB_DrosWB_LE_RMA_1009", + "UAB Whole body D.m. mRNA lead (pbAc) (Oct09) RMA" + ] + ] + }, + "Oregon-R_x_2b3": { + "Genotypes": [ + [ + "Oregon-R_x_2b3Geno", + "Oregon-R_x_2b3 Genotypes" + ] + ], + "Phenotypes": [ + [ + "Oregon-R_x_2b3Publish", + "Oregon-R_x_2b3 Published Phenotypes" + ] + ], + "Whole Body": [ + [ + "NCSU_DrosWB_LC_RMA_0111", + "NCSU Drosophila Whole Body (Jan11) RMA" + ], + [ + "UAB_DrosWB_LC_RMA_1009", + "UAB Whole body D.m. mRNA control (Oct09) RMA" + ], + [ + "UAB_DrosWB_LE_RMA_1009", + "UAB Whole body D.m. mRNA lead (pbAc) (Oct09) RMA" + ] + ] + } + }, + "human": { + "AD-cases-controls": { + "Brain": [ + [ + "GSE15222_F_N_RI_0409", + "GSE15222 Human Brain Normal Myers (Apr09) RankInv" + ], + [ + "GSE15222_F_A_RI_0409", + "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA_N_0709", + "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" + ], + [ + "GSE5281_F_RMA_Alzh_0709", + "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" + ], + [ + "INIA_MacFas_brain_RMA_0110", + "INIA Macaca fasicularis Brain (Jan10) RMA **" + ], + [ + "GSE15222_F_RI_0409", + "GSE15222 Human Brain Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA0709", + "GSE5281 Human Brain Full Liang (Jul09) RMA" + ], + [ + "GSE5281_RMA0709", + "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_FEMALE", + "UCLA CTB6B6CTF2 Brain Female mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_MALE", + "UCLA CTB6B6CTF2 Brain Male mlratio **" + ], + [ + "UCLA_BHF2_BRAIN_MALE", + "UCLA BHF2 Brain Male mlratio" + ], + [ + "UCLA_BHF2_BRAIN_FEMALE", + "UCLA BHF2 Brain Female mlratio" + ], + [ + "UCLA_BHHBF2_BRAIN_FEMALE", + "UCLA BHHBF2 Brain Female Only" + ], + [ + "UCLA_BHHBF2_BRAIN_MALE", + "UCLA BHHBF2 Brain Male Only" + ], + [ + "UCLA_BHHBF2_BRAIN_2005", + "UCLA BHHBF2 Brain (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_2005", + "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" + ], + [ + "UCLA_BHF2_BRAIN_0605", + "UCLA BHF2 Brain (June05) mlratio" + ], + [ + "BR_M2_1106_R", + "UCHSC BXD Whole Brain M430 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"UCLA_BHHBF2_LIVER_FEMALE", + "UCLA BHHBF2 Liver Female Only" + ], + [ + "UCLA_BHHBF2_LIVER_MALE", + "UCLA BHHBF2 Liver Male Only" + ], + [ + "UCLA_BHF2_LIVER_FEMALE", + "UCLA BHF2 Liver Female mlratio" + ], + [ + "UCLA_BHHBF2_LIVER_2005", + "UCLA BHHBF2 Liver (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_2005", + "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" + ], + [ + "UCLA_BHF2_LIVER_0605", + "UCLA BHF2 Liver (June05) mlratio" + ], + [ + "UCLA_BDF2_LIVER_1999", + "UCLA BDF2 Liver (1999) mlratio" + ], + [ + "LVF2_M_0704_R", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" + ], + [ + "LVF2_M_0704_M", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" + ], + [ + "HLCF_0311", + "GSE9588 Human Liver Normal (Mar11) Females" + ] + ], + "Muscle": [ + [ + "EPFLMouseMuscleRMA1211", + "EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "EPFLMouseMuscleCDRMA1211", + "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + 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], + [ + "UCLA_CTB6B6CTF2_ADIPOSE_MALE", + "UCLA CTB6B6CTF2 Adipose Male mlratio **" + ], + [ + "UCLA_BHF2_ADIPOSE_FEMALE", + "UCLA BHF2 Adipose Female mlratio" + ], + [ + "UCLA_CTB6B6CTF2_ADIPOSE_FEMALE", + "UCLA CTB6B6CTF2 Adipose Female mlratio **" + ], + [ + "UCLA_BHHBF2_ADIPOSE_MALE", + "UCLA BHHBF2 Adipose Male Only" + ], + [ + "UCLA_BHHBF2_ADIPOSE_FEMALE", + "UCLA BHHBF2 Adipose Female Only" + ], + [ + "UCLA_BHHBF2_ADIPOSE_2005", + "UCLA BHHBF2 Adipose (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_ADIPOSE_2005", + "UCLA CTB6/B6CTF2 Adipose (2005) mlratio **" + ], + [ + "UCLA_BHF2_ADIPOSE_0605", + "UCLA BHF2 Adipose (June05) mlratio" + ] + ], + "Brain": [ + [ + "GSE15222_F_N_RI_0409", + "GSE15222 Human Brain Normal Myers (Apr09) RankInv" + ], + [ + "GSE15222_F_A_RI_0409", + "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA_N_0709", + "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" + ], + [ + "GSE5281_F_RMA_Alzh_0709", + "GSE5281 Human Brain 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"UCLA_BHF2_BRAIN_0605", + "UCLA BHF2 Brain (June05) mlratio" + ], + [ + "BR_M2_1106_R", + "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" + ], + [ + "IBR_M_0606_R", + "INIA Brain mRNA M430 (Jun06) RMA" + ], + [ + "IBR_M_0106_P", + "INIA Brain mRNA M430 (Jan06) PDNN" + ], + [ + "IBR_M_0106_R", + "INIA Brain mRNA M430 (Jan06) RMA" + ], + [ + "BR_U_1105_P", + "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" + ], + [ + "BR_U_0805_M", + "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" + ], + [ + "BR_U_0805_P", + "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" + ], + [ + "BR_U_0805_R", + "UTHSC Brain mRNA U74Av2 (Aug05) RMA" + ], + [ + "BRF2_M_0805_R", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" + ], + [ + "BRF2_M_0805_P", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" + ], + [ + "BRF2_M_0805_M", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" + ], + [ + "BRF2_M_0304_P", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" + ], + [ + "BRF2_M_0304_R", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" + ], + [ + "BRF2_M_0304_M", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" + ], + [ + "CB_M_0204_P", + "INIA Brain mRNA M430 (Feb04) PDNN" + ] + ], + "Genotypes": [ + [ + "BHHBF2Geno", + "BHHBF2 Genotypes" + ] + ], + "Liver": [ + [ + "GSE16780_UCLA_ML0911", + "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" + ], + [ + "JAX_CSB_L_0711", + "JAX Liver Affy M430 2.0 (Jul11) MDP" + ], + [ + "JAX_CSB_L_HF_0711", + "JAX Liver HF Affy M430 2.0 (Jul11) MDP" + ], + [ + "JAX_CSB_L_6C_0711", + "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" + ], + [ + "HLC_0311", + "GSE9588 Human Liver Normal (Mar11) Both Sexes" + ], + [ + "HLCM_0311", + "GSE9588 Human Liver Normal (Mar11) Males" + ], + [ + "LV_G_0106_F", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" + ], + [ + "LV_G_0106_M", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" + ], + [ + "LV_G_0106_B", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" + ], + [ + "GenEx_BXD_liverSal_RMA_F_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" + ], + [ + "GenEx_BXD_liverSal_RMA_M_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" + ], + [ + "GenEx_BXD_liverSal_RMA_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" + ], + [ + "GenEx_BXD_liverEt_RMA_F_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" + ], + [ + "GenEx_BXD_liverEt_RMA_M_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" + ], + [ + "GenEx_BXD_liverEt_RMA_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" + ], + [ + "SUH_Liv_RMA_0611", + "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" + ], + [ + "OXUKHS_ILMLiver_RI0510", + "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" + ], + [ + "HXB_Liver_1208", + "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_MALE", + "UCLA CTB6B6CTF2 Liver Male mlratio **" + ], + [ + "UCLA_BHF2_LIVER_MALE", + "UCLA BHF2 Liver Male mlratio" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_FEMALE", + "UCLA CTB6B6CTF2 Liver Female mlratio **" + ], + [ + "UCLA_BHHBF2_LIVER_FEMALE", + "UCLA BHHBF2 Liver Female Only" + ], + [ + "UCLA_BHHBF2_LIVER_MALE", + "UCLA BHHBF2 Liver Male Only" + ], + [ + "UCLA_BHF2_LIVER_FEMALE", + "UCLA BHF2 Liver Female mlratio" + ], + [ + "UCLA_BHHBF2_LIVER_2005", + "UCLA BHHBF2 Liver (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_2005", + "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" + ], + [ + "UCLA_BHF2_LIVER_0605", + "UCLA BHF2 Liver (June05) mlratio" + ], + [ + "UCLA_BDF2_LIVER_1999", + "UCLA BDF2 Liver (1999) mlratio" + ], + [ + "LVF2_M_0704_R", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" + ], + [ + "LVF2_M_0704_M", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" + ], + [ + "HLCF_0311", + "GSE9588 Human Liver Normal (Mar11) Females" + ] + ], + "Muscle": [ + [ + "EPFLMouseMuscleRMA1211", + "EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "EPFLMouseMuscleCDRMA1211", + "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "EPFLMouseMuscleHFDRMA1211", + "EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_FEMALE", + "UCLA CTB6B6CTF2 Muscle Female mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_MALE", + "UCLA CTB6B6CTF2 Muscle Male mlratio **" + ], + [ + "UCLA_BHHBF2_MUSCLE_FEMALE", + "UCLA BHHBF2 Muscle Female Only" + ], + [ + "UCLA_BHHBF2_MUSCLE_MALE", + "UCLA BHHBF2 Muscle Male Only" + ], + [ + "UCLA_BHF2_MUSCLE_MALE", + "UCLA BHF2 Muscle Male mlratio **" + ], + [ + "UCLA_BHF2_MUSCLE_FEMALE", + "UCLA BHF2 Muscle Female mlratio **" + ], + [ + "UCLA_BHHBF2_MUSCLE_2005", + "UCLA BHHBF2 Muscle (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_2005", + "UCLA CTB6/B6CTF2 Muscle (2005) mlratio **" + ], + [ + "UCLA_BHF2_MUSCLE_0605", + "UCLA BHF2 Muscle (June05) mlratio **" + ] + ], + "Phenotypes": [ + [ + "BHHBF2Publish", + "BHHBF2 Published Phenotypes" + ] + ] + }, + "BXD": { + "Amygdala": [ + [ + "KIN_YSM_AMY_0711", + "KIN/YSM Human AMY Affy Hu-Exon 1.0 ST (Jul11) Quantile **" + ], + [ + "INIA_AmgCoh_0311", + "INIA Amygdala Cohort Affy MoGene 1.0 ST (Mar11) RMA" + ], + [ + "INIA_Amg_BLA_RMA_1110", + "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA" + ], + [ + "INIA_Amg_BLA_RMA_M_1110", + "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Male" + ], + [ + "INIA_Amg_BLA_RMA_F_1110", + "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Female" + ], + [ + "INIA_MacFas_AMGc_RMA_0110", + "INIA Macaca fasicularis Amygdala control (Jan10) RMA **" + ], + [ + "INIA_MacFas_AMGe_RMA_0110", + "INIA Macaca fasicularis Amygdala ethanol (Jan10) RMA **" + ] + ], + "Brain": [ + [ + "GSE15222_F_N_RI_0409", + "GSE15222 Human Brain Normal Myers (Apr09) RankInv" + ], + [ + "GSE15222_F_A_RI_0409", + "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA_N_0709", + "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" + ], + [ + "GSE5281_F_RMA_Alzh_0709", + "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" + ], + [ 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+ "SRxSHRSPF2Publish", + "SRxSHRSPF2 Published Phenotypes" + ] + ] + } + }, + "soybean": { + "J12XJ58F2": { + "Genotypes": [ + [ + "J12XJ58F2Geno", + "J12XJ58F2 Genotypes" + ] + ], + "Phenotypes": [ + [ + "J12XJ58F2Publish", + "J12XJ58F2 Published Phenotypes" + ] + ] + } + }, + "tomato": { + "LXP": { + "Genotypes": [ + [ + "LXPGeno", + "LXP Genotypes" + ] + ], + "Phenotypes": [ + [ + "LXPPublish", + "LXP Published Phenotypes" + ] + ] + } + } + }, + "groups": { + "All Species": [ + [ + "All Groups", + "All Groups" + ] + ], + "arabidopsis": [ + [ + "BayXSha", + "BayXSha" + ], + [ + "ColXBur", + "ColXBur" + ], + [ + "ColXCvi", + "ColXCvi" + ] + ], + "barley": [ + [ + "QSM", + "QSM" + ], + [ + "SXM", + "SXM" + ] + ], + "drosophila": [ + [ + "DGRP", + "Drosophila Genetic Reference Panel" + ], + [ + "Oregon-R_x_2b3", + "Oregon-R x 2b3" + ] + ], + "human": [ + [ + "AD-cases-controls", + "AD Cases & Controls (Liang)" + ], + [ + "AD-cases-controls-Myers", + "AD Cases & Controls (Myers)" + ], + 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"Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Leaf", + "Leaf mRNA" + ] + ], + "SXM": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Embryo", + "Embryo mRNA" + ], + [ + "Leaf", + "Leaf mRNA" + ] + ] + }, + "drosophila": { + "DGRP": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Whole Body", + "Whole Body mRNA" + ] + ], + "Oregon-R_x_2b3": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Whole Body", + "Whole Body mRNA" + ] + ] + }, + "human": { + "AD-cases-controls": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Brain", + "Brain mRNA" + ] + ], + "AD-cases-controls-Myers": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Brain", + "Brain mRNA" + ] + ], + "CANDLE": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Newborn Cord Blood", + "Newborn Cord Blood mRNA" + ] + ], + "CEPH-2004": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Lymphoblast B-cell", + "Lymphoblast B-cell mRNA" + ] + ], + "HB": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Cerebellum", + "Cerebellum mRNA" + ], + [ + "Prefrontal Cortex", + "Prefrontal Cortex mRNA" + ], + [ + "Primary Visual Cortex", + "Primary Visual Cortex mRNA" + ] + ], + "HLC": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Liver", + "Liver mRNA" + ] + ], + "HSB": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Amygdala", + "Amygdala mRNA" + ], + [ + "Caudal Ganglionic Eminence", + "Caudal Ganglionic Eminence mRNA" + ], + [ + "Cerebellar Cortex", + "Cerebellar Cortex mRNA" + ], + [ + "Diencephalon", + "Diencephalon mRNA" + ], + [ + "Dorsal Thalamus", + "Dorsal Thalamus mRNA" + ], + [ + "Dorsolateral Prefrontal Cortex", + "Dorsolateral Prefrontal Cortex mRNA" + ], + [ + "Frontal Cerebral Wall", + "Frontal Cerebral Wall mRNA" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Inferior Temporal Cortex", + "Inferior Temporal Cortex mRNA" + ], + [ + "Lateral Ganglionic Eminence", + "Lateral Ganglionic Eminence mRNA" + ], + [ + "Medial Ganglionic Eminence", + "Medial Ganglionic Eminence mRNA" + ], + [ + "Medial Prefrontal Cortex", + "Medial Prefrontal Cortex mRNA" + ], + [ + "Mediodorsal Nucleus of Thalamus", + "Mediodorsal Nucleus of Thalamus mRNA" + ], + [ + "Occipital Cerebral Wall", + "Occipital Cerebral Wall mRNA" + ], + [ + "Orbital Prefrontal Cortex", + "Orbital Prefrontal Cortex mRNA" + ], + [ + "Parietal Cerebral Wall", + "Parietal Cerebral Wall mRNA" + ], + [ + "Posterior Inferior Parietal Cortex", + "Posterior Inferior Parietal Cortex mRNA" + ], + [ + "Posterior Superior Temporal Cortex", + "Posterior Superior Temporal Cortex mRNA" + ], + [ + "Primary Auditory (A1) Cortex", + "Primary Auditory (A1) Cortex mRNA" + ], + [ + "Primary Motor (M1) Cortex", + "Primary Motor (M1) Cortex mRNA" + ], + [ + "Primary Somatosensory (S1) Cortex", + "Primary Somatosensory (S1) Cortex mRNA" + ], + [ + "Primary Visual Cortex", + "Primary Visual Cortex mRNA" + ], + [ + "Striatum", + "Striatum mRNA" + ], + [ + "Temporal Cerebral Wall", + "Temporal Cerebral Wall mRNA" + ], + [ + "Upper (Rostral) Rhombic Lip", + "Upper (Rostral) Rhombic Lip mRNA" + ], + [ + "Ventral Forebrain", + "Ventral Forebrain mRNA" + ], + [ + "Ventrolateral Prefrontal Cortex", + "Ventrolateral Prefrontal Cortex mRNA" + ] + ] + }, + "macaque monkey": { + "Macaca-fasicularis": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Amygdala", + "Amygdala mRNA" + ], + [ + "Brain", + "Brain mRNA" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Nucleus Accumbens", + "Nucleus Accumbens mRNA" + ], + [ + "Prefrontal Cortex", + "Prefrontal Cortex mRNA" + ] + ] + }, + "mouse": { + "AKXD": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Mammary Tumors", + "Mammary Tumors mRNA" + ] + ], + "AXBXA": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Eye", + "Eye mRNA" + ] + ], + "B6BTBRF2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Liver", + "Liver mRNA" + ] + ], + "B6D2F2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Brain", + "Brain mRNA" + ] + ], + "BDF2-1999": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Liver", + "Liver mRNA" + ] + ], + "BDF2-2005": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Striatum", + "Striatum mRNA" + ] + ], + "BHF2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Adipose", + "Adipose mRNA" + ], + [ + "Brain", + "Brain mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Muscle", + "Muscle mRNA" + ] + ], + "BHHBF2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Adipose", + "Adipose mRNA" + ], + [ + "Brain", + "Brain mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Muscle", + "Muscle mRNA" + ] + ], + "BXD": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Amygdala", + "Amygdala mRNA" + ], + [ + "Brain", + "Brain mRNA" + ], + [ + "Cartilage", + "Cartilage mRNA" + ], + [ + "Cerebellum", + "Cerebellum mRNA" + ], + [ + "Eye", + "Eye mRNA" + ], + [ + "Hematopoietic Cells", + "Hematopoietic Cells mRNA" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Hypothalamus", + "Hypothalamus mRNA" + ], + [ + "Kidney", + "Kidney mRNA" + ], + [ + "Leucocytes", + "Leucocytes mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Lung", + "Lung mRNA" + ], + [ + "Midbrain", + "Midbrain mRNA" + ], + [ + "Muscle", + "Muscle mRNA" + ], + [ + "Neocortex", + "Neocortex mRNA" + ], + [ + "Nucleus Accumbens", + "Nucleus Accumbens mRNA" + ], + [ + "Prefrontal Cortex", + "Prefrontal Cortex mRNA" + ], + [ + "Retina", + "Retina mRNA" + ], + [ + "Spleen", + "Spleen mRNA" + ], + [ + "Striatum", + "Striatum mRNA" + ], + [ + "T Cell (helper)", + "T Cell (helper) mRNA" + ], + [ + "T Cell (regulatory)", + "T Cell (regulatory) mRNA" + ], + [ + "Thymus", + "Thymus mRNA" + ], + [ + "Ventral Tegmental Area", + "Ventral Tegmental Area mRNA" + ] + ], + "BXH": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Cartilage", + "Cartilage mRNA" + ] + ], + "CTB6F2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Adipose", + "Adipose mRNA" + ], + [ + "Brain", + "Brain mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Muscle", + "Muscle mRNA" + ] + ], + "CXB": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Spleen", + "Spleen mRNA" + ] + ], + "HS": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Lung", + "Lung mRNA" + ] + ], + "HS-CC": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Striatum", + "Striatum mRNA" + ] + ], + "LXS": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Prefrontal Cortex", + "Prefrontal Cortex mRNA" + ] + ], + "MDP": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Liver", + "Liver mRNA" + ] + ], + "NZBXFVB-N2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Mammary Tumors", + "Mammary Tumors mRNA" + ] + ] + }, + "rat": { + "HXBBXH": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Adrenal Gland", + "Adrenal Gland mRNA" + ], + [ + "Heart", + "Heart mRNA" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Kidney", + "Kidney mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Peritoneal Fat", + "Peritoneal Fat mRNA" + ] + ], + "SRxSHRSPF2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Eye", + "Eye mRNA" + ] + ] + }, + "soybean": { + "J12XJ58F2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ] + ] + }, + "tomato": { + "LXP": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ] + ] + } + } +} \ No newline at end of file -- cgit v1.2.3 From 3c57b46a3c746f0bce58de9a70aca55d1df20002 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Tue, 23 Oct 2012 17:06:01 -0500 Subject: Finished coffeescript that handles the home page dataset selection dropdowns --- misc/notes.txt | 2 + wqflask/base/webqtlCaseData.py | 4 +- .../static/new/javascript/dataset_menu_structure | 6792 ++++++++++++++++++++ .../new/javascript/dataset_menu_structure.json | 6792 -------------------- .../new/javascript/dataset_select_menu.coffee | 834 +-- .../static/new/javascript/dataset_select_menu.js | 408 +- .../static/new/javascript/show_trait.coffee | 8 +- .../wqflask/static/new/javascript/show_trait.js | 9 +- wqflask/wqflask/templates/new_index_page.html | 24 +- 9 files changed, 7310 insertions(+), 7563 deletions(-) create mode 100644 wqflask/wqflask/static/new/javascript/dataset_menu_structure delete mode 100644 wqflask/wqflask/static/new/javascript/dataset_menu_structure.json (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index c12cd2bb..e7b94087 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -36,6 +36,8 @@ screen -r zas1024/ Start up log: Go to /tmp and tail -f flask_gn_log +=========================================== + Coffeescript Stuff: coffee -c (filename) diff --git a/wqflask/base/webqtlCaseData.py b/wqflask/base/webqtlCaseData.py index 6352a083..42763aed 100755 --- a/wqflask/base/webqtlCaseData.py +++ b/wqflask/base/webqtlCaseData.py @@ -27,9 +27,7 @@ print("Mr. Mojo Risin 2") class webqtlCaseData(object): - """one case data in one trait - - """ + """one case data in one trait""" def __init__(self, name, value=None, variance=None, num_cases=None): self.name = name diff --git a/wqflask/wqflask/static/new/javascript/dataset_menu_structure b/wqflask/wqflask/static/new/javascript/dataset_menu_structure new file mode 100644 index 00000000..d25d3cf5 --- /dev/null +++ b/wqflask/wqflask/static/new/javascript/dataset_menu_structure @@ -0,0 +1,6792 @@ +{ + "datasets": { + "All Species": { + "All Groups": { + "Phenotypes": [ + [ + "All Phenotypes", + "All Phenotypes" + ] + ] + } + }, + "arabidopsis": { + "BayXSha": { + "Genotypes": [ + [ + "BayXShaGeno", + "BayXSha Genotypes" + ] + ], + "Phenotypes": [ + [ + "BayXShaPublish", + "BayXSha Published Phenotypes" + ] + ] + }, + "ColXBur": { + "Genotypes": [ + [ + "ColXBurGeno", + "ColXBur Genotypes" + ] + ], + "Phenotypes": [ + [ + "ColXBurPublish", + "ColXBur Published Phenotypes" + ] + ] + }, + "ColXCvi": { + "Genotypes": [ + [ + "ColXCviGeno", + "ColXCvi Genotypes" + ] + ], + "Phenotypes": [ + [ + "ColXCviPublish", + "ColXCvi Published Phenotypes" + ] + ] + } + }, + "barley": { + "QSM": { + "Genotypes": [ + [ + "QSMGeno", + "QSM Genotypes" + ] + ], + "Leaf": [ + [ + "B1LI0809R", + "Barley1 Leaf INOC TTKS (Aug09) RMA" + ], + [ + "B1LI0809M5", + "Barley1 Leaf INOC TTKS (Aug09) MAS5" + ], + [ + "B1MI0809M5", + "Barley1 Leaf MOCK TTKS (Aug09) MAS5" + ], + [ + "B1MI0809R", + "Barley1 Leaf MOCK TTKS (Aug09) RMA" + ], + [ + "B30_K_1206_M", + "Barley1 Leaf MAS 5.0 SCRI (Dec06)" + ], + [ + "B30_K_1206_R", + "Barley1 Leaf gcRMA SCRI (Dec06)" + ], + [ + "B30_K_1206_Rn", + "Barley1 Leaf gcRMAn SCRI (Dec06)" + ] + ], + "Phenotypes": [ + [ + "QSMPublish", + "QSM Published Phenotypes" + ] + ] + }, + "SXM": { + "Embryo": [ + [ + "B139_K_1206_R", + "Barley1 Embryo gcRMA SCRI (Dec06)" + ], + [ + "B139_K_1206_M", + "Barley1 Embryo MAS 5.0 SCRI (Dec06)" + ], + [ + "B150_K_0406_R", + "Barley1 Embryo0 gcRMA SCRI (Apr06)" + ] + ], + "Genotypes": [ + [ + "SXMGeno", + "SXM Genotypes" + ] + ], + "Leaf": [ + [ + "B1LI0809R", + "Barley1 Leaf INOC TTKS (Aug09) RMA" + ], + [ + "B1LI0809M5", + "Barley1 Leaf INOC TTKS (Aug09) MAS5" + ], + [ + "B1MI0809M5", + "Barley1 Leaf MOCK TTKS (Aug09) MAS5" + ], + [ + "B1MI0809R", + "Barley1 Leaf MOCK TTKS (Aug09) RMA" + ], + [ + "B30_K_1206_M", + "Barley1 Leaf MAS 5.0 SCRI (Dec06)" + ], + [ + "B30_K_1206_R", + "Barley1 Leaf gcRMA SCRI (Dec06)" + ], + [ + "B30_K_1206_Rn", + "Barley1 Leaf gcRMAn SCRI (Dec06)" + ] + ], + "Phenotypes": [ + [ + "SXMPublish", + "SXM Published Phenotypes" + ] + ] + } + }, + "drosophila": { + "DGRP": { + "Genotypes": [ + [ + "DGRPGeno", + "DGRP Genotypes" + ] + ], + "Phenotypes": [ + [ + "DGRPPublish", + "DGRP Published Phenotypes" + ] + ], + "Whole Body": [ + [ + "NCSU_DrosWB_LC_RMA_0111", + "NCSU Drosophila Whole Body (Jan11) RMA" + ], + [ + "UAB_DrosWB_LC_RMA_1009", + "UAB Whole body D.m. mRNA control (Oct09) RMA" + ], + [ + "UAB_DrosWB_LE_RMA_1009", + "UAB Whole body D.m. mRNA lead (pbAc) (Oct09) RMA" + ] + ] + }, + "Oregon-R_x_2b3": { + "Genotypes": [ + [ + "Oregon-R_x_2b3Geno", + "Oregon-R_x_2b3 Genotypes" + ] + ], + "Phenotypes": [ + [ + "Oregon-R_x_2b3Publish", + "Oregon-R_x_2b3 Published Phenotypes" + ] + ], + "Whole Body": [ + [ + "NCSU_DrosWB_LC_RMA_0111", + "NCSU Drosophila Whole Body (Jan11) RMA" + ], + [ + "UAB_DrosWB_LC_RMA_1009", + "UAB Whole body D.m. mRNA control (Oct09) RMA" + ], + [ + "UAB_DrosWB_LE_RMA_1009", + "UAB Whole body D.m. mRNA lead (pbAc) (Oct09) RMA" + ] + ] + } + }, + "human": { + "AD-cases-controls": { + "Brain": [ + [ + "GSE15222_F_N_RI_0409", + "GSE15222 Human Brain Normal Myers (Apr09) RankInv" + ], + [ + "GSE15222_F_A_RI_0409", + "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA_N_0709", + "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" + ], + [ + "GSE5281_F_RMA_Alzh_0709", + "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" + ], + [ + "INIA_MacFas_brain_RMA_0110", + "INIA Macaca fasicularis Brain (Jan10) RMA **" + ], + [ + "GSE15222_F_RI_0409", + "GSE15222 Human Brain Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA0709", + "GSE5281 Human Brain Full Liang (Jul09) RMA" + ], + [ + "GSE5281_RMA0709", + "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_FEMALE", + "UCLA CTB6B6CTF2 Brain Female mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_MALE", + "UCLA CTB6B6CTF2 Brain Male mlratio **" + ], + [ + "UCLA_BHF2_BRAIN_MALE", + "UCLA BHF2 Brain Male mlratio" + ], + [ + "UCLA_BHF2_BRAIN_FEMALE", + "UCLA BHF2 Brain Female mlratio" + ], + [ + "UCLA_BHHBF2_BRAIN_FEMALE", + "UCLA BHHBF2 Brain Female Only" + ], + [ + "UCLA_BHHBF2_BRAIN_MALE", + "UCLA BHHBF2 Brain Male Only" + ], + [ + "UCLA_BHHBF2_BRAIN_2005", + "UCLA BHHBF2 Brain (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_2005", + "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" + ], + [ + "UCLA_BHF2_BRAIN_0605", + "UCLA BHF2 Brain (June05) mlratio" + ], + [ + "BR_M2_1106_R", + "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" + ], + [ + "IBR_M_0606_R", + "INIA Brain mRNA M430 (Jun06) RMA" + ], + [ + "IBR_M_0106_P", + "INIA Brain mRNA M430 (Jan06) PDNN" + ], + [ + "IBR_M_0106_R", + "INIA Brain mRNA M430 (Jan06) RMA" + ], + [ + "BR_U_1105_P", + "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" + ], + [ + "BR_U_0805_M", + "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" + ], + [ + "BR_U_0805_P", + "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" + ], + [ + "BR_U_0805_R", + "UTHSC Brain mRNA U74Av2 (Aug05) RMA" + ], + [ + "BRF2_M_0805_R", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" + ], + [ + "BRF2_M_0805_P", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" + ], + [ + "BRF2_M_0805_M", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" + ], + [ + "BRF2_M_0304_P", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" + ], + [ + "BRF2_M_0304_R", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" + ], + [ + "BRF2_M_0304_M", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" + ], + [ + "CB_M_0204_P", + "INIA Brain mRNA M430 (Feb04) PDNN" + ] + ], + "Genotypes": [ + [ + "AD-cases-controlsGeno", + "AD-cases-controls Genotypes" + ] + ], + "Phenotypes": [ + [ + "AD-cases-controlsPublish", + "AD-cases-controls Published Phenotypes" + ] + ] + }, + "AD-cases-controls-Myers": { + "Brain": [ + [ + "GSE15222_F_N_RI_0409", + "GSE15222 Human Brain Normal Myers (Apr09) RankInv" + ], + [ + "GSE15222_F_A_RI_0409", + "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA_N_0709", + "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" + ], + [ + "GSE5281_F_RMA_Alzh_0709", + "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" + ], + [ + "INIA_MacFas_brain_RMA_0110", + "INIA Macaca fasicularis Brain (Jan10) RMA **" + ], + [ + "GSE15222_F_RI_0409", + "GSE15222 Human Brain Myers (Apr09) RankInv" + ], + [ + "GSE5281_F_RMA0709", + "GSE5281 Human Brain Full Liang (Jul09) RMA" + ], + [ + "GSE5281_RMA0709", + "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_FEMALE", + "UCLA CTB6B6CTF2 Brain Female mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_MALE", + "UCLA CTB6B6CTF2 Brain Male mlratio **" + ], + [ + "UCLA_BHF2_BRAIN_MALE", + "UCLA BHF2 Brain Male mlratio" + ], + [ + "UCLA_BHF2_BRAIN_FEMALE", + "UCLA BHF2 Brain Female mlratio" + ], + [ + "UCLA_BHHBF2_BRAIN_FEMALE", + "UCLA BHHBF2 Brain Female Only" + ], + [ + "UCLA_BHHBF2_BRAIN_MALE", + "UCLA BHHBF2 Brain Male Only" + ], + [ + "UCLA_BHHBF2_BRAIN_2005", + "UCLA BHHBF2 Brain (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_BRAIN_2005", + "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" + ], + [ + "UCLA_BHF2_BRAIN_0605", + "UCLA BHF2 Brain (June05) mlratio" + ], + [ + "BR_M2_1106_R", + "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" + ], + [ + "IBR_M_0606_R", + "INIA Brain mRNA M430 (Jun06) RMA" + ], + [ + "IBR_M_0106_P", + "INIA Brain mRNA M430 (Jan06) PDNN" + ], + [ + "IBR_M_0106_R", + "INIA Brain mRNA M430 (Jan06) RMA" + ], + [ + "BR_U_1105_P", + "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" + ], + [ + "BR_U_0805_M", + "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" + ], + [ + "BR_U_0805_P", + "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" + ], + [ + "BR_U_0805_R", + "UTHSC Brain mRNA U74Av2 (Aug05) RMA" + ], + [ + "BRF2_M_0805_R", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" + ], + [ + "BRF2_M_0805_P", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" + ], + [ + "BRF2_M_0805_M", + "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" + ], + [ + "BRF2_M_0304_P", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" + ], + [ + "BRF2_M_0304_R", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" + ], + [ + "BRF2_M_0304_M", + "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" + ], + [ + "CB_M_0204_P", + "INIA Brain mRNA M430 (Feb04) PDNN" + ] + ], + "Genotypes": [ + [ + "AD-cases-controls-MyersGeno", + "AD-cases-controls-Myers Genotypes" + ] + ], + "Phenotypes": [ + [ + "AD-cases-controls-MyersPublish", + "AD-cases-controls-Myers Published Phenotypes" + ] + ] + }, + "CANDLE": { + "Genotypes": [ + [ + "CANDLEGeno", + "CANDLE Genotypes" + ] + ], + "Newborn Cord Blood": [ + [ + "CANDLE_NB_0711", + "CANDLE Newborn Cord ILMv6.3 (Jun11) QUANT **" + ] + ], + "Phenotypes": [ + [ + "CANDLEPublish", + "CANDLE Published Phenotypes" + ] + ] + }, + "CEPH-2004": { + "Genotypes": [ + [ + "CEPH-2004Geno", + "CEPH-2004 Genotypes" + ] + ], + "Lymphoblast B-cell": [ + [ + "UT_CEPH_RankInv0909", + "UTHSC CEPH B-cells Illumina (Sep09) RankInv" + ], + [ + "Human_1008", + "Monks CEPH B-cells Agilent (Dec04) Log10Ratio" + ] + ], + "Phenotypes": [ + [ + "CEPH-2004Publish", + "CEPH-2004 Published Phenotypes" + ] + ] + }, + "HB": { + "Cerebellum": [ + [ + "HBTRC-MLC_0611", + "HBTRC-MLC Human Cerebellum Agilent (Jun11) mlratio" + ], + [ + "HBTRC-MLC_N_0611", + "HBTRC-MLC Human Cerebellum Agilent Normal (Jun11) mlratio" + ], + [ + "HBTRC-MLC_AD_0611", + "HBTRC-MLC Human Cerebellum Agilent AD (Jun11) mlratio" + ], + [ + "HBTRC-MLC_HD_0611", + "HBTRC-MLC Human Cerebellum Agilent HD (Jun11) mlratio" + ], + [ + "GCB_M2_0505_M", + "GE-NIAAA Cerebellum mRNA M430v2 (May05) MAS5" + ], + [ + "GCB_M2_0505_R", + "GE-NIAAA Cerebellum mRNA M430v2 (May05) RMA" + ], + [ + "GCB_M2_0505_P", + "GE-NIAAA Cerebellum mRNA M430v2 (May05) PDNN" + ], + [ + "CB_M_0305_R", + "SJUT Cerebellum mRNA M430 (Mar05) RMA" + ], + [ + "CB_M_0305_M", + "SJUT Cerebellum mRNA M430 (Mar05) MAS5" + ], + [ + "CB_M_0305_P", + "SJUT Cerebellum mRNA M430 (Mar05) PDNN" + ], + [ + "CB_M_1004_R", + "SJUT Cerebellum mRNA M430 (Oct04) RMA" + ], + [ + "CB_M_1004_M", + "SJUT Cerebellum mRNA M430 (Oct04) MAS5" + ], + [ + "CB_M_1004_P", + "SJUT Cerebellum mRNA M430 (Oct04) PDNN" + ], + [ + "CB_M_1003_M", + "SJUT Cerebellum mRNA M430 (Oct03) MAS5" + ] + ], + "Genotypes": [ + [ + "HBGeno", + "HB Genotypes" + ] + ], + "Phenotypes": [ + [ + "HBPublish", + "HB Published Phenotypes" + ] + ], + "Prefrontal Cortex": [ + [ + "HBTRC-MLPFC_0611", + "HBTRC-MLC Human Prefrontal Cortex Agilent (Jun11) mlratio" + ], + [ + "HBTRC-MLPFC_N_0611", + "HBTRC-MLC Human Prefrontal Cortex Agilent Normal (Jun11) mlratio" + ], + [ + "HBTRC-MLPFC_AD_0611", + "HBTRC-MLC Human Prefrontal Cortex Agilent AD (Jun11) mlratio" + ], + [ + "HBTRC-MLPFC_HD_0611", + "HBTRC-MLC Human Prefrontal Cortex Agilent HD (Jun11) mlratio" + ], + [ + "INIA_MacFas_Pf_RMA_0110", + "INIA Macaca fasicularis Prefrontal Cortex control (Jan10) RMA **" + ], + [ + "INIA_MacFas_PfE_RMA_0110", + "INIA Macaca fasicularis Prefrontal Cortex ethanol (Jan10) RMA **" + ], + [ + "VCUSal_1006_R", + "VCU BXD PFC Et vs Sal M430 2.0 (Dec06) Sscore" + ], + [ + "VCUEtOH_1206_R", + "VCU BXD PFC EtOH M430 2.0 (Dec06) RMA" + ], + [ + "VCUSal_1206_R", + "VCU BXD PFC Sal M430 2.0 (Dec06) RMA" + ], + [ + "VCU_PF_Air_0111_R", + "VCU BXD PFC CIE Air M430 2.0 (Jan11) RMA **" + ], + [ + "VCU_PF_Et_0111_R", + "VCU BXD PFC CIE EtOH M430 2.0 (Jan11) RMA **" + ], + [ + "VCU_PF_AvE_0111_Ss", + "VCU BXD PFC EtOH vs CIE Air M430 2.0 (Jan11) Sscore **" + ], + [ + "VCUEt_vs_Sal_0806_R", + "VCU LXS PFC Et vs Sal M430A 2.0 (Aug06) Sscore **" + ], + [ + "VCUEtOH_0806_R", + "VCU LXS PFC EtOH M430A 2.0 (Aug06) RMA **" + ], + [ + "VCUSal_0806_R", + "VCU LXS PFC Sal M430A 2.0 (Aug06) RMA" + ] + ], + "Primary Visual Cortex": [ + [ + "KIN_YSM_V1C_0711", + "KIN/YSM Human V1C Affy Hu-Exon 1.0 ST (Jul11) Quantile **" + ], + [ + "HBTRC-MLVC_0611", + "HBTRC-MLC Human Visual Cortex Agilent (Jun11) mlratio" + ], + [ + "HBTRC-MLVC_N_0611", + "HBTRC-MLC Human Visual Cortex Agilent Normal (Jun11) mlratio" + ], + [ + "HBTRC-MLVC_AD_0611", + "HBTRC-MLC Human Visual Cortex Agilent AD (Jun11) mlratio" + ], + [ + "HBTRC-MLVC_HD_0611", + "HBTRC-MLC Human Visual Cortex Agilent HD (Jun11) mlratio" + ] + ] + }, + "HLC": { + "Genotypes": [ + [ + "HLCGeno", + "HLC Genotypes" + ] + ], + "Liver": [ + [ + "GSE16780_UCLA_ML0911", + "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" + ], + [ + "JAX_CSB_L_0711", + "JAX Liver Affy M430 2.0 (Jul11) MDP" + ], + [ + "JAX_CSB_L_HF_0711", + "JAX Liver HF Affy M430 2.0 (Jul11) MDP" + ], + [ + "JAX_CSB_L_6C_0711", + "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" + ], + [ + "HLC_0311", + "GSE9588 Human Liver Normal (Mar11) Both Sexes" + ], + [ + "HLCM_0311", + "GSE9588 Human Liver Normal (Mar11) Males" + ], + [ + "LV_G_0106_F", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" + ], + [ + "LV_G_0106_M", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" + ], + [ + "LV_G_0106_B", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" + ], + [ + "GenEx_BXD_liverSal_RMA_F_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" + ], + [ + "GenEx_BXD_liverSal_RMA_M_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" + ], + [ + "GenEx_BXD_liverSal_RMA_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" + ], + [ + "GenEx_BXD_liverEt_RMA_F_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" + ], + [ + "GenEx_BXD_liverEt_RMA_M_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" + ], + [ + "GenEx_BXD_liverEt_RMA_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" + ], + [ + "SUH_Liv_RMA_0611", + "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" + ], + [ + "OXUKHS_ILMLiver_RI0510", + "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" + ], + [ + "HXB_Liver_1208", + "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_MALE", + "UCLA CTB6B6CTF2 Liver Male mlratio **" + ], + [ + "UCLA_BHF2_LIVER_MALE", + "UCLA BHF2 Liver Male mlratio" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_FEMALE", + "UCLA CTB6B6CTF2 Liver Female mlratio **" + ], + [ + "UCLA_BHHBF2_LIVER_FEMALE", + "UCLA BHHBF2 Liver Female Only" + ], + [ + "UCLA_BHHBF2_LIVER_MALE", + "UCLA BHHBF2 Liver Male Only" + ], + [ + "UCLA_BHF2_LIVER_FEMALE", + "UCLA BHF2 Liver Female mlratio" + ], + [ + "UCLA_BHHBF2_LIVER_2005", + "UCLA BHHBF2 Liver (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_2005", + "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" + ], + [ + "UCLA_BHF2_LIVER_0605", + "UCLA BHF2 Liver (June05) mlratio" + ], + [ + "UCLA_BDF2_LIVER_1999", + "UCLA BDF2 Liver (1999) mlratio" + ], + [ + "LVF2_M_0704_R", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" + ], + [ + "LVF2_M_0704_M", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" + ], + [ + "HLCF_0311", + "GSE9588 Human Liver Normal (Mar11) Females" + ] + ], + "Phenotypes": [ + [ + "HLCPublish", + "HLC Published Phenotypes" + ] + ] + }, + "HSB": { + "Amygdala": [ + [ + "KIN_YSM_AMY_0711", + "KIN/YSM Human AMY Affy Hu-Exon 1.0 ST (Jul11) Quantile **" + ], + [ + "INIA_AmgCoh_0311", + "INIA Amygdala Cohort Affy MoGene 1.0 ST (Mar11) RMA" + ], + [ + "INIA_Amg_BLA_RMA_1110", + "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA" + ], + [ + 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"MDC/CAS/UCL Liver 230v2 (Dec08) RMA" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_MALE", + "UCLA CTB6B6CTF2 Liver Male mlratio **" + ], + [ + "UCLA_BHF2_LIVER_MALE", + "UCLA BHF2 Liver Male mlratio" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_FEMALE", + "UCLA CTB6B6CTF2 Liver Female mlratio **" + ], + [ + "UCLA_BHHBF2_LIVER_FEMALE", + "UCLA BHHBF2 Liver Female Only" + ], + [ + "UCLA_BHHBF2_LIVER_MALE", + "UCLA BHHBF2 Liver Male Only" + ], + [ + "UCLA_BHF2_LIVER_FEMALE", + "UCLA BHF2 Liver Female mlratio" + ], + [ + "UCLA_BHHBF2_LIVER_2005", + "UCLA BHHBF2 Liver (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_2005", + "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" + ], + [ + "UCLA_BHF2_LIVER_0605", + "UCLA BHF2 Liver (June05) mlratio" + ], + [ + "UCLA_BDF2_LIVER_1999", + "UCLA BDF2 Liver (1999) mlratio" + ], + [ + "LVF2_M_0704_R", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" + ], + [ + "LVF2_M_0704_M", + "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" + ], + [ + "HLCF_0311", + "GSE9588 Human Liver Normal (Mar11) Females" + ] + ], + "Muscle": [ + [ + "EPFLMouseMuscleRMA1211", + "EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "EPFLMouseMuscleCDRMA1211", + "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "EPFLMouseMuscleHFDRMA1211", + "EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_FEMALE", + "UCLA CTB6B6CTF2 Muscle Female mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_MALE", + "UCLA CTB6B6CTF2 Muscle Male mlratio **" + ], + [ + "UCLA_BHHBF2_MUSCLE_FEMALE", + "UCLA BHHBF2 Muscle Female Only" + ], + [ + "UCLA_BHHBF2_MUSCLE_MALE", + "UCLA BHHBF2 Muscle Male Only" + ], + [ + "UCLA_BHF2_MUSCLE_MALE", + "UCLA BHF2 Muscle Male mlratio **" + ], + [ + "UCLA_BHF2_MUSCLE_FEMALE", + "UCLA BHF2 Muscle Female mlratio **" + ], + [ + "UCLA_BHHBF2_MUSCLE_2005", + "UCLA BHHBF2 Muscle (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_2005", + "UCLA CTB6/B6CTF2 Muscle (2005) mlratio **" + ], + [ + "UCLA_BHF2_MUSCLE_0605", + "UCLA BHF2 Muscle (June05) mlratio **" + ] + ], + "Phenotypes": [ + [ + "BHF2Publish", + "BHF2 Published Phenotypes" + ] + ] + }, + "BHHBF2": { + "Adipose": [ + [ + "UCLA_BHF2_ADIPOSE_MALE", + "UCLA BHF2 Adipose Male mlratio" + ], + [ + "UCLA_CTB6B6CTF2_ADIPOSE_MALE", + "UCLA CTB6B6CTF2 Adipose Male mlratio **" + ], + [ + "UCLA_BHF2_ADIPOSE_FEMALE", + "UCLA BHF2 Adipose Female mlratio" + ], + [ + "UCLA_CTB6B6CTF2_ADIPOSE_FEMALE", + "UCLA CTB6B6CTF2 Adipose Female mlratio **" + ], + [ + "UCLA_BHHBF2_ADIPOSE_MALE", + "UCLA BHHBF2 Adipose Male Only" + ], + [ + "UCLA_BHHBF2_ADIPOSE_FEMALE", + "UCLA BHHBF2 Adipose Female Only" + ], + [ + "UCLA_BHHBF2_ADIPOSE_2005", + "UCLA BHHBF2 Adipose (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_ADIPOSE_2005", + "UCLA CTB6/B6CTF2 Adipose (2005) mlratio **" + ], + [ + "UCLA_BHF2_ADIPOSE_0605", + "UCLA BHF2 Adipose (June05) mlratio" + ] + ], + "Brain": [ + [ + "GSE15222_F_N_RI_0409", + "GSE15222 Human Brain Normal 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(2005) mlratio **" + ], + [ + "UCLA_BHF2_MUSCLE_0605", + "UCLA BHF2 Muscle (June05) mlratio **" + ] + ], + "Phenotypes": [ + [ + "BHHBF2Publish", + "BHHBF2 Published Phenotypes" + ] + ] + }, + "BXD": { + "Amygdala": [ + [ + "KIN_YSM_AMY_0711", + "KIN/YSM Human AMY Affy Hu-Exon 1.0 ST (Jul11) Quantile **" + ], + [ + "INIA_AmgCoh_0311", + "INIA Amygdala Cohort Affy MoGene 1.0 ST (Mar11) RMA" + ], + [ + "INIA_Amg_BLA_RMA_1110", + "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA" + ], + [ + "INIA_Amg_BLA_RMA_M_1110", + "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Male" + ], + [ + "INIA_Amg_BLA_RMA_F_1110", + "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Female" + ], + [ + "INIA_MacFas_AMGc_RMA_0110", + "INIA Macaca fasicularis Amygdala control (Jan10) RMA **" + ], + [ + "INIA_MacFas_AMGe_RMA_0110", + "INIA Macaca fasicularis Amygdala ethanol (Jan10) RMA **" + ] + ], + "Brain": [ + [ + "GSE15222_F_N_RI_0409", + "GSE15222 Human Brain Normal Myers (Apr09) RankInv" + ], + [ + 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"HBTRC-MLC_AD_0611", + "HBTRC-MLC Human Cerebellum Agilent AD (Jun11) mlratio" + ], + [ + "HBTRC-MLC_HD_0611", + "HBTRC-MLC Human Cerebellum Agilent HD (Jun11) mlratio" + ], + [ + "GCB_M2_0505_M", + "GE-NIAAA Cerebellum mRNA M430v2 (May05) MAS5" + ], + [ + "GCB_M2_0505_R", + "GE-NIAAA Cerebellum mRNA M430v2 (May05) RMA" + ], + [ + "GCB_M2_0505_P", + "GE-NIAAA Cerebellum mRNA M430v2 (May05) PDNN" + ], + [ + "CB_M_0305_R", + "SJUT Cerebellum mRNA M430 (Mar05) RMA" + ], + [ + "CB_M_0305_M", + "SJUT Cerebellum mRNA M430 (Mar05) MAS5" + ], + [ + "CB_M_0305_P", + "SJUT Cerebellum mRNA M430 (Mar05) PDNN" + ], + [ + "CB_M_1004_R", + "SJUT Cerebellum mRNA M430 (Oct04) RMA" + ], + [ + "CB_M_1004_M", + "SJUT Cerebellum mRNA M430 (Oct04) MAS5" + ], + [ + "CB_M_1004_P", + "SJUT Cerebellum mRNA M430 (Oct04) PDNN" + ], + [ + "CB_M_1003_M", + "SJUT Cerebellum mRNA M430 (Oct03) MAS5" + ] + ], + "Eye": [ + [ + "Eye_AXBXA_1008_RankInv", + "Eye AXBXA Illumina V6.2(Oct08) RankInv Beta" + ], + [ + "Eye_M2_0908_R", + "Eye M430v2 (Sep08) RMA" + ], + [ + "Eye_M2_0908_R_NB", + "Eye M430v2 Mutant Gpnmb (Sep08) RMA **" + ], + [ + "Eye_M2_0908_R_ND", + "Eye M430v2 WT Gpnmb (Sep08) RMA **" + ], + [ + "Eye_M2_0908_WTWT", + "Eye M430v2 WT WT (Sep08) RMA **" + ], + [ + "Eye_M2_0908_R_WT", + "Eye M430v2 WT Tyrp1 (Sep08) RMA **" + ], + [ + "Eye_M2_0908_R_MT", + "Eye M430v2 Mutant Tyrp1 (Sep08) RMA **" + ], + [ + "BXD_GLA_0911", + "BXD Glaucoma Affy M430 2.0 Trial (Sep11) RMA **" + ], + [ + "UIOWA_Eye_RMA_0906", + "UIOWA Eye mRNA RAE230v2 (Sep06) RMA" + ] + ], + "Genotypes": [ + [ + "BXDGeno", + "BXD Genotypes" + ] + ], + "Hematopoietic Cells": [ + [ + "UMCG_0907_HemaStem_ori", + "UMCG Stem Cells ILM6v1.1 (Apr09) original" + ], + [ + "UMCG_0907_HemaStem", + "UMCG Stem Cells ILM6v1.1 (Apr09) transformed" + ], + [ + "UMCG_0907_Pro_ori", + "UMCG Progenitor Cells ILM6v1.1 (Apr09) original" + ], + [ + "UMCG_0907_Pro", + "UMCG Progenitor Cells ILM6v1.1 (Apr09) transformed" + ], + [ + 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"UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" + ], + [ + "Illum_LXS_Hipp_loess0807", + "Hippocampus Illumina (Aug07) LOESS" + ], + [ + "Illum_LXS_Hipp_loess_nb0807", + "Hippocampus Illumina (Aug07) LOESS_NB" + ], + [ + "Illum_LXS_Hipp_quant0807", + "Hippocampus Illumina (Aug07) QUANT" + ], + [ + "Illum_LXS_Hipp_quant_nb0807", + "Hippocampus Illumina (Aug07) QUANT_NB" + ], + [ + "Illum_LXS_Hipp_rsn0807", + "Hippocampus Illumina (Aug07) RSN" + ], + [ + "Illum_LXS_Hipp_rsn_nb0807", + "Hippocampus Illumina (Aug07) RSN_NB" + ], + [ + "Hipp_Illumina_RankInv_0507", + "Hippocampus Illumina (May07) RankInv" + ], + [ + "HC_M2_0606_P", + "Hippocampus Consortium M430v2 (Jun06) PDNN" + ], + [ + "HC_M2_0606_M", + "Hippocampus Consortium M430v2 (Jun06) MAS5" + ], + [ + "HC_M2_0606_R", + "Hippocampus Consortium M430v2 (Jun06) RMA" + ], + [ + "HC_M2CB_1205_R", + "Hippocampus Consortium M430v2 CXB (Dec05) RMA" + ], + [ + "HC_M2CB_1205_P", + "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" + ], + [ + "UMUTAffyExon_0209_RMA", + "UMUTAffy Hippocampus Exon (Feb09) RMA" + ], + [ + "UT_ILM_BXD_hipp_NON_0909", + "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" + ], + [ + "UT_ILM_BXD_hipp_NOS_0909", + "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" + ], + [ + "UT_ILM_BXD_hipp_NOE_0909", + "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" + ], + [ + "UT_ILM_BXD_hipp_RSS_0909", + "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" + ], + [ + "UT_ILM_BXD_hipp_RSE_0909", + "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" + ], + [ + "Illum_LXS_Hipp_RSE_1008", + "Hippocampus Illumina RSE (Oct08) RankInv beta" + ], + [ + "Illum_LXS_Hipp_NOS_1008", + "Hippocampus Illumina NOS (Oct08) RankInv beta" + ], + [ + "Illum_LXS_Hipp_NOE_1008", + "Hippocampus Illumina NOE (Oct08) RankInv beta" + ], + [ + "Illum_LXS_Hipp_RSS_1008", + "Hippocampus Illumina RSS (Oct08) RankInv beta" + ], + [ + "Illum_LXS_Hipp_NON_1008", + "Hippocampus Illumina NON (Oct08) RankInv beta" + ] + ], + "Hypothalamus": [ + [ + "INIA_Hyp_RMA_1110", + "INIA Hypothalamus Affy MoGene 1.0 ST (Nov10)" + ], + [ + "INIA_Hyp_M_RMA_1110", + "INIA Hypothalamus Affy MoGene 1.0 ST (Nov10) Male" + ], + [ + "INIA_Hyp_F_RMA_1110", + "INIA Hypothalamus Affy MoGene 1.0 ST (Nov10) Female" + ] + ], + "Kidney": [ + [ + "MA_M2F_0706_R", + "Mouse kidney M430v2 Female (Aug06) RMA" + ], + [ + "MA_M2M_0706_R", + "Mouse kidney M430v2 Male (Aug06) RMA" + ], + [ + "MA_M2_0806_R", + "Mouse kidney M430v2 Sex Balanced (Aug06) RMA" + ], + [ + "MA_M2_0806_P", + "Mouse Kidney M430v2 Sex Balanced (Aug06) PDNN" + ], + [ + "MA_M2_0706_P", + "Mouse Kidney M430v2 (Jul06) PDNN" + ], + [ + "MA_M2_0706_R", + "Mouse Kidney M430v2 (Jul06) RMA" + ], + [ + "KI_2A_0405_M", + "MDC/CAS/ICL Kidney 230A (Apr05) MAS5" + ], + [ + "KI_2A_0405_Rz", + "MDC/CAS/ICL Kidney 230A (Apr05) RMA 2z+8" + ], + [ + "KI_2A_0405_R", + "MDC/CAS/ICL Kidney 230A (Apr05) RMA" + ] + ], + "Leucocytes": [ + [ + "Illum_BXD_PBL_1108", + "UWA Illumina PBL (Nov08) RSN **" + ] + ], + "Liver": [ + [ + "GSE16780_UCLA_ML0911", + "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" + ], + [ + "JAX_CSB_L_0711", + "JAX Liver Affy M430 2.0 (Jul11) MDP" + ], + [ + "JAX_CSB_L_HF_0711", + "JAX Liver HF Affy M430 2.0 (Jul11) MDP" + ], + [ + "JAX_CSB_L_6C_0711", + "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" + ], + [ + "HLC_0311", + "GSE9588 Human Liver Normal (Mar11) Both Sexes" + ], + [ + "HLCM_0311", + "GSE9588 Human Liver Normal (Mar11) Males" + ], + [ + "LV_G_0106_F", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" + ], + [ + "LV_G_0106_M", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" + ], + [ + "LV_G_0106_B", + "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" + ], + [ + "GenEx_BXD_liverSal_RMA_F_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" + ], + [ + "GenEx_BXD_liverSal_RMA_M_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" + ], + [ + "GenEx_BXD_liverSal_RMA_0211", + "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" + ], + [ + "GenEx_BXD_liverEt_RMA_F_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" + ], + [ + "GenEx_BXD_liverEt_RMA_M_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" + ], + [ + "GenEx_BXD_liverEt_RMA_0211", + "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" + ], + [ + "SUH_Liv_RMA_0611", + "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" + ], + [ + "OXUKHS_ILMLiver_RI0510", + "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" + ], + [ + "HXB_Liver_1208", + "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_MALE", + "UCLA CTB6B6CTF2 Liver Male mlratio **" + ], + [ + "UCLA_BHF2_LIVER_MALE", + "UCLA BHF2 Liver Male mlratio" + ], + [ + "UCLA_CTB6B6CTF2_LIVER_FEMALE", + "UCLA CTB6B6CTF2 Liver Female mlratio **" + ], + [ + "UCLA_BHHBF2_LIVER_FEMALE", + "UCLA BHHBF2 Liver Female Only" + ], + [ + "UCLA_BHHBF2_LIVER_MALE", + "UCLA BHHBF2 Liver Male Only" + ], 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"EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "EPFLMouseMuscleCDRMA1211", + "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "EPFLMouseMuscleHFDRMA1211", + "EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_FEMALE", + "UCLA CTB6B6CTF2 Muscle Female mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_MALE", + "UCLA CTB6B6CTF2 Muscle Male mlratio **" + ], + [ + "UCLA_BHHBF2_MUSCLE_FEMALE", + "UCLA BHHBF2 Muscle Female Only" + ], + [ + "UCLA_BHHBF2_MUSCLE_MALE", + "UCLA BHHBF2 Muscle Male Only" + ], + [ + "UCLA_BHF2_MUSCLE_MALE", + "UCLA BHF2 Muscle Male mlratio **" + ], + [ + "UCLA_BHF2_MUSCLE_FEMALE", + "UCLA BHF2 Muscle Female mlratio **" + ], + [ + "UCLA_BHHBF2_MUSCLE_2005", + "UCLA BHHBF2 Muscle (2005) mlratio **" + ], + [ + "UCLA_CTB6B6CTF2_MUSCLE_2005", + "UCLA CTB6/B6CTF2 Muscle (2005) mlratio **" + ], + [ + "UCLA_BHF2_MUSCLE_0605", + "UCLA BHF2 Muscle (June05) mlratio **" + ] + ], + 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+ "Liver", + "Liver mRNA" + ], + [ + "Muscle", + "Muscle mRNA" + ] + ], + "CXB": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Spleen", + "Spleen mRNA" + ] + ], + "HS": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Lung", + "Lung mRNA" + ] + ], + "HS-CC": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Striatum", + "Striatum mRNA" + ] + ], + "LXS": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Prefrontal Cortex", + "Prefrontal Cortex mRNA" + ] + ], + "MDP": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Liver", + "Liver mRNA" + ] + ], + "NZBXFVB-N2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Mammary Tumors", + "Mammary Tumors mRNA" + ] + ] + }, + "rat": { + "HXBBXH": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Adrenal Gland", + "Adrenal Gland mRNA" + ], + [ + "Heart", + "Heart mRNA" + ], + [ + "Hippocampus", + "Hippocampus mRNA" + ], + [ + "Kidney", + "Kidney mRNA" + ], + [ + "Liver", + "Liver mRNA" + ], + [ + "Peritoneal Fat", + "Peritoneal Fat mRNA" + ] + ], + "SRxSHRSPF2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ], + [ + "Eye", + "Eye mRNA" + ] + ] + }, + "soybean": { + "J12XJ58F2": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ] + ] + }, + "tomato": { + "LXP": [ + [ + "Phenotypes", + "Phenotypes" + ], + [ + "Genotypes", + "Genotypes" + ] + ] + } + } +} \ No newline at end of file diff --git a/wqflask/wqflask/static/new/javascript/dataset_menu_structure.json b/wqflask/wqflask/static/new/javascript/dataset_menu_structure.json deleted file mode 100644 index d25d3cf5..00000000 --- a/wqflask/wqflask/static/new/javascript/dataset_menu_structure.json +++ /dev/null @@ -1,6792 +0,0 @@ -{ - "datasets": { - "All Species": { - "All Groups": { - "Phenotypes": [ - [ - "All Phenotypes", - "All Phenotypes" - ] - ] - } - }, - "arabidopsis": { - "BayXSha": { - "Genotypes": [ - [ - "BayXShaGeno", - "BayXSha Genotypes" - ] - ], - "Phenotypes": [ - [ - "BayXShaPublish", - "BayXSha Published Phenotypes" - ] - ] - }, - "ColXBur": { - "Genotypes": [ - [ - "ColXBurGeno", - "ColXBur Genotypes" - ] - ], - "Phenotypes": [ - [ - "ColXBurPublish", - "ColXBur Published Phenotypes" - ] - ] - }, - "ColXCvi": { - "Genotypes": [ - [ - "ColXCviGeno", - "ColXCvi Genotypes" - ] - ], - "Phenotypes": [ - [ - "ColXCviPublish", - "ColXCvi Published Phenotypes" - ] - ] - } - }, - "barley": { - "QSM": { - "Genotypes": [ - [ - "QSMGeno", - "QSM Genotypes" - ] - ], - "Leaf": [ - [ - "B1LI0809R", - "Barley1 Leaf INOC TTKS (Aug09) RMA" - ], - [ - "B1LI0809M5", - "Barley1 Leaf INOC TTKS (Aug09) MAS5" - ], - [ - "B1MI0809M5", - "Barley1 Leaf MOCK TTKS (Aug09) MAS5" - ], - [ - "B1MI0809R", - "Barley1 Leaf MOCK TTKS (Aug09) RMA" - ], - [ - "B30_K_1206_M", - "Barley1 Leaf MAS 5.0 SCRI (Dec06)" - ], - [ - "B30_K_1206_R", - "Barley1 Leaf gcRMA SCRI (Dec06)" - ], - [ - "B30_K_1206_Rn", - "Barley1 Leaf gcRMAn SCRI (Dec06)" - ] - ], - "Phenotypes": [ - [ - "QSMPublish", - "QSM Published Phenotypes" - ] - ] - }, - "SXM": { - "Embryo": [ - [ - "B139_K_1206_R", - "Barley1 Embryo gcRMA SCRI (Dec06)" - ], - [ - "B139_K_1206_M", - "Barley1 Embryo MAS 5.0 SCRI (Dec06)" - ], - [ - "B150_K_0406_R", - "Barley1 Embryo0 gcRMA SCRI (Apr06)" - ] - ], - "Genotypes": [ - [ - "SXMGeno", - "SXM Genotypes" - ] - ], - "Leaf": [ - [ - "B1LI0809R", - "Barley1 Leaf INOC TTKS (Aug09) RMA" - ], - [ - "B1LI0809M5", - "Barley1 Leaf INOC TTKS (Aug09) MAS5" - ], - [ - "B1MI0809M5", - "Barley1 Leaf MOCK TTKS (Aug09) MAS5" - ], - [ - "B1MI0809R", - "Barley1 Leaf MOCK TTKS (Aug09) RMA" - ], - [ - "B30_K_1206_M", - "Barley1 Leaf MAS 5.0 SCRI (Dec06)" - ], - [ - "B30_K_1206_R", - "Barley1 Leaf gcRMA SCRI (Dec06)" - ], - [ - "B30_K_1206_Rn", - "Barley1 Leaf gcRMAn SCRI (Dec06)" - ] - ], - "Phenotypes": [ - [ - "SXMPublish", - "SXM Published Phenotypes" - ] - ] - } - }, - "drosophila": { - "DGRP": { - "Genotypes": [ - [ - "DGRPGeno", - "DGRP Genotypes" - ] - ], - "Phenotypes": [ - [ - "DGRPPublish", - "DGRP Published Phenotypes" - ] - ], - "Whole Body": [ - [ - "NCSU_DrosWB_LC_RMA_0111", - "NCSU Drosophila Whole Body (Jan11) RMA" - ], - [ - "UAB_DrosWB_LC_RMA_1009", - "UAB Whole body D.m. mRNA control (Oct09) RMA" - ], - [ - "UAB_DrosWB_LE_RMA_1009", - "UAB Whole body D.m. mRNA lead (pbAc) (Oct09) RMA" - ] - ] - }, - "Oregon-R_x_2b3": { - "Genotypes": [ - [ - "Oregon-R_x_2b3Geno", - "Oregon-R_x_2b3 Genotypes" - ] - ], - "Phenotypes": [ - [ - "Oregon-R_x_2b3Publish", - "Oregon-R_x_2b3 Published Phenotypes" - ] - ], - "Whole Body": [ - [ - "NCSU_DrosWB_LC_RMA_0111", - "NCSU Drosophila Whole Body (Jan11) RMA" - ], - [ - "UAB_DrosWB_LC_RMA_1009", - "UAB Whole body D.m. mRNA control (Oct09) RMA" - ], - [ - "UAB_DrosWB_LE_RMA_1009", - "UAB Whole body D.m. mRNA lead (pbAc) (Oct09) RMA" - ] - ] - } - }, - "human": { - "AD-cases-controls": { - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca fasicularis Brain (Jan10) RMA **" - ], - [ - "GSE15222_F_RI_0409", - "GSE15222 Human Brain Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA0709", - "GSE5281 Human Brain Full Liang (Jul09) RMA" - ], - [ - "GSE5281_RMA0709", - "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_FEMALE", - "UCLA CTB6B6CTF2 Brain Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_MALE", - "UCLA CTB6B6CTF2 Brain Male mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_MALE", - "UCLA BHF2 Brain Male mlratio" - ], - [ - "UCLA_BHF2_BRAIN_FEMALE", - "UCLA BHF2 Brain Female mlratio" - ], - [ - "UCLA_BHHBF2_BRAIN_FEMALE", - "UCLA BHHBF2 Brain Female Only" - ], - [ - "UCLA_BHHBF2_BRAIN_MALE", - "UCLA BHHBF2 Brain Male Only" - ], - [ - "UCLA_BHHBF2_BRAIN_2005", - "UCLA BHHBF2 Brain (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_2005", - "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_0605", - "UCLA BHF2 Brain (June05) mlratio" - ], - [ - "BR_M2_1106_R", - "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" - ], - [ - "IBR_M_0606_R", - "INIA Brain mRNA M430 (Jun06) RMA" - ], - [ - "IBR_M_0106_P", - "INIA Brain mRNA M430 (Jan06) PDNN" - ], - [ - "IBR_M_0106_R", - "INIA Brain 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Phenotypes" - ] - ] - }, - "AD-cases-controls-Myers": { - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca fasicularis Brain (Jan10) RMA **" - ], - [ - "GSE15222_F_RI_0409", - "GSE15222 Human Brain Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA0709", - "GSE5281 Human Brain Full Liang (Jul09) RMA" - ], - [ - "GSE5281_RMA0709", - "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_FEMALE", - "UCLA CTB6B6CTF2 Brain Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_MALE", - "UCLA CTB6B6CTF2 Brain Male mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_MALE", - "UCLA BHF2 Brain Male mlratio" - ], - [ - "UCLA_BHF2_BRAIN_FEMALE", - "UCLA BHF2 Brain Female mlratio" - ], - [ - "UCLA_BHHBF2_BRAIN_FEMALE", - "UCLA BHHBF2 Brain Female Only" - ], - [ - "UCLA_BHHBF2_BRAIN_MALE", - "UCLA BHHBF2 Brain Male Only" - ], - [ - "UCLA_BHHBF2_BRAIN_2005", - "UCLA BHHBF2 Brain (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_2005", - "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_0605", - "UCLA BHF2 Brain (June05) mlratio" - ], - [ - "BR_M2_1106_R", - "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" - ], - [ - "IBR_M_0606_R", - "INIA Brain mRNA M430 (Jun06) RMA" - ], - [ - "IBR_M_0106_P", - "INIA Brain mRNA M430 (Jan06) PDNN" - ], - [ - "IBR_M_0106_R", - "INIA Brain mRNA M430 (Jan06) RMA" - ], - [ - "BR_U_1105_P", - "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" - ], - [ - "BR_U_0805_M", - "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" - ], - [ - "BR_U_0805_P", - "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" - ], - [ - "BR_U_0805_R", - "UTHSC Brain mRNA U74Av2 (Aug05) RMA" - ], - [ - "BRF2_M_0805_R", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" - ], - [ - "BRF2_M_0805_P", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" - ], - [ - "BRF2_M_0805_M", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" - ], - [ - "BRF2_M_0304_P", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" - ], - [ - "BRF2_M_0304_R", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" - ], - [ - "BRF2_M_0304_M", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" - ], - [ - "CB_M_0204_P", - "INIA Brain mRNA M430 (Feb04) PDNN" - ] - ], - "Genotypes": [ - [ - "AD-cases-controls-MyersGeno", - "AD-cases-controls-Myers Genotypes" - ] - ], - "Phenotypes": [ - [ - "AD-cases-controls-MyersPublish", - "AD-cases-controls-Myers Published Phenotypes" - ] - ] - }, - "CANDLE": { - "Genotypes": [ - [ - "CANDLEGeno", - "CANDLE Genotypes" - ] - ], - "Newborn Cord Blood": [ - [ - "CANDLE_NB_0711", - "CANDLE Newborn Cord ILMv6.3 (Jun11) QUANT **" - ] - ], - "Phenotypes": [ - [ - "CANDLEPublish", - "CANDLE Published Phenotypes" - ] - ] - }, - "CEPH-2004": { - "Genotypes": [ - [ - "CEPH-2004Geno", - "CEPH-2004 Genotypes" - ] - ], - "Lymphoblast B-cell": [ - [ - "UT_CEPH_RankInv0909", - "UTHSC CEPH B-cells Illumina (Sep09) RankInv" - ], - [ - "Human_1008", - "Monks CEPH B-cells Agilent (Dec04) Log10Ratio" - ] - ], - "Phenotypes": [ - [ - "CEPH-2004Publish", - "CEPH-2004 Published Phenotypes" - ] - ] - }, - "HB": { - "Cerebellum": [ - [ - "HBTRC-MLC_0611", - "HBTRC-MLC Human Cerebellum Agilent (Jun11) mlratio" - ], - [ - "HBTRC-MLC_N_0611", - "HBTRC-MLC Human Cerebellum Agilent Normal (Jun11) mlratio" - ], - [ - "HBTRC-MLC_AD_0611", - "HBTRC-MLC Human Cerebellum Agilent AD (Jun11) mlratio" - ], - [ - "HBTRC-MLC_HD_0611", - "HBTRC-MLC Human Cerebellum Agilent HD (Jun11) mlratio" - ], - [ - "GCB_M2_0505_M", - "GE-NIAAA Cerebellum mRNA M430v2 (May05) MAS5" - ], - [ - "GCB_M2_0505_R", - "GE-NIAAA Cerebellum mRNA M430v2 (May05) RMA" - ], - [ - "GCB_M2_0505_P", - "GE-NIAAA Cerebellum mRNA M430v2 (May05) 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Prefrontal Cortex Agilent HD (Jun11) mlratio" - ], - [ - "INIA_MacFas_Pf_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex control (Jan10) RMA **" - ], - [ - "INIA_MacFas_PfE_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex ethanol (Jan10) RMA **" - ], - [ - "VCUSal_1006_R", - "VCU BXD PFC Et vs Sal M430 2.0 (Dec06) Sscore" - ], - [ - "VCUEtOH_1206_R", - "VCU BXD PFC EtOH M430 2.0 (Dec06) RMA" - ], - [ - "VCUSal_1206_R", - "VCU BXD PFC Sal M430 2.0 (Dec06) RMA" - ], - [ - "VCU_PF_Air_0111_R", - "VCU BXD PFC CIE Air M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_Et_0111_R", - "VCU BXD PFC CIE EtOH M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_AvE_0111_Ss", - "VCU BXD PFC EtOH vs CIE Air M430 2.0 (Jan11) Sscore **" - ], - [ - "VCUEt_vs_Sal_0806_R", - "VCU LXS PFC Et vs Sal M430A 2.0 (Aug06) Sscore **" - ], - [ - "VCUEtOH_0806_R", - "VCU LXS PFC EtOH M430A 2.0 (Aug06) RMA **" - ], - [ - "VCUSal_0806_R", - "VCU LXS PFC Sal M430A 2.0 (Aug06) RMA" - ] - ], - "Primary Visual Cortex": [ - [ - "KIN_YSM_V1C_0711", - "KIN/YSM Human V1C Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "HBTRC-MLVC_0611", - "HBTRC-MLC Human Visual Cortex Agilent (Jun11) mlratio" - ], - [ - "HBTRC-MLVC_N_0611", - "HBTRC-MLC Human Visual Cortex Agilent Normal (Jun11) mlratio" - ], - [ - "HBTRC-MLVC_AD_0611", - "HBTRC-MLC Human Visual Cortex Agilent AD (Jun11) mlratio" - ], - [ - "HBTRC-MLVC_HD_0611", - "HBTRC-MLC Human Visual Cortex Agilent HD (Jun11) mlratio" - ] - ] - }, - "HLC": { - "Genotypes": [ - [ - "HLCGeno", - "HLC Genotypes" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal 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- "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Phenotypes": [ - [ - "HLCPublish", - "HLC Published Phenotypes" - ] - ] - }, - "HSB": { - "Amygdala": [ - [ - "KIN_YSM_AMY_0711", - "KIN/YSM Human AMY Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "INIA_AmgCoh_0311", - "INIA Amygdala Cohort Affy MoGene 1.0 ST (Mar11) RMA" - ], - [ - "INIA_Amg_BLA_RMA_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA" - ], - [ - "INIA_Amg_BLA_RMA_M_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Male" - ], - [ - "INIA_Amg_BLA_RMA_F_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Female" - ], - [ - "INIA_MacFas_AMGc_RMA_0110", - "INIA Macaca fasicularis Amygdala control (Jan10) RMA **" - ], - [ - "INIA_MacFas_AMGe_RMA_0110", - "INIA Macaca fasicularis Amygdala ethanol (Jan10) RMA **" - ] - ], - "Caudal Ganglionic Eminence": [ - [ - "KIN_YSM_CGE_0711", - "KIN/YSM Human CGE Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Cerebellar Cortex": [ - [ - "KIN_YSM_CBC_0711", - "KIN/YSM Human CBC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Diencephalon": [ - [ - "KIN_YSM_DIE_0711", - "KIN/YSM Human DIE Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Dorsal Thalamus": [ - [ - "KIN_YSM_DTH_0711", - "KIN/YSM Human DTH Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Dorsolateral Prefrontal Cortex": [ - [ - "KIN_YSM_DFC_0711", - "KIN/YSM Human DFC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Frontal Cerebral Wall": [ - [ - "KIN_YSM_FC_0711", - "KIN/YSM Human FC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Genotypes": [ - [ - "HSBGeno", - "HSB Genotypes" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium M430v2 (Jun06) RMA MDP" - ], - [ - "OXUKHS_ILMHipp_RI0510", - "OX UK HS ILM6v1.1 Hippocampus (May 2010) RankInv" - ], - [ - "INIA_MacFas_Hc_RMA_0110", - "INIA Macaca fasicularis Hippocampus control (Jan10) RMA **" - ], - [ - "INIA_MacFas_He_RMA_0110", - "INIA Macaca fasicularis Hippocampus ethanol (Jan10) RMA **" - ], - [ - "UT_HippRatEx_RMA_0709", - "UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" - ], - [ - "Illum_LXS_Hipp_loess0807", - "Hippocampus Illumina (Aug07) LOESS" - ], - [ - "Illum_LXS_Hipp_loess_nb0807", - "Hippocampus Illumina (Aug07) LOESS_NB" - ], - [ - "Illum_LXS_Hipp_quant0807", - "Hippocampus Illumina (Aug07) QUANT" - ], - [ - "Illum_LXS_Hipp_quant_nb0807", - "Hippocampus Illumina (Aug07) QUANT_NB" - ], - [ - "Illum_LXS_Hipp_rsn0807", - "Hippocampus Illumina (Aug07) RSN" - ], - [ - "Illum_LXS_Hipp_rsn_nb0807", - "Hippocampus Illumina (Aug07) RSN_NB" - ], - [ - "Hipp_Illumina_RankInv_0507", - "Hippocampus Illumina (May07) RankInv" - ], - [ - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Inferior Temporal Cortex": [ - [ - "KIN_YSM_ITC_0711", - "KIN/YSM Human ITC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Lateral Ganglionic Eminence": [ - [ - "KIN_YSM_LGE_0711", - "KIN/YSM Human LGE Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Medial Ganglionic Eminence": [ - [ - "KIN_YSM_MGE_0711", - "KIN/YSM Human MGE Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Medial Prefrontal Cortex": [ - [ - "KIN_YSM_MFC_0711", - "KIN/YSM Human MFC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Mediodorsal Nucleus of Thalamus": [ - [ - "KIN_YSM_MD_0711", - "KIN/YSM Human MD Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Occipital Cerebral Wall": [ - [ - "KIN_YSM_OC_0711", - "KIN/YSM Human OC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Orbital Prefrontal Cortex": [ - [ - "KIN_YSM_OFC_0711", - "KIN/YSM Human OFC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Parietal Cerebral Wall": [ - [ - "KIN_YSM_PC_0711", - "KIN/YSM Human PC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Phenotypes": [ - [ - "HSBPublish", - "HSB Published Phenotypes" - ] - ], - "Posterior Inferior Parietal Cortex": [ - [ - "KIN_YSM_IPC_0711", - "KIN/YSM Human IPC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Posterior Superior Temporal Cortex": [ - [ - "KIN_YSM_STC_0711", - "KIN/YSM Human STC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Primary Auditory (A1) Cortex": [ - [ - "KIN_YSM_A1C_0711", - "KIN/YSM Human A1C Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Primary Motor (M1) Cortex": [ - [ - "KIN_YSM_M1C_0711", - "KIN/YSM Human M1C Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Primary Somatosensory (S1) Cortex": [ - [ - "KIN_YSM_S1C_0711", - "KIN/YSM Human S1C Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Primary Visual Cortex": [ - [ - "KIN_YSM_V1C_0711", - "KIN/YSM Human V1C Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "HBTRC-MLVC_0611", - "HBTRC-MLC Human Visual Cortex Agilent (Jun11) mlratio" - ], - [ - "HBTRC-MLVC_N_0611", - "HBTRC-MLC Human Visual Cortex Agilent Normal (Jun11) mlratio" - ], - [ - "HBTRC-MLVC_AD_0611", - "HBTRC-MLC Human Visual Cortex Agilent AD (Jun11) mlratio" - ], - [ - "HBTRC-MLVC_HD_0611", - "HBTRC-MLC Human Visual Cortex Agilent HD (Jun11) mlratio" - ] - ], - "Striatum": [ - [ - "DevStriatum_ILM6.2P3RInv_1111", - "BIDMC/UTHSC Dev Striatum P3 ILMv6.2 (Nov11) RankInv **" - ], - [ - "DevStriatum_ILM6.2P14RInv_1111", - "BIDMC/UTHSC Dev Striatum P14 ILMv6.2 (Nov11) RankInv **" - ], - [ - "KIN_YSM_STR_0711", - "KIN/YSM Human STR Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "OHSU_HS-CC_ILMStr_0211", - "OHSU HS-CC Striatum ILM6v1 (Feb11) RankInv" - ], - [ - "UTHSC_Striatum_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10v2) RankInv" - ], - [ - "UTHSC_Str_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10) RankInv" - ], - [ - "UTHSC_1107_RankInv", - "HQF BXD Striatum ILM6.1 (Nov07) RankInv" - ], - [ - "SA_M2_0905_P", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) PDNN" - ], - [ - "SA_M2_0905_M", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) MAS5" - ], - [ - "SA_M2_0905_R", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) RMA" - ], - [ - "SA_M2_0405_MC", - "HBP Rosen Striatum M430V2 (Apr05) MAS5 Clean" - ], - [ - "SA_M2_0405_RC", - "HBP Rosen Striatum M430V2 (Apr05) RMA Clean" - ], - [ - "SA_M2_0405_PC", - "HBP Rosen Striatum M430V2 (Apr05) PDNN Clean" - ], - [ - "SA_M2_0405_SS", - "HBP Rosen Striatum M430V2 (Apr05) SScore" - ], - [ - "SA_M2_0405_RR", - "HBP Rosen Striatum M430V2 (Apr05) RMA Orig" - ], - [ - "Striatum_Exon_0209", - "HQF Striatum Exon (Feb09) RMA" - ], - [ - "DevStriatum_ILM6.2P3RInv_1110", - "BIDMC/UTHSC Dev Striatum P3 ILMv6.2 (Nov10) RankInv **" - ], - [ - "DevStriatum_ILM6.2P14RInv_1110", - "BIDMC/UTHSC Dev Striatum P14 ILMv6.2 (Nov10) RankInv **" - ] - ], - "Temporal Cerebral Wall": [ - [ - "KIN_YSM_TC_0711", - "KIN/YSM Human TC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Upper (Rostral) Rhombic Lip": [ - [ - "KIN_YSM_URL_0711", - "KIN/YSM Human URL Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Ventral Forebrain": [ - [ - "KIN_YSM_VF_0711", - "KIN/YSM Human VF Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ], - "Ventrolateral Prefrontal Cortex": [ - [ - "KIN_YSM_VFC_0711", - "KIN/YSM Human VFC Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ] - ] - } - }, - "macaque monkey": { - "Macaca-fasicularis": { - "Amygdala": [ - [ - "KIN_YSM_AMY_0711", - "KIN/YSM Human AMY Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "INIA_AmgCoh_0311", - "INIA Amygdala Cohort Affy MoGene 1.0 ST (Mar11) RMA" - ], - [ - "INIA_Amg_BLA_RMA_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA" - ], - [ - "INIA_Amg_BLA_RMA_M_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Male" - ], - [ - "INIA_Amg_BLA_RMA_F_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Female" - ], - [ - "INIA_MacFas_AMGc_RMA_0110", - "INIA Macaca fasicularis Amygdala control (Jan10) RMA **" - ], - [ - "INIA_MacFas_AMGe_RMA_0110", - "INIA Macaca fasicularis Amygdala ethanol (Jan10) RMA **" - ] - ], - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca fasicularis Brain (Jan10) RMA **" - ], - [ - "GSE15222_F_RI_0409", - "GSE15222 Human Brain Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA0709", - "GSE5281 Human Brain Full Liang (Jul09) RMA" - ], - [ - "GSE5281_RMA0709", - "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_FEMALE", - "UCLA CTB6B6CTF2 Brain Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_MALE", - "UCLA CTB6B6CTF2 Brain Male mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_MALE", - "UCLA BHF2 Brain Male mlratio" - ], - [ - "UCLA_BHF2_BRAIN_FEMALE", - "UCLA BHF2 Brain Female mlratio" - ], - [ - "UCLA_BHHBF2_BRAIN_FEMALE", - "UCLA BHHBF2 Brain Female Only" - ], - [ - "UCLA_BHHBF2_BRAIN_MALE", - "UCLA BHHBF2 Brain Male Only" - ], - [ - "UCLA_BHHBF2_BRAIN_2005", - "UCLA BHHBF2 Brain (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_2005", - "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_0605", - "UCLA BHF2 Brain (June05) mlratio" - ], - [ - "BR_M2_1106_R", - "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" - ], - [ - "IBR_M_0606_R", - "INIA Brain mRNA M430 (Jun06) RMA" - ], - [ - "IBR_M_0106_P", - "INIA Brain mRNA M430 (Jan06) PDNN" - ], - [ - "IBR_M_0106_R", - "INIA Brain mRNA M430 (Jan06) RMA" - ], - [ - "BR_U_1105_P", - "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" - ], - [ - "BR_U_0805_M", - "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" - ], - [ - "BR_U_0805_P", - "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" - ], - [ - "BR_U_0805_R", - "UTHSC Brain mRNA U74Av2 (Aug05) RMA" - ], - [ - "BRF2_M_0805_R", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" - ], - [ - "BRF2_M_0805_P", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" - ], - [ - "BRF2_M_0805_M", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" - ], - [ - "BRF2_M_0304_P", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" - ], - [ - "BRF2_M_0304_R", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" - ], - [ - "BRF2_M_0304_M", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" - ], - [ - "CB_M_0204_P", - "INIA Brain mRNA M430 (Feb04) PDNN" - ] - ], - "Genotypes": [ - [ - "Macaca-fasicularisGeno", - "Macaca-fasicularis Genotypes" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium M430v2 (Jun06) RMA MDP" - ], - [ - "OXUKHS_ILMHipp_RI0510", - "OX UK HS ILM6v1.1 Hippocampus (May 2010) RankInv" - ], - [ - "INIA_MacFas_Hc_RMA_0110", - "INIA Macaca fasicularis Hippocampus control (Jan10) RMA **" - ], - [ - "INIA_MacFas_He_RMA_0110", - "INIA Macaca fasicularis Hippocampus ethanol (Jan10) RMA **" - ], - [ - "UT_HippRatEx_RMA_0709", - "UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" - ], - [ - "Illum_LXS_Hipp_loess0807", - "Hippocampus Illumina (Aug07) LOESS" - ], - [ - "Illum_LXS_Hipp_loess_nb0807", - "Hippocampus Illumina (Aug07) LOESS_NB" - ], - [ - "Illum_LXS_Hipp_quant0807", - "Hippocampus Illumina (Aug07) QUANT" - ], - [ - "Illum_LXS_Hipp_quant_nb0807", - "Hippocampus Illumina (Aug07) QUANT_NB" - ], - [ - "Illum_LXS_Hipp_rsn0807", - "Hippocampus Illumina (Aug07) RSN" - ], - [ - "Illum_LXS_Hipp_rsn_nb0807", - "Hippocampus Illumina (Aug07) RSN_NB" - ], - [ - "Hipp_Illumina_RankInv_0507", - "Hippocampus Illumina (May07) RankInv" - ], - [ - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Nucleus Accumbens": [ - [ - "INIA_MacFas_Ac_RMA_0110", - "INIA Macaca fasicularis Nucleus Accumbens control (Jan10) RMA **" - ], - [ - "INIA_MacFas_Ae_RMA_0110", - "INIA Macaca fasicularis Nucleus Accumbens ethanol (Jan10) RMA **" - ], - [ - "VCUSalo_1007_R", - "VCU BXD NA Sal M430 2.0 (Oct07) RMA" - ], - [ - "VCUEtOH_1007_R", - "VCU BXD NA EtOH M430 2.0 (Oct07) RMA **" - ], - [ - "VCUSal_1007_R", - "VCU BXD NA Et vs Sal M430 2.0 (Oct07) Sscore **" - ] - ], - "Phenotypes": [ - [ - "Macaca-fasicularisPublish", - "Macaca-fasicularis Published Phenotypes" - ] - ], - "Prefrontal Cortex": [ - [ - "HBTRC-MLPFC_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_N_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent Normal (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_AD_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent AD (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_HD_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent HD (Jun11) mlratio" - ], - [ - "INIA_MacFas_Pf_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex control (Jan10) RMA **" - ], - [ - "INIA_MacFas_PfE_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex ethanol (Jan10) RMA **" - ], - [ - "VCUSal_1006_R", - "VCU BXD PFC Et vs Sal M430 2.0 (Dec06) Sscore" - ], - [ - "VCUEtOH_1206_R", - "VCU BXD PFC EtOH M430 2.0 (Dec06) RMA" - ], - [ - "VCUSal_1206_R", - "VCU BXD PFC Sal M430 2.0 (Dec06) RMA" - ], - [ - "VCU_PF_Air_0111_R", - "VCU BXD PFC CIE Air M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_Et_0111_R", - "VCU BXD PFC CIE EtOH M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_AvE_0111_Ss", - "VCU BXD PFC EtOH vs CIE Air M430 2.0 (Jan11) Sscore **" - ], - [ - "VCUEt_vs_Sal_0806_R", - "VCU LXS PFC Et vs Sal M430A 2.0 (Aug06) Sscore **" - ], - [ - "VCUEtOH_0806_R", - "VCU LXS PFC EtOH M430A 2.0 (Aug06) RMA **" - ], - [ - "VCUSal_0806_R", - "VCU LXS PFC Sal M430A 2.0 (Aug06) RMA" - ] - ] - } - }, - "mouse": { - "AKXD": { - "Genotypes": [ - [ - "AKXDGeno", - "AKXD Genotypes" - ] - ], - "Mammary Tumors": [ - [ - "NCI_Mam_Tum_RMA_0409", - "NCI Mammary M430v2 (Apr09) RMA" - ], - [ - "NCI_Agil_Mam_Tum_RMA_0409", - "NCI Mammary LMT miRNA v2 (Apr09) RMA" - ], - [ - "MA_M_0704_R", - "NCI Mammary mRNA M430 (July04) RMA" - ], - [ - "MA_M_0704_M", - "NCI Mammary mRNA M430 (July04) MAS5" - ] - ], - "Phenotypes": [ - [ - "AKXDPublish", - "AKXD Published Phenotypes" - ] - ] - }, - "AXBXA": { - "Eye": [ - [ - "Eye_AXBXA_1008_RankInv", - "Eye AXBXA Illumina V6.2(Oct08) RankInv Beta" - ], - [ - "Eye_M2_0908_R", - "Eye M430v2 (Sep08) RMA" - ], - [ - "Eye_M2_0908_R_NB", - "Eye M430v2 Mutant Gpnmb (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_ND", - "Eye M430v2 WT Gpnmb (Sep08) RMA **" - ], - [ - "Eye_M2_0908_WTWT", - "Eye M430v2 WT WT (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_WT", - "Eye M430v2 WT Tyrp1 (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_MT", - "Eye M430v2 Mutant Tyrp1 (Sep08) RMA **" - ], - [ - "BXD_GLA_0911", - "BXD Glaucoma Affy M430 2.0 Trial (Sep11) RMA **" - ], - [ - "UIOWA_Eye_RMA_0906", - "UIOWA Eye mRNA RAE230v2 (Sep06) RMA" - ] - ], - "Genotypes": [ - [ - "AXBXAGeno", - "AXBXA Genotypes" - ] - ], - "Phenotypes": [ - [ - "AXBXAPublish", - "AXBXA Published Phenotypes" - ] - ] - }, - "B6BTBRF2": { - "Genotypes": [ - [ - "B6BTBRF2Geno", - "B6BTBRF2 Genotypes" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Phenotypes": [ - [ - "B6BTBRF2Publish", - "B6BTBRF2 Published Phenotypes" - ] - ] - }, - "B6D2F2": { - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca 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"BDF2-2005Geno", - "BDF2-2005 Genotypes" - ] - ], - "Phenotypes": [ - [ - "BDF2-2005Publish", - "BDF2-2005 Published Phenotypes" - ] - ], - "Striatum": [ - [ - "DevStriatum_ILM6.2P3RInv_1111", - "BIDMC/UTHSC Dev Striatum P3 ILMv6.2 (Nov11) RankInv **" - ], - [ - "DevStriatum_ILM6.2P14RInv_1111", - "BIDMC/UTHSC Dev Striatum P14 ILMv6.2 (Nov11) RankInv **" - ], - [ - "KIN_YSM_STR_0711", - "KIN/YSM Human STR Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "OHSU_HS-CC_ILMStr_0211", - "OHSU HS-CC Striatum ILM6v1 (Feb11) RankInv" - ], - [ - "UTHSC_Striatum_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10v2) RankInv" - ], - [ - "UTHSC_Str_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10) RankInv" - ], - [ - "UTHSC_1107_RankInv", - "HQF BXD Striatum ILM6.1 (Nov07) RankInv" - ], - [ - "SA_M2_0905_P", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) PDNN" - ], - [ - "SA_M2_0905_M", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) MAS5" - ], - [ - "SA_M2_0905_R", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) 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Female mlratio **" - ], - [ - "UCLA_BHHBF2_ADIPOSE_MALE", - "UCLA BHHBF2 Adipose Male Only" - ], - [ - "UCLA_BHHBF2_ADIPOSE_FEMALE", - "UCLA BHHBF2 Adipose Female Only" - ], - [ - "UCLA_BHHBF2_ADIPOSE_2005", - "UCLA BHHBF2 Adipose (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_ADIPOSE_2005", - "UCLA CTB6/B6CTF2 Adipose (2005) mlratio **" - ], - [ - "UCLA_BHF2_ADIPOSE_0605", - "UCLA BHF2 Adipose (June05) mlratio" - ] - ], - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca fasicularis Brain (Jan10) RMA **" - ], - [ - "GSE15222_F_RI_0409", - "GSE15222 Human Brain Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA0709", - "GSE5281 Human Brain Full Liang (Jul09) RMA" - ], - [ - "GSE5281_RMA0709", - "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_FEMALE", - "UCLA CTB6B6CTF2 Brain Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_MALE", - "UCLA CTB6B6CTF2 Brain Male mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_MALE", - "UCLA BHF2 Brain Male mlratio" - ], - [ - "UCLA_BHF2_BRAIN_FEMALE", - "UCLA BHF2 Brain Female mlratio" - ], - [ - "UCLA_BHHBF2_BRAIN_FEMALE", - "UCLA BHHBF2 Brain Female Only" - ], - [ - "UCLA_BHHBF2_BRAIN_MALE", - "UCLA BHHBF2 Brain Male Only" - ], - [ - "UCLA_BHHBF2_BRAIN_2005", - "UCLA BHHBF2 Brain (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_2005", - "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_0605", - "UCLA BHF2 Brain (June05) mlratio" - ], - [ - "BR_M2_1106_R", - "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" - ], - [ - "IBR_M_0606_R", - "INIA Brain mRNA M430 (Jun06) RMA" - ], - [ - "IBR_M_0106_P", - "INIA Brain mRNA M430 (Jan06) PDNN" - ], - [ - "IBR_M_0106_R", - "INIA Brain mRNA M430 (Jan06) RMA" - ], - [ - "BR_U_1105_P", - "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" - ], - [ - "BR_U_0805_M", - "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" - ], - [ - "BR_U_0805_P", - "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" - ], - [ - "BR_U_0805_R", - "UTHSC Brain mRNA U74Av2 (Aug05) RMA" - ], - [ - "BRF2_M_0805_R", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" - ], - [ - "BRF2_M_0805_P", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" - ], - [ - "BRF2_M_0805_M", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" - ], - [ - "BRF2_M_0304_P", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" - ], - [ - "BRF2_M_0304_R", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" - ], - [ - "BRF2_M_0304_M", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" - ], - [ - "CB_M_0204_P", - "INIA Brain mRNA M430 (Feb04) PDNN" - ] - ], - "Genotypes": [ - [ - "BHF2Geno", - "BHF2 Genotypes" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Muscle": [ - [ - "EPFLMouseMuscleRMA1211", - "EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleCDRMA1211", - "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleHFDRMA1211", - "EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_FEMALE", - "UCLA CTB6B6CTF2 Muscle Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_MALE", - "UCLA CTB6B6CTF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_FEMALE", - "UCLA BHHBF2 Muscle Female Only" - ], - [ - "UCLA_BHHBF2_MUSCLE_MALE", - "UCLA BHHBF2 Muscle Male Only" - ], - [ - "UCLA_BHF2_MUSCLE_MALE", - "UCLA BHF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_FEMALE", - "UCLA BHF2 Muscle Female mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_2005", - "UCLA BHHBF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_2005", - "UCLA CTB6/B6CTF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_0605", - "UCLA BHF2 Muscle (June05) mlratio **" - ] - ], - "Phenotypes": [ - [ - "BHF2Publish", - "BHF2 Published Phenotypes" - ] - ] - }, - "BHHBF2": { - "Adipose": [ - [ - "UCLA_BHF2_ADIPOSE_MALE", - "UCLA BHF2 Adipose Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_ADIPOSE_MALE", - "UCLA CTB6B6CTF2 Adipose Male mlratio **" - ], - [ - "UCLA_BHF2_ADIPOSE_FEMALE", - "UCLA BHF2 Adipose Female mlratio" - ], - [ - "UCLA_CTB6B6CTF2_ADIPOSE_FEMALE", - "UCLA CTB6B6CTF2 Adipose Female mlratio **" - ], - [ - "UCLA_BHHBF2_ADIPOSE_MALE", - "UCLA BHHBF2 Adipose Male Only" - ], - [ - "UCLA_BHHBF2_ADIPOSE_FEMALE", - "UCLA BHHBF2 Adipose Female Only" - ], - [ - "UCLA_BHHBF2_ADIPOSE_2005", - "UCLA BHHBF2 Adipose (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_ADIPOSE_2005", - "UCLA CTB6/B6CTF2 Adipose (2005) mlratio **" - ], - [ - "UCLA_BHF2_ADIPOSE_0605", - "UCLA BHF2 Adipose (June05) mlratio" - ] - ], - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca fasicularis Brain (Jan10) RMA **" - ], - [ - "GSE15222_F_RI_0409", - "GSE15222 Human Brain Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA0709", - "GSE5281 Human Brain Full Liang (Jul09) RMA" - ], - [ - "GSE5281_RMA0709", - "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_FEMALE", - "UCLA CTB6B6CTF2 Brain Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_MALE", - "UCLA CTB6B6CTF2 Brain Male mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_MALE", - "UCLA BHF2 Brain Male mlratio" - ], - [ - "UCLA_BHF2_BRAIN_FEMALE", - "UCLA BHF2 Brain Female mlratio" - ], - [ - "UCLA_BHHBF2_BRAIN_FEMALE", - "UCLA BHHBF2 Brain Female Only" - ], - [ - "UCLA_BHHBF2_BRAIN_MALE", - "UCLA BHHBF2 Brain Male Only" - ], - [ - "UCLA_BHHBF2_BRAIN_2005", - "UCLA BHHBF2 Brain (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_2005", - "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_0605", - "UCLA BHF2 Brain (June05) mlratio" - ], - [ - "BR_M2_1106_R", - "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" - ], - [ - "IBR_M_0606_R", - "INIA Brain mRNA M430 (Jun06) RMA" - ], - [ - "IBR_M_0106_P", - "INIA Brain mRNA M430 (Jan06) PDNN" - ], - [ - "IBR_M_0106_R", - "INIA Brain mRNA M430 (Jan06) RMA" - ], - [ - "BR_U_1105_P", - "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" - ], - [ - "BR_U_0805_M", - "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" - ], - [ - "BR_U_0805_P", - "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" - ], - [ - "BR_U_0805_R", - "UTHSC Brain mRNA U74Av2 (Aug05) RMA" - ], - [ - "BRF2_M_0805_R", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" - ], - [ - "BRF2_M_0805_P", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" - ], - [ - "BRF2_M_0805_M", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" - ], - [ - "BRF2_M_0304_P", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" - ], - [ - "BRF2_M_0304_R", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" - ], - [ - "BRF2_M_0304_M", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" - ], - [ - "CB_M_0204_P", - "INIA Brain mRNA M430 (Feb04) PDNN" - ] - ], - "Genotypes": [ - [ - "BHHBF2Geno", - "BHHBF2 Genotypes" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Muscle": [ - [ - "EPFLMouseMuscleRMA1211", - "EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleCDRMA1211", - "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleHFDRMA1211", - "EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_FEMALE", - "UCLA CTB6B6CTF2 Muscle Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_MALE", - "UCLA CTB6B6CTF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_FEMALE", - "UCLA BHHBF2 Muscle Female Only" - ], - [ - "UCLA_BHHBF2_MUSCLE_MALE", - "UCLA BHHBF2 Muscle Male Only" - ], - [ - "UCLA_BHF2_MUSCLE_MALE", - "UCLA BHF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_FEMALE", - "UCLA BHF2 Muscle Female mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_2005", - "UCLA BHHBF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_2005", - "UCLA CTB6/B6CTF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_0605", - "UCLA BHF2 Muscle (June05) mlratio **" - ] - ], - "Phenotypes": [ - [ - "BHHBF2Publish", - "BHHBF2 Published Phenotypes" - ] - ] - }, - "BXD": { - "Amygdala": [ - [ - "KIN_YSM_AMY_0711", - "KIN/YSM Human AMY Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "INIA_AmgCoh_0311", - "INIA Amygdala Cohort Affy MoGene 1.0 ST (Mar11) RMA" - ], - [ - "INIA_Amg_BLA_RMA_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA" - ], - [ - "INIA_Amg_BLA_RMA_M_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Male" - ], - [ - "INIA_Amg_BLA_RMA_F_1110", - "INIA Amygdala Affy MoGene 1.0 ST (Nov10) RMA Female" - ], - [ - "INIA_MacFas_AMGc_RMA_0110", - "INIA Macaca fasicularis Amygdala control (Jan10) RMA **" - ], - [ - "INIA_MacFas_AMGe_RMA_0110", - "INIA Macaca fasicularis Amygdala ethanol (Jan10) RMA **" - ] - ], - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca fasicularis Brain (Jan10) RMA **" - ], - [ - "GSE15222_F_RI_0409", - "GSE15222 Human Brain Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA0709", - "GSE5281 Human Brain Full Liang (Jul09) RMA" - ], - [ - "GSE5281_RMA0709", - "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_FEMALE", - "UCLA CTB6B6CTF2 Brain Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_MALE", - "UCLA CTB6B6CTF2 Brain Male mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_MALE", - "UCLA BHF2 Brain Male mlratio" - ], - [ - "UCLA_BHF2_BRAIN_FEMALE", - "UCLA BHF2 Brain Female mlratio" - ], - [ - "UCLA_BHHBF2_BRAIN_FEMALE", - "UCLA BHHBF2 Brain Female Only" - ], - [ - "UCLA_BHHBF2_BRAIN_MALE", - "UCLA BHHBF2 Brain Male Only" - ], - [ - "UCLA_BHHBF2_BRAIN_2005", - "UCLA BHHBF2 Brain (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_2005", - "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_0605", - "UCLA BHF2 Brain (June05) mlratio" - ], - [ - "BR_M2_1106_R", - "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" - ], - [ - "IBR_M_0606_R", - "INIA Brain mRNA M430 (Jun06) RMA" - ], - [ - "IBR_M_0106_P", - "INIA Brain mRNA M430 (Jan06) PDNN" - ], - [ - "IBR_M_0106_R", - "INIA Brain mRNA M430 (Jan06) RMA" - ], - [ - "BR_U_1105_P", - "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" - ], - [ - "BR_U_0805_M", - "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" - ], - [ - "BR_U_0805_P", - "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" - ], - [ - "BR_U_0805_R", - "UTHSC Brain mRNA U74Av2 (Aug05) RMA" - ], - [ - "BRF2_M_0805_R", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" - ], - [ - "BRF2_M_0805_P", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" - ], - [ - "BRF2_M_0805_M", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" - ], - [ - "BRF2_M_0304_P", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" - ], - [ - "BRF2_M_0304_R", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" - ], - [ - "BRF2_M_0304_M", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" - ], - [ - "CB_M_0204_P", - "INIA Brain mRNA M430 (Feb04) PDNN" - ] - ], - "Cartilage": [ - [ - "UCLA_BXDBXH_CARTILAGE_V2", - "UCLA BXD and BXH Cartilage v2" - ], - [ - "UCLA_BXHBXD_CARTILAGE_V2", - "UCLA BXH and BXD Cartilage v2" - ], - [ - "UCLA_BXDBXH_CARTILAGE", - "UCLA BXD and BXH Cartilage" - ], - [ - "UCLA_BXHBXD_CARTILAGE", - "UCLA BXH and BXD Cartilage" - ], - [ - "UCLA_BXD_CARTILAGE", - "UCLA BXD Cartilage" - ], - [ - "UCLA_BXH_CARTILAGE", - "UCLA BXH Cartilage" - ] - ], - "Cerebellum": [ - [ - "HBTRC-MLC_0611", - "HBTRC-MLC Human Cerebellum Agilent (Jun11) mlratio" - ], - [ - "HBTRC-MLC_N_0611", - "HBTRC-MLC Human Cerebellum Agilent Normal (Jun11) mlratio" - ], - [ - "HBTRC-MLC_AD_0611", - "HBTRC-MLC Human Cerebellum Agilent AD (Jun11) mlratio" - ], - [ - "HBTRC-MLC_HD_0611", - "HBTRC-MLC Human Cerebellum Agilent HD (Jun11) mlratio" - ], - [ - "GCB_M2_0505_M", - "GE-NIAAA Cerebellum mRNA M430v2 (May05) MAS5" - ], - [ - "GCB_M2_0505_R", - "GE-NIAAA Cerebellum mRNA M430v2 (May05) RMA" - ], - [ - "GCB_M2_0505_P", - "GE-NIAAA Cerebellum mRNA M430v2 (May05) PDNN" - ], - [ - "CB_M_0305_R", - "SJUT Cerebellum mRNA M430 (Mar05) RMA" - ], - [ - "CB_M_0305_M", - "SJUT Cerebellum mRNA M430 (Mar05) MAS5" - ], - [ - "CB_M_0305_P", - "SJUT Cerebellum mRNA M430 (Mar05) PDNN" - ], - [ - "CB_M_1004_R", - "SJUT Cerebellum mRNA M430 (Oct04) RMA" - ], - [ - "CB_M_1004_M", - "SJUT Cerebellum mRNA M430 (Oct04) MAS5" - ], - [ - "CB_M_1004_P", - "SJUT Cerebellum mRNA M430 (Oct04) PDNN" - ], - [ - "CB_M_1003_M", - "SJUT Cerebellum mRNA M430 (Oct03) MAS5" - ] - ], - "Eye": [ - [ - "Eye_AXBXA_1008_RankInv", - "Eye AXBXA Illumina V6.2(Oct08) RankInv Beta" - ], - [ - "Eye_M2_0908_R", - "Eye M430v2 (Sep08) RMA" - ], - [ - "Eye_M2_0908_R_NB", - "Eye M430v2 Mutant Gpnmb (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_ND", - "Eye M430v2 WT Gpnmb (Sep08) RMA **" - ], - [ - "Eye_M2_0908_WTWT", - "Eye M430v2 WT WT (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_WT", - "Eye M430v2 WT Tyrp1 (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_MT", - "Eye M430v2 Mutant Tyrp1 (Sep08) RMA **" - ], - [ - "BXD_GLA_0911", - "BXD Glaucoma Affy M430 2.0 Trial (Sep11) RMA **" - ], - [ - "UIOWA_Eye_RMA_0906", - "UIOWA Eye mRNA RAE230v2 (Sep06) RMA" - ] - ], - "Genotypes": [ - [ - "BXDGeno", - "BXD Genotypes" - ] - ], - "Hematopoietic Cells": [ - [ - "UMCG_0907_HemaStem_ori", - "UMCG Stem Cells ILM6v1.1 (Apr09) original" - ], - [ - "UMCG_0907_HemaStem", - "UMCG Stem Cells ILM6v1.1 (Apr09) transformed" - ], - [ - "UMCG_0907_Pro_ori", - "UMCG Progenitor Cells ILM6v1.1 (Apr09) original" - ], - [ - "UMCG_0907_Pro", - "UMCG Progenitor Cells ILM6v1.1 (Apr09) transformed" - ], - [ - "UMCG_0907_Eryth_ori", - "UMCG Erythroid Cells ILM6v1.1 (Apr09) original" - ], - [ - "UMCG_0907_Eryth", - "UMCG Erythroid Cells ILM6v1.1 (Apr09) transformed" - ], - [ - "UMCG_0907_Myeloid_ori", - "UMCG Myeloid Cells ILM6v1.1 (Apr09) original" - ], - [ - "UMCG_0907_Myeloid", - "UMCG Myeloid Cells ILM6v1.1 (Apr09) transformed" - ], - [ - "HC_U_0304_R", - "GNF Stem Cells U74Av2 (Mar04) RMA" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium M430v2 (Jun06) RMA MDP" - ], - [ - "OXUKHS_ILMHipp_RI0510", - "OX UK HS ILM6v1.1 Hippocampus (May 2010) RankInv" - ], - [ - "INIA_MacFas_Hc_RMA_0110", - "INIA Macaca fasicularis Hippocampus control (Jan10) RMA **" - ], - [ - "INIA_MacFas_He_RMA_0110", - "INIA Macaca fasicularis Hippocampus ethanol (Jan10) RMA **" - ], - [ - "UT_HippRatEx_RMA_0709", - "UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" - ], - [ - "Illum_LXS_Hipp_loess0807", - "Hippocampus Illumina (Aug07) LOESS" - ], - [ - "Illum_LXS_Hipp_loess_nb0807", - "Hippocampus Illumina (Aug07) LOESS_NB" - ], - [ - "Illum_LXS_Hipp_quant0807", - "Hippocampus Illumina (Aug07) QUANT" - ], - [ - "Illum_LXS_Hipp_quant_nb0807", - "Hippocampus Illumina (Aug07) QUANT_NB" - ], - [ - "Illum_LXS_Hipp_rsn0807", - "Hippocampus Illumina (Aug07) RSN" - ], - [ - "Illum_LXS_Hipp_rsn_nb0807", - "Hippocampus Illumina (Aug07) RSN_NB" - ], - [ - "Hipp_Illumina_RankInv_0507", - "Hippocampus Illumina (May07) RankInv" - ], - [ - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Hypothalamus": [ - [ - "INIA_Hyp_RMA_1110", - "INIA Hypothalamus Affy MoGene 1.0 ST (Nov10)" - ], - [ - "INIA_Hyp_M_RMA_1110", - "INIA Hypothalamus Affy MoGene 1.0 ST (Nov10) Male" - ], - [ - "INIA_Hyp_F_RMA_1110", - "INIA Hypothalamus Affy MoGene 1.0 ST (Nov10) Female" - ] - ], - "Kidney": [ - [ - "MA_M2F_0706_R", - "Mouse kidney M430v2 Female (Aug06) RMA" - ], - [ - "MA_M2M_0706_R", - "Mouse kidney M430v2 Male (Aug06) RMA" - ], - [ - "MA_M2_0806_R", - "Mouse kidney M430v2 Sex Balanced (Aug06) RMA" - ], - [ - "MA_M2_0806_P", - "Mouse Kidney M430v2 Sex Balanced (Aug06) PDNN" - ], - [ - "MA_M2_0706_P", - "Mouse Kidney M430v2 (Jul06) PDNN" - ], - [ - "MA_M2_0706_R", - "Mouse Kidney M430v2 (Jul06) RMA" - ], - [ - "KI_2A_0405_M", - "MDC/CAS/ICL Kidney 230A (Apr05) MAS5" - ], - [ - "KI_2A_0405_Rz", - "MDC/CAS/ICL Kidney 230A (Apr05) RMA 2z+8" - ], - [ - "KI_2A_0405_R", - "MDC/CAS/ICL Kidney 230A (Apr05) RMA" - ] - ], - "Leucocytes": [ - [ - "Illum_BXD_PBL_1108", - "UWA Illumina PBL (Nov08) RSN **" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Lung": [ - [ - "OXUKHS_ILMLung_RI0510", - "OX UK HS ILM6v1.1 Lung (May 2010) RankInv" - ], - [ - "HZI_0408_R", - "HZI Lung M430v2 (Apr08) RMA" - ], - [ - "HZI_0408_M", - "HZI Lung M430v2 (Apr08) MAS5" - ] - ], - "Midbrain": [ - [ - "VUBXDMouseMidBrainQ0212", - "VU BXD Midbrain Agilent SurePrint G3 Mouse GE (Feb12) Quantile" - ] - ], - "Muscle": [ - [ - "EPFLMouseMuscleRMA1211", - "EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleCDRMA1211", - "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleHFDRMA1211", - "EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_FEMALE", - "UCLA CTB6B6CTF2 Muscle Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_MALE", - "UCLA CTB6B6CTF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_FEMALE", - "UCLA BHHBF2 Muscle Female Only" - ], - [ - "UCLA_BHHBF2_MUSCLE_MALE", - "UCLA BHHBF2 Muscle Male Only" - ], - [ - "UCLA_BHF2_MUSCLE_MALE", - "UCLA BHF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_FEMALE", - "UCLA BHF2 Muscle Female mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_2005", - "UCLA BHHBF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_2005", - "UCLA CTB6/B6CTF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_0605", - "UCLA BHF2 Muscle (June05) mlratio **" - ] - ], - "Neocortex": [ - [ - "DevNeocortex_ILM6.2P14RInv_1111", - "BIDMC/UTHSC Dev Neocortex P14 ILMv6.2 (Nov11) RankInv **" - ], - [ - "DevNeocortex_ILM6.2P3RInv_1111", - "BIDMC/UTHSC Dev Neocortex P3 ILMv6.2 (Nov11) RankInv **" - ], - [ - "HQFNeoc_1210v2_RankInv", - "HQF BXD Neocortex ILM6v1.1 (Dec10v2) RankInv" - ], - [ - "HQFNeoc_1210_RankInv", - "HQF BXD Neocortex ILM6v1.1 (Dec10) RankInv" - ], - [ - "HQFNeoc_0208_RankInv", - "HQF BXD Neocortex ILM6v1.1 (Feb08) RankInv" - ], - [ - "DevNeocortex_ILM6.2P3RInv_1110", - "BIDMC/UTHSC Dev Neocortex P3 ILMv6.2 (Nov10) RankInv **" - ], - [ - "DevNeocortex_ILM6.2P14RInv_1110", - "BIDMC/UTHSC Dev Neocortex P14 ILMv6.2 (Nov10) RankInv **" - ] - ], - "Nucleus Accumbens": [ - [ - "INIA_MacFas_Ac_RMA_0110", - "INIA Macaca fasicularis Nucleus Accumbens control (Jan10) RMA **" - ], - [ - "INIA_MacFas_Ae_RMA_0110", - "INIA Macaca fasicularis Nucleus Accumbens ethanol (Jan10) RMA **" - ], - [ - "VCUSalo_1007_R", - "VCU BXD NA Sal M430 2.0 (Oct07) RMA" - ], - [ - "VCUEtOH_1007_R", - "VCU BXD NA EtOH M430 2.0 (Oct07) RMA **" - ], - [ - "VCUSal_1007_R", - "VCU BXD NA Et vs Sal M430 2.0 (Oct07) Sscore **" - ] - ], - "Phenotypes": [ - [ - "BXDPublish", - "BXD Published Phenotypes" - ] - ], - "Prefrontal Cortex": [ - [ - "HBTRC-MLPFC_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_N_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent Normal (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_AD_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent AD (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_HD_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent HD (Jun11) mlratio" - ], - [ - "INIA_MacFas_Pf_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex control (Jan10) RMA **" - ], - [ - "INIA_MacFas_PfE_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex ethanol (Jan10) RMA **" - ], - [ - "VCUSal_1006_R", - "VCU BXD PFC Et vs Sal M430 2.0 (Dec06) Sscore" - ], - [ - "VCUEtOH_1206_R", - "VCU BXD PFC EtOH M430 2.0 (Dec06) RMA" - ], - [ - "VCUSal_1206_R", - "VCU BXD PFC Sal M430 2.0 (Dec06) RMA" - ], - [ - "VCU_PF_Air_0111_R", - "VCU BXD PFC CIE Air M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_Et_0111_R", - "VCU BXD PFC CIE EtOH M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_AvE_0111_Ss", - "VCU BXD PFC EtOH vs CIE Air M430 2.0 (Jan11) Sscore **" - ], - [ - "VCUEt_vs_Sal_0806_R", - "VCU LXS PFC Et vs Sal M430A 2.0 (Aug06) Sscore **" - ], - [ - "VCUEtOH_0806_R", - "VCU LXS PFC EtOH M430A 2.0 (Aug06) RMA **" - ], - [ - "VCUSal_0806_R", - "VCU LXS PFC Sal M430A 2.0 (Aug06) RMA" - ] - ], - "Retina": [ - [ - "Illum_Retina_BXD_RankInv0410", - "HEI Retina Illumina V6.2 (April 2010) RankInv" - ], - [ - "B6D2ONCILM_0412", - "B6D2 ONC Illumina v6.1 (Apr12) RankInv **" - ], - [ - "ONCRetILM6_0412", - "ONC Retina Illumina V6.2 (Apr12) RankInv **" - ], - [ - "G2HEIONCRetILM6_0911", - "G2 HEI ONC Retina Illumina V6.2 (Sep11) RankInv **" - ], - [ - "HEIONCRetILM6_0911", - "HEI ONC Retina Illumina V6.2 (Sep11) RankInv **" - ], - [ - "HEIONCvsCRetILM6_0911", - "HEI ONC vs Control Retina Illumina V6.2 (Sep11) RankInv **" - ], - [ - "ILM_Retina_BXD_F_RankInv1210", - "HEI Retina Females Illumina V6.2 (Dec10) RankInv **" - ], - [ - "ILM_Retina_BXD_M_RankInv1210", - "HEI Retina Males Illumina V6.2 (Dec10) RankInv **" - ], - [ - "ILM_Retina_BXD_FM_RankInv1210", - "HEI Retina F-M Illumina V6.2 (Dec10) RankInv **" - ], - [ - "G2NEI_ILM_Retina_BXD_RI0410", - "G2NEI Retina Illumina V6.2 (April 2010) RankInv **" - ] - ], - "Spleen": [ - [ - "UTHSC_SPL_RMA_1210", - "UTHSC Affy MoGene 1.0 ST Spleen (Dec10) RMA" - ], - [ - "UTHSC_SPL_RMA_1010", - "UTHSC Affy MoGene 1.0 ST Spleen (Oct10) RMA" - ], - [ - "IoP_SPL_RMA_0509", - "IoP Affy MOE 430v2 Spleen (May09) RMA" - ], - [ - "Illum_BXD_Spl_1108", - "UWA Illumina Spleen (Nov08) RSN **" - ], - [ - "UTK_BXDSpl_VST_0110", - "UTK Spleen ILM6.1 (Jan10) VST" - ], - [ - "STSPL_1107_R", - "Stuart Spleen M430v2 (Nov07) RMA" - ] - ], - "Striatum": [ - [ - "DevStriatum_ILM6.2P3RInv_1111", - "BIDMC/UTHSC Dev Striatum P3 ILMv6.2 (Nov11) RankInv **" - ], - [ - "DevStriatum_ILM6.2P14RInv_1111", - "BIDMC/UTHSC Dev Striatum P14 ILMv6.2 (Nov11) RankInv **" - ], - [ - "KIN_YSM_STR_0711", - "KIN/YSM Human STR Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "OHSU_HS-CC_ILMStr_0211", - "OHSU HS-CC Striatum ILM6v1 (Feb11) RankInv" - ], - [ - "UTHSC_Striatum_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10v2) RankInv" - ], - [ - "UTHSC_Str_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10) RankInv" - ], - [ - "UTHSC_1107_RankInv", - "HQF BXD Striatum ILM6.1 (Nov07) RankInv" - ], - [ - "SA_M2_0905_P", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) PDNN" - ], - [ - "SA_M2_0905_M", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) MAS5" - ], - [ - "SA_M2_0905_R", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) RMA" - ], - [ - "SA_M2_0405_MC", - "HBP Rosen Striatum M430V2 (Apr05) MAS5 Clean" - ], - [ - "SA_M2_0405_RC", - "HBP Rosen Striatum M430V2 (Apr05) RMA Clean" - ], - [ - "SA_M2_0405_PC", - "HBP Rosen Striatum M430V2 (Apr05) PDNN Clean" - ], - [ - "SA_M2_0405_SS", - "HBP Rosen Striatum M430V2 (Apr05) SScore" - ], - [ - "SA_M2_0405_RR", - "HBP Rosen Striatum M430V2 (Apr05) RMA Orig" - ], - [ - "Striatum_Exon_0209", - "HQF Striatum Exon (Feb09) RMA" - ], - [ - "DevStriatum_ILM6.2P3RInv_1110", - "BIDMC/UTHSC Dev Striatum P3 ILMv6.2 (Nov10) RankInv **" - ], - [ - "DevStriatum_ILM6.2P14RInv_1110", - "BIDMC/UTHSC Dev Striatum P14 ILMv6.2 (Nov10) RankInv **" - ] - ], - "T Cell (helper)": [ - [ - "RTHC_0211_R", - "HZI Thelp M430v2 (Feb11) RMA" - ] - ], - "T Cell (regulatory)": [ - [ - "RTC_1106_R", - "HZI Treg M430v2 (Feb11) RMA" - ] - ], - "Thymus": [ - [ - "Illum_BXD_Thy_1108", - "UWA Illumina Thymus (Nov08) RSN **" - ] - ], - "Ventral Tegmental Area": [ - [ - "VCUEtvsSal_0609_R", - "VCU BXD VTA Et vs Sal M430 2.0 (Jun09) Sscore **" - ], - [ - "VCUEtOH_0609_R", - "VCU BXD VTA EtOH M430 2.0 (Jun09) RMA **" - ], - [ - "VCUSal_0609_R", - "VCU BXD VTA Sal M430 2.0 (Jun09) RMA **" - ] - ] - }, - "BXH": { - "Cartilage": [ - [ - "UCLA_BXDBXH_CARTILAGE_V2", - "UCLA BXD and BXH Cartilage v2" - ], - [ - "UCLA_BXHBXD_CARTILAGE_V2", - "UCLA BXH and BXD Cartilage v2" - ], - [ - "UCLA_BXDBXH_CARTILAGE", - "UCLA BXD and BXH Cartilage" - ], - [ - "UCLA_BXHBXD_CARTILAGE", - "UCLA BXH and BXD Cartilage" - ], - [ - "UCLA_BXD_CARTILAGE", - "UCLA BXD Cartilage" - ], - [ - "UCLA_BXH_CARTILAGE", - "UCLA BXH Cartilage" - ] - ], - "Genotypes": [ - [ - "BXHGeno", - "BXH Genotypes" - ] - ], - "Phenotypes": [ - [ - "BXHPublish", - "BXH Published Phenotypes" - ] - ] - }, - "CTB6F2": { - "Adipose": [ - [ - "UCLA_BHF2_ADIPOSE_MALE", - "UCLA BHF2 Adipose Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_ADIPOSE_MALE", - "UCLA CTB6B6CTF2 Adipose Male mlratio **" - ], - [ - "UCLA_BHF2_ADIPOSE_FEMALE", - "UCLA BHF2 Adipose Female mlratio" - ], - [ - "UCLA_CTB6B6CTF2_ADIPOSE_FEMALE", - "UCLA CTB6B6CTF2 Adipose Female mlratio **" - ], - [ - "UCLA_BHHBF2_ADIPOSE_MALE", - "UCLA BHHBF2 Adipose Male Only" - ], - [ - "UCLA_BHHBF2_ADIPOSE_FEMALE", - "UCLA BHHBF2 Adipose Female Only" - ], - [ - "UCLA_BHHBF2_ADIPOSE_2005", - "UCLA BHHBF2 Adipose (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_ADIPOSE_2005", - "UCLA CTB6/B6CTF2 Adipose (2005) mlratio **" - ], - [ - "UCLA_BHF2_ADIPOSE_0605", - "UCLA BHF2 Adipose (June05) mlratio" - ] - ], - "Brain": [ - [ - "GSE15222_F_N_RI_0409", - "GSE15222 Human Brain Normal Myers (Apr09) RankInv" - ], - [ - "GSE15222_F_A_RI_0409", - "GSE15222 Human Brain Alzheimer Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA_N_0709", - "GSE5281 Human Brain Normal Full Liang (Jul09) RMA" - ], - [ - "GSE5281_F_RMA_Alzh_0709", - "GSE5281 Human Brain Alzheimer Full Liang (Jul09) RMA" - ], - [ - "INIA_MacFas_brain_RMA_0110", - "INIA Macaca fasicularis Brain (Jan10) RMA **" - ], - [ - "GSE15222_F_RI_0409", - "GSE15222 Human Brain Myers (Apr09) RankInv" - ], - [ - "GSE5281_F_RMA0709", - "GSE5281 Human Brain Full Liang (Jul09) RMA" - ], - [ - "GSE5281_RMA0709", - "GSE5281 Human Brain Best 102 Liang (Jul09) RMA" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_FEMALE", - "UCLA CTB6B6CTF2 Brain Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_MALE", - "UCLA CTB6B6CTF2 Brain Male mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_MALE", - "UCLA BHF2 Brain Male mlratio" - ], - [ - "UCLA_BHF2_BRAIN_FEMALE", - "UCLA BHF2 Brain Female mlratio" - ], - [ - "UCLA_BHHBF2_BRAIN_FEMALE", - "UCLA BHHBF2 Brain Female Only" - ], - [ - "UCLA_BHHBF2_BRAIN_MALE", - "UCLA BHHBF2 Brain Male Only" - ], - [ - "UCLA_BHHBF2_BRAIN_2005", - "UCLA BHHBF2 Brain (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_BRAIN_2005", - "UCLA CTB6/B6CTF2 Brain (2005) mlratio **" - ], - [ - "UCLA_BHF2_BRAIN_0605", - "UCLA BHF2 Brain (June05) mlratio" - ], - [ - "BR_M2_1106_R", - "UCHSC BXD Whole Brain M430 2.0 (Nov06) RMA" - ], - [ - "IBR_M_0606_R", - "INIA Brain mRNA M430 (Jun06) RMA" - ], - [ - "IBR_M_0106_P", - "INIA Brain mRNA M430 (Jan06) PDNN" - ], - [ - "IBR_M_0106_R", - "INIA Brain mRNA M430 (Jan06) RMA" - ], - [ - "BR_U_1105_P", - "UTHSC Brain mRNA U74Av2 (Nov05) PDNN" - ], - [ - "BR_U_0805_M", - "UTHSC Brain mRNA U74Av2 (Aug05) MAS5" - ], - [ - "BR_U_0805_P", - "UTHSC Brain mRNA U74Av2 (Aug05) PDNN" - ], - [ - "BR_U_0805_R", - "UTHSC Brain mRNA U74Av2 (Aug05) RMA" - ], - [ - "BRF2_M_0805_R", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) RMA" - ], - [ - "BRF2_M_0805_P", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) PDNN" - ], - [ - "BRF2_M_0805_M", - "OHSU/VA B6D2F2 Brain mRNA M430 (Aug05) MAS5" - ], - [ - "BRF2_M_0304_P", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) PDNN" - ], - [ - "BRF2_M_0304_R", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) RMA" - ], - [ - "BRF2_M_0304_M", - "OHSU/VA B6D2F2 Brain mRNA M430A (Mar04) MAS5" - ], - [ - "CB_M_0204_P", - "INIA Brain mRNA M430 (Feb04) PDNN" - ] - ], - "Genotypes": [ - [ - "CTB6F2Geno", - "CTB6F2 Genotypes" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Muscle": [ - [ - "EPFLMouseMuscleRMA1211", - "EPFL/LISP BXD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleCDRMA1211", - "EPFL/LISP BXD CD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "EPFLMouseMuscleHFDRMA1211", - "EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST (Dec11) RMA **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_FEMALE", - "UCLA CTB6B6CTF2 Muscle Female mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_MALE", - "UCLA CTB6B6CTF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_FEMALE", - "UCLA BHHBF2 Muscle Female Only" - ], - [ - "UCLA_BHHBF2_MUSCLE_MALE", - "UCLA BHHBF2 Muscle Male Only" - ], - [ - "UCLA_BHF2_MUSCLE_MALE", - "UCLA BHF2 Muscle Male mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_FEMALE", - "UCLA BHF2 Muscle Female mlratio **" - ], - [ - "UCLA_BHHBF2_MUSCLE_2005", - "UCLA BHHBF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_MUSCLE_2005", - "UCLA CTB6/B6CTF2 Muscle (2005) mlratio **" - ], - [ - "UCLA_BHF2_MUSCLE_0605", - "UCLA BHF2 Muscle (June05) mlratio **" - ] - ], - "Phenotypes": [ - [ - "CTB6F2Publish", - "CTB6F2 Published Phenotypes" - ] - ] - }, - "CXB": { - "Genotypes": [ - [ - "CXBGeno", - "CXB Genotypes" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium M430v2 (Jun06) RMA MDP" - ], - [ - "OXUKHS_ILMHipp_RI0510", - "OX UK HS ILM6v1.1 Hippocampus (May 2010) RankInv" - ], - [ - "INIA_MacFas_Hc_RMA_0110", - "INIA Macaca fasicularis Hippocampus control (Jan10) RMA **" - ], - [ - "INIA_MacFas_He_RMA_0110", - "INIA Macaca fasicularis Hippocampus ethanol (Jan10) RMA **" - ], - [ - "UT_HippRatEx_RMA_0709", - "UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" - ], - [ - "Illum_LXS_Hipp_loess0807", - "Hippocampus Illumina (Aug07) LOESS" - ], - [ - "Illum_LXS_Hipp_loess_nb0807", - "Hippocampus Illumina (Aug07) LOESS_NB" - ], - [ - "Illum_LXS_Hipp_quant0807", - "Hippocampus Illumina (Aug07) QUANT" - ], - [ - "Illum_LXS_Hipp_quant_nb0807", - "Hippocampus Illumina (Aug07) QUANT_NB" - ], - [ - "Illum_LXS_Hipp_rsn0807", - "Hippocampus Illumina (Aug07) RSN" - ], - [ - "Illum_LXS_Hipp_rsn_nb0807", - "Hippocampus Illumina (Aug07) RSN_NB" - ], - [ - "Hipp_Illumina_RankInv_0507", - "Hippocampus Illumina (May07) RankInv" - ], - [ - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Phenotypes": [ - [ - "CXBPublish", - "CXB Published Phenotypes" - ] - ], - "Spleen": [ - [ - "UTHSC_SPL_RMA_1210", - "UTHSC Affy MoGene 1.0 ST Spleen (Dec10) RMA" - ], - [ - "UTHSC_SPL_RMA_1010", - "UTHSC Affy MoGene 1.0 ST Spleen (Oct10) RMA" - ], - [ - "IoP_SPL_RMA_0509", - "IoP Affy MOE 430v2 Spleen (May09) RMA" - ], - [ - "Illum_BXD_Spl_1108", - "UWA Illumina Spleen (Nov08) RSN **" - ], - [ - "UTK_BXDSpl_VST_0110", - "UTK Spleen ILM6.1 (Jan10) VST" - ], - [ - "STSPL_1107_R", - "Stuart Spleen M430v2 (Nov07) RMA" - ] - ] - }, - "HS": { - "Genotypes": [ - [ - "HSGeno", - "HS Genotypes" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium M430v2 (Jun06) RMA MDP" - ], - [ - "OXUKHS_ILMHipp_RI0510", - "OX UK HS ILM6v1.1 Hippocampus (May 2010) RankInv" - ], - [ - "INIA_MacFas_Hc_RMA_0110", - "INIA Macaca fasicularis Hippocampus control (Jan10) RMA **" - ], - [ - "INIA_MacFas_He_RMA_0110", - "INIA Macaca fasicularis Hippocampus ethanol (Jan10) RMA **" - ], - [ - "UT_HippRatEx_RMA_0709", - "UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" - ], - [ - "Illum_LXS_Hipp_loess0807", - "Hippocampus Illumina (Aug07) LOESS" - ], - [ - "Illum_LXS_Hipp_loess_nb0807", - "Hippocampus Illumina (Aug07) LOESS_NB" - ], - [ - "Illum_LXS_Hipp_quant0807", - "Hippocampus Illumina (Aug07) QUANT" - ], - [ - "Illum_LXS_Hipp_quant_nb0807", - "Hippocampus Illumina (Aug07) QUANT_NB" - ], - [ - "Illum_LXS_Hipp_rsn0807", - "Hippocampus Illumina (Aug07) RSN" - ], - [ - "Illum_LXS_Hipp_rsn_nb0807", - "Hippocampus Illumina (Aug07) RSN_NB" - ], - [ - "Hipp_Illumina_RankInv_0507", - "Hippocampus Illumina (May07) RankInv" - ], - [ - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Lung": [ - [ - "OXUKHS_ILMLung_RI0510", - "OX UK HS ILM6v1.1 Lung (May 2010) RankInv" - ], - [ - "HZI_0408_R", - "HZI Lung M430v2 (Apr08) RMA" - ], - [ - "HZI_0408_M", - "HZI Lung M430v2 (Apr08) MAS5" - ] - ], - "Phenotypes": [ - [ - "HSPublish", - "HS Published Phenotypes" - ] - ] - }, - "HS-CC": { - "Genotypes": [ - [ - "HS-CCGeno", - "HS-CC Genotypes" - ] - ], - "Phenotypes": [ - [ - "HS-CCPublish", - "HS-CC Published Phenotypes" - ] - ], - "Striatum": [ - [ - "DevStriatum_ILM6.2P3RInv_1111", - "BIDMC/UTHSC Dev Striatum P3 ILMv6.2 (Nov11) RankInv **" - ], - [ - "DevStriatum_ILM6.2P14RInv_1111", - "BIDMC/UTHSC Dev Striatum P14 ILMv6.2 (Nov11) RankInv **" - ], - [ - "KIN_YSM_STR_0711", - "KIN/YSM Human STR Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "OHSU_HS-CC_ILMStr_0211", - "OHSU HS-CC Striatum ILM6v1 (Feb11) RankInv" - ], - [ - "UTHSC_Striatum_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10v2) RankInv" - ], - [ - "UTHSC_Str_RankInv_1210", - "HQF BXD Striatum ILM6.1 (Dec10) RankInv" - ], - [ - "UTHSC_1107_RankInv", - "HQF BXD Striatum ILM6.1 (Nov07) RankInv" - ], - [ - "SA_M2_0905_P", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) PDNN" - ], - [ - "SA_M2_0905_M", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) MAS5" - ], - [ - "SA_M2_0905_R", - "OHSU/VA B6D2F2 Striatum M430v2 (Sep05) RMA" - ], - [ - "SA_M2_0405_MC", - "HBP Rosen Striatum M430V2 (Apr05) MAS5 Clean" - ], - [ - "SA_M2_0405_RC", - "HBP Rosen Striatum M430V2 (Apr05) RMA Clean" - ], - [ - "SA_M2_0405_PC", - "HBP Rosen Striatum M430V2 (Apr05) PDNN Clean" - ], - [ - "SA_M2_0405_SS", - "HBP Rosen Striatum M430V2 (Apr05) SScore" - ], - [ - "SA_M2_0405_RR", - "HBP Rosen Striatum M430V2 (Apr05) RMA Orig" - ], - [ - "Striatum_Exon_0209", - "HQF Striatum Exon (Feb09) RMA" - ], - [ - "DevStriatum_ILM6.2P3RInv_1110", - "BIDMC/UTHSC Dev Striatum P3 ILMv6.2 (Nov10) RankInv **" - ], - [ - "DevStriatum_ILM6.2P14RInv_1110", - "BIDMC/UTHSC Dev Striatum P14 ILMv6.2 (Nov10) RankInv **" - ] - ] - }, - "LXS": { - "Genotypes": [ - [ - "LXSGeno", - "LXS Genotypes" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium M430v2 (Jun06) RMA MDP" - ], - [ - "OXUKHS_ILMHipp_RI0510", - "OX UK HS ILM6v1.1 Hippocampus (May 2010) RankInv" - ], - [ - "INIA_MacFas_Hc_RMA_0110", - "INIA Macaca fasicularis Hippocampus control (Jan10) RMA **" - ], - [ - "INIA_MacFas_He_RMA_0110", - "INIA Macaca fasicularis Hippocampus ethanol (Jan10) RMA **" - ], - [ - "UT_HippRatEx_RMA_0709", - "UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" - ], - [ - "Illum_LXS_Hipp_loess0807", - "Hippocampus Illumina (Aug07) LOESS" - ], - [ - "Illum_LXS_Hipp_loess_nb0807", - "Hippocampus Illumina (Aug07) LOESS_NB" - ], - [ - "Illum_LXS_Hipp_quant0807", - "Hippocampus Illumina (Aug07) QUANT" - ], - [ - "Illum_LXS_Hipp_quant_nb0807", - "Hippocampus Illumina (Aug07) QUANT_NB" - ], - [ - "Illum_LXS_Hipp_rsn0807", - "Hippocampus Illumina (Aug07) RSN" - ], - [ - "Illum_LXS_Hipp_rsn_nb0807", - "Hippocampus Illumina (Aug07) RSN_NB" - ], - [ - "Hipp_Illumina_RankInv_0507", - "Hippocampus Illumina (May07) RankInv" - ], - [ - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Phenotypes": [ - [ - "LXSPublish", - "LXS Published Phenotypes" - ] - ], - "Prefrontal Cortex": [ - [ - "HBTRC-MLPFC_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_N_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent Normal (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_AD_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent AD (Jun11) mlratio" - ], - [ - "HBTRC-MLPFC_HD_0611", - "HBTRC-MLC Human Prefrontal Cortex Agilent HD (Jun11) mlratio" - ], - [ - "INIA_MacFas_Pf_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex control (Jan10) RMA **" - ], - [ - "INIA_MacFas_PfE_RMA_0110", - "INIA Macaca fasicularis Prefrontal Cortex ethanol (Jan10) RMA **" - ], - [ - "VCUSal_1006_R", - "VCU BXD PFC Et vs Sal M430 2.0 (Dec06) Sscore" - ], - [ - "VCUEtOH_1206_R", - "VCU BXD PFC EtOH M430 2.0 (Dec06) RMA" - ], - [ - "VCUSal_1206_R", - "VCU BXD PFC Sal M430 2.0 (Dec06) RMA" - ], - [ - "VCU_PF_Air_0111_R", - "VCU BXD PFC CIE Air M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_Et_0111_R", - "VCU BXD PFC CIE EtOH M430 2.0 (Jan11) RMA **" - ], - [ - "VCU_PF_AvE_0111_Ss", - "VCU BXD PFC EtOH vs CIE Air M430 2.0 (Jan11) Sscore **" - ], - [ - "VCUEt_vs_Sal_0806_R", - "VCU LXS PFC Et vs Sal M430A 2.0 (Aug06) Sscore **" - ], - [ - "VCUEtOH_0806_R", - "VCU LXS PFC EtOH M430A 2.0 (Aug06) RMA **" - ], - [ - "VCUSal_0806_R", - "VCU LXS PFC Sal M430A 2.0 (Aug06) RMA" - ] - ] - }, - "MDP": { - "Genotypes": [ - [ - "MDPGeno", - "MDP Genotypes" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium 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"Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Phenotypes": [ - [ - "MDPPublish", - "Mouse Phenome Database" - ] - ] - }, - "NZBXFVB-N2": { - "Genotypes": [ - [ - "NZBXFVB-N2Geno", - "NZBXFVB-N2 Genotypes" - ] - ], - "Mammary Tumors": [ - [ - "NCI_Mam_Tum_RMA_0409", - "NCI Mammary M430v2 (Apr09) RMA" - ], - [ - "NCI_Agil_Mam_Tum_RMA_0409", - "NCI Mammary LMT miRNA v2 (Apr09) RMA" - ], - [ - "MA_M_0704_R", - "NCI Mammary mRNA M430 (July04) RMA" - ], - [ - "MA_M_0704_M", - "NCI Mammary mRNA M430 (July04) MAS5" - ] - ], - "Phenotypes": [ - [ - "NZBXFVB-N2Publish", - "NZBXFVB-N2 Published Phenotypes" - ] - ] - } - }, - "rat": { - "HXBBXH": { - "Adrenal Gland": [ - [ - "HXB_Adrenal_1208", - "MDC/CAS/UCL Adrenal 230A (Dec08) RMA" - ] - ], - "Genotypes": [ - [ - "HXBBXHGeno", - "HXBBXH Genotypes" - ] - ], - "Heart": [ - [ - "HXB_Heart_1208", - "MDC/CAS/UCL Heart 230_V2 (Dec08) RMA" - ] - ], - "Hippocampus": [ - [ - "KIN_YSM_HIP_0711", - "KIN/YSM Human HIP Affy Hu-Exon 1.0 ST (Jul11) Quantile **" - ], - [ - "UMUTAffyExon_0209_RMA_MDP", - "UMUTAffy Hippocampus Exon (Feb09) RMA MDP" - ], - [ - "HC_M2_0606_MDP", - "Hippocampus Consortium M430v2 (Jun06) RMA MDP" - ], - [ - "OXUKHS_ILMHipp_RI0510", - "OX UK HS ILM6v1.1 Hippocampus (May 2010) RankInv" - ], - [ - "INIA_MacFas_Hc_RMA_0110", - "INIA Macaca fasicularis Hippocampus control (Jan10) RMA **" - ], - [ - "INIA_MacFas_He_RMA_0110", - "INIA Macaca fasicularis Hippocampus ethanol (Jan10) RMA **" - ], - [ - "UT_HippRatEx_RMA_0709", - "UT Hippocampus Affy RaEx 1.0 Exon (Jul09) RMA" - ], - [ - "Illum_LXS_Hipp_loess0807", - "Hippocampus Illumina (Aug07) LOESS" - ], - [ - "Illum_LXS_Hipp_loess_nb0807", - "Hippocampus Illumina (Aug07) LOESS_NB" - ], - [ - "Illum_LXS_Hipp_quant0807", - "Hippocampus Illumina (Aug07) QUANT" - ], - [ - "Illum_LXS_Hipp_quant_nb0807", - "Hippocampus Illumina (Aug07) QUANT_NB" - ], - [ - "Illum_LXS_Hipp_rsn0807", - "Hippocampus Illumina (Aug07) RSN" - ], - [ - "Illum_LXS_Hipp_rsn_nb0807", - "Hippocampus Illumina (Aug07) RSN_NB" - ], - [ - "Hipp_Illumina_RankInv_0507", - "Hippocampus Illumina (May07) RankInv" - ], - [ - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ], - [ - "HC_M2_0606_M", - "Hippocampus Consortium M430v2 (Jun06) MAS5" - ], - [ - "HC_M2_0606_R", - "Hippocampus Consortium M430v2 (Jun06) RMA" - ], - [ - "HC_M2CB_1205_R", - "Hippocampus Consortium M430v2 CXB (Dec05) RMA" - ], - [ - "HC_M2CB_1205_P", - "Hippocampus Consortium M430v2 CXB (Dec05) PDNN" - ], - [ - "UMUTAffyExon_0209_RMA", - "UMUTAffy Hippocampus Exon (Feb09) RMA" - ], - [ - "UT_ILM_BXD_hipp_NON_0909", - "UTHSC Hippocampus Illumina v6.1 NON (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOS_0909", - "UTHSC Hippocampus Illumina v6.1 NOS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_NOE_0909", - "UTHSC Hippocampus Illumina v6.1 NOE (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSS_0909", - "UTHSC Hippocampus Illumina v6.1 RSS (Sep09) RankInv" - ], - [ - "UT_ILM_BXD_hipp_RSE_0909", - "UTHSC Hippocampus Illumina v6.1 RSE (Sep09) RankInv" - ], - [ - "Illum_LXS_Hipp_RSE_1008", - "Hippocampus Illumina RSE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOS_1008", - "Hippocampus Illumina NOS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NOE_1008", - "Hippocampus Illumina NOE (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_RSS_1008", - "Hippocampus Illumina RSS (Oct08) RankInv beta" - ], - [ - "Illum_LXS_Hipp_NON_1008", - "Hippocampus Illumina NON (Oct08) RankInv beta" - ] - ], - "Kidney": [ - [ - "MA_M2F_0706_R", - "Mouse kidney M430v2 Female (Aug06) RMA" - ], - [ - "MA_M2M_0706_R", - "Mouse kidney M430v2 Male (Aug06) RMA" - ], - [ - "MA_M2_0806_R", - "Mouse kidney M430v2 Sex Balanced (Aug06) RMA" - ], - [ - "MA_M2_0806_P", - "Mouse Kidney M430v2 Sex Balanced (Aug06) PDNN" - ], - [ - "MA_M2_0706_P", - "Mouse Kidney M430v2 (Jul06) PDNN" - ], - [ - "MA_M2_0706_R", - "Mouse Kidney M430v2 (Jul06) RMA" - ], - [ - "KI_2A_0405_M", - "MDC/CAS/ICL Kidney 230A (Apr05) MAS5" - ], - [ - "KI_2A_0405_Rz", - "MDC/CAS/ICL Kidney 230A (Apr05) RMA 2z+8" - ], - [ - "KI_2A_0405_R", - "MDC/CAS/ICL Kidney 230A (Apr05) RMA" - ] - ], - "Liver": [ - [ - "GSE16780_UCLA_ML0911", - "GSE16780 UCLA Hybrid MDP Liver Affy HT M430A (Sep11) RMA" - ], - [ - "JAX_CSB_L_0711", - "JAX Liver Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_HF_0711", - "JAX Liver HF Affy M430 2.0 (Jul11) MDP" - ], - [ - "JAX_CSB_L_6C_0711", - "JAX Liver 6C Affy M430 2.0 (Jul11) MDP" - ], - [ - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ], - [ - "HLCM_0311", - "GSE9588 Human Liver Normal (Mar11) Males" - ], - [ - "LV_G_0106_F", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Females" - ], - [ - "LV_G_0106_M", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Males" - ], - [ - "LV_G_0106_B", - "UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes" - ], - [ - "GenEx_BXD_liverSal_RMA_F_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverSal_RMA_M_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverSal_RMA_0211", - "GenEx BXD Sal Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "GenEx_BXD_liverEt_RMA_F_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Females **" - ], - [ - "GenEx_BXD_liverEt_RMA_M_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Males **" - ], - [ - "GenEx_BXD_liverEt_RMA_0211", - "GenEx BXD EtOH Liver Affy M430 2.0 (Feb11) RMA Both Sexes **" - ], - [ - "SUH_Liv_RMA_0611", - "SUH BXD Liver Affy Mouse Gene 1.0 ST (Jun11) RMA **" - ], - [ - "OXUKHS_ILMLiver_RI0510", - "OX UK HS ILM6v1.1 Liver (May 2010) RankInv" - ], - [ - "HXB_Liver_1208", - "MDC/CAS/UCL Liver 230v2 (Dec08) RMA" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_MALE", - "UCLA CTB6B6CTF2 Liver Male mlratio **" - ], - [ - "UCLA_BHF2_LIVER_MALE", - "UCLA BHF2 Liver Male mlratio" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_FEMALE", - "UCLA CTB6B6CTF2 Liver Female mlratio **" - ], - [ - "UCLA_BHHBF2_LIVER_FEMALE", - "UCLA BHHBF2 Liver Female Only" - ], - [ - "UCLA_BHHBF2_LIVER_MALE", - "UCLA BHHBF2 Liver Male Only" - ], - [ - "UCLA_BHF2_LIVER_FEMALE", - "UCLA BHF2 Liver Female mlratio" - ], - [ - "UCLA_BHHBF2_LIVER_2005", - "UCLA BHHBF2 Liver (2005) mlratio **" - ], - [ - "UCLA_CTB6B6CTF2_LIVER_2005", - "UCLA CTB6/B6CTF2 Liver (2005) mlratio **" - ], - [ - "UCLA_BHF2_LIVER_0605", - "UCLA BHF2 Liver (June05) mlratio" - ], - [ - "UCLA_BDF2_LIVER_1999", - "UCLA BDF2 Liver (1999) mlratio" - ], - [ - "LVF2_M_0704_R", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) RMA" - ], - [ - "LVF2_M_0704_M", - "(B6 x BTBR)F2-ob/ob Liver mRNA M430 (Jul04) MAS5" - ], - [ - "HLCF_0311", - "GSE9588 Human Liver Normal (Mar11) Females" - ] - ], - "Peritoneal Fat": [ - [ - "FT_2A_0805_M", - "MDC/CAS/ICL Peritoneal Fat 230A (Aug05) MAS5" - ], - [ - "FT_2A_0605_Rz", - "MDC/CAS/ICL Peritoneal Fat 230A (Jun05) RMA 2z+8" - ] - ], - "Phenotypes": [ - [ - "HXBBXHPublish", - "HXBBXH Published Phenotypes" - ] - ] - }, - "SRxSHRSPF2": { - "Eye": [ - [ - "Eye_AXBXA_1008_RankInv", - "Eye AXBXA Illumina V6.2(Oct08) RankInv Beta" - ], - [ - "Eye_M2_0908_R", - "Eye M430v2 (Sep08) RMA" - ], - [ - "Eye_M2_0908_R_NB", - "Eye M430v2 Mutant Gpnmb (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_ND", - "Eye M430v2 WT Gpnmb (Sep08) RMA **" - ], - [ - "Eye_M2_0908_WTWT", - "Eye M430v2 WT WT (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_WT", - "Eye M430v2 WT Tyrp1 (Sep08) RMA **" - ], - [ - "Eye_M2_0908_R_MT", - "Eye M430v2 Mutant Tyrp1 (Sep08) RMA **" - ], - [ - "BXD_GLA_0911", - "BXD Glaucoma Affy M430 2.0 Trial (Sep11) RMA **" - ], - [ - "UIOWA_Eye_RMA_0906", - "UIOWA Eye mRNA RAE230v2 (Sep06) RMA" - ] - ], - "Genotypes": [ - [ - "SRxSHRSPF2Geno", - "SRxSHRSPF2 Genotypes" - ] - ], - "Phenotypes": [ - [ - "SRxSHRSPF2Publish", - "SRxSHRSPF2 Published Phenotypes" - ] - ] - } - }, - "soybean": { - "J12XJ58F2": { - "Genotypes": [ - [ - "J12XJ58F2Geno", - "J12XJ58F2 Genotypes" - ] - ], - "Phenotypes": [ - [ - "J12XJ58F2Publish", - "J12XJ58F2 Published Phenotypes" - ] - ] - } - }, - "tomato": { - "LXP": { - "Genotypes": [ - [ - "LXPGeno", - "LXP Genotypes" - ] - ], - "Phenotypes": [ - [ - "LXPPublish", - "LXP Published Phenotypes" - ] - ] - } - } - }, - "groups": { - "All Species": [ - [ - "All Groups", - "All Groups" - ] - ], - "arabidopsis": [ - [ - "BayXSha", - "BayXSha" - ], - [ - "ColXBur", - "ColXBur" - ], - [ - "ColXCvi", - "ColXCvi" - ] - ], - "barley": [ - [ - "QSM", - "QSM" - ], - [ - "SXM", - "SXM" - ] - ], - "drosophila": [ - [ - "DGRP", - "Drosophila Genetic Reference Panel" - ], - [ - "Oregon-R_x_2b3", - "Oregon-R x 2b3" - ] - ], - "human": [ - [ - "AD-cases-controls", - "AD Cases & Controls (Liang)" - ], - [ - "AD-cases-controls-Myers", - "AD Cases & Controls (Myers)" - ], - [ - "CANDLE", - "CANDLE" - ], - [ - "CEPH-2004", - "CEPH Families" - ], - [ - "HB", - "Harvard Brain Tissue Resource Center" - ], - [ - "HLC", - "Human Liver Cohort" - ], - [ - "HSB", - "KIN/YSM" - ] - ], - "macaque monkey": [ - [ - "Macaca-fasicularis", - "Macaca fasicularis (Cynomolgus monkey)" - ] - ], - "mouse": [ - [ - "AKXD", - "AKXD" - ], - [ - "AXBXA", - "AXB/BXA" - ], - [ - "B6BTBRF2", - "B6BTBRF2" - ], - [ - "B6D2F2", - "B6D2F2" - ], - [ - "BDF2-1999", - "BDF2 UCLA" - ], - [ - "BDF2-2005", - "BDF2-2005" - ], - [ - "BHF2", - "BHF2 (Apoe Null) UCLA" - ], - [ - "BHHBF2", - "BH/HB F2 UCLA" - ], - [ - "BXD", - "BXD" - ], - [ - "BXH", - "BXH" - ], - [ - "CTB6F2", - "CastB6/B6Cast F2 UCLA" - ], - [ - "CXB", - "CXB" - ], - [ - "HS", - "Heterogeneous Stock" - ], - [ - "HS-CC", - "Heterogeneous Stock Collaborative Cross" - ], - [ - "LXS", - "LXS" - ], - [ - "MDP", - "Mouse Diversity Panel" - ], - [ - "NZBXFVB-N2", - "NZB/FVB N2 NCI" - ] - ], - "rat": [ - [ - "HXBBXH", - "HXB/BXH" - ], - [ - "SRxSHRSPF2", - "UIOWA SRxSHRSP F2" - ] - ], - "soybean": [ - [ - "J12XJ58F2", - "J12XJ58F2" - ] - ], - "tomato": [ - [ - "LXP", - "LXP" - ] - ] - }, - "species": [ - [ - "human", - "Human" - ], - [ - "macaque monkey", - "Macaque monkey" - ], - [ - "mouse", - "Mouse" - ], - [ - "rat", - "Rat" - ], - [ - "drosophila", - "Drosophila" - ], - [ - "arabidopsis", - "Arabidopsis thaliana" - ], - [ - "barley", - "Barley" - ], - [ - "soybean", - "Soybean" - ], - [ - "tomato", - "Tomato" - ], - [ - "All Species", - "All Species" - ] - ], - "types": { - "All Species": { - "All Groups": [ - [ - "Phenotypes", - "Phenotypes" - ] - ] - }, - "arabidopsis": { - "BayXSha": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ] - ], - "ColXBur": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ] - ], - "ColXCvi": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ] - ] - }, - "barley": { - "QSM": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Leaf", - "Leaf mRNA" - ] - ], - "SXM": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Embryo", - "Embryo mRNA" - ], - [ - "Leaf", - "Leaf mRNA" - ] - ] - }, - "drosophila": { - "DGRP": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Whole Body", - "Whole Body mRNA" - ] - ], - "Oregon-R_x_2b3": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Whole Body", - "Whole Body mRNA" - ] - ] - }, - "human": { - "AD-cases-controls": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Brain", - "Brain mRNA" - ] - ], - "AD-cases-controls-Myers": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Brain", - "Brain mRNA" - ] - ], - "CANDLE": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Newborn Cord Blood", - "Newborn Cord Blood mRNA" - ] - ], - "CEPH-2004": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Lymphoblast B-cell", - "Lymphoblast B-cell mRNA" - ] - ], - "HB": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Cerebellum", - "Cerebellum mRNA" - ], - [ - "Prefrontal Cortex", - "Prefrontal Cortex mRNA" - ], - [ - "Primary Visual Cortex", - "Primary Visual Cortex mRNA" - ] - ], - "HLC": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Liver", - "Liver mRNA" - ] - ], - "HSB": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Amygdala", - "Amygdala mRNA" - ], - [ - "Caudal Ganglionic Eminence", - "Caudal Ganglionic Eminence mRNA" - ], - [ - "Cerebellar Cortex", - "Cerebellar Cortex mRNA" - ], - [ - "Diencephalon", - "Diencephalon mRNA" - ], - [ - "Dorsal Thalamus", - "Dorsal Thalamus mRNA" - ], - [ - "Dorsolateral Prefrontal Cortex", - "Dorsolateral Prefrontal Cortex mRNA" - ], - [ - "Frontal Cerebral Wall", - "Frontal Cerebral Wall mRNA" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Inferior Temporal Cortex", - "Inferior Temporal Cortex mRNA" - ], - [ - "Lateral Ganglionic Eminence", - "Lateral Ganglionic Eminence mRNA" - ], - [ - "Medial Ganglionic Eminence", - "Medial Ganglionic Eminence mRNA" - ], - [ - "Medial Prefrontal Cortex", - "Medial Prefrontal Cortex mRNA" - ], - [ - "Mediodorsal Nucleus of Thalamus", - "Mediodorsal Nucleus of Thalamus mRNA" - ], - [ - "Occipital Cerebral Wall", - "Occipital Cerebral Wall mRNA" - ], - [ - "Orbital Prefrontal Cortex", - "Orbital Prefrontal Cortex mRNA" - ], - [ - "Parietal Cerebral Wall", - "Parietal Cerebral Wall mRNA" - ], - [ - "Posterior Inferior Parietal Cortex", - "Posterior Inferior Parietal Cortex mRNA" - ], - [ - "Posterior Superior Temporal Cortex", - "Posterior Superior Temporal Cortex mRNA" - ], - [ - "Primary Auditory (A1) Cortex", - "Primary Auditory (A1) Cortex mRNA" - ], - [ - "Primary Motor (M1) Cortex", - "Primary Motor (M1) Cortex mRNA" - ], - [ - "Primary Somatosensory (S1) Cortex", - "Primary Somatosensory (S1) Cortex mRNA" - ], - [ - "Primary Visual Cortex", - "Primary Visual Cortex mRNA" - ], - [ - "Striatum", - "Striatum mRNA" - ], - [ - "Temporal Cerebral Wall", - "Temporal Cerebral Wall mRNA" - ], - [ - "Upper (Rostral) Rhombic Lip", - "Upper (Rostral) Rhombic Lip mRNA" - ], - [ - "Ventral Forebrain", - "Ventral Forebrain mRNA" - ], - [ - "Ventrolateral Prefrontal Cortex", - "Ventrolateral Prefrontal Cortex mRNA" - ] - ] - }, - "macaque monkey": { - "Macaca-fasicularis": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Amygdala", - "Amygdala mRNA" - ], - [ - "Brain", - "Brain mRNA" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Nucleus Accumbens", - "Nucleus Accumbens mRNA" - ], - [ - "Prefrontal Cortex", - "Prefrontal Cortex mRNA" - ] - ] - }, - "mouse": { - "AKXD": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Mammary Tumors", - "Mammary Tumors mRNA" - ] - ], - "AXBXA": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Eye", - "Eye mRNA" - ] - ], - "B6BTBRF2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Liver", - "Liver mRNA" - ] - ], - "B6D2F2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Brain", - "Brain mRNA" - ] - ], - "BDF2-1999": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Liver", - "Liver mRNA" - ] - ], - "BDF2-2005": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Striatum", - "Striatum mRNA" - ] - ], - "BHF2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Adipose", - "Adipose mRNA" - ], - [ - "Brain", - "Brain mRNA" - ], - [ - "Liver", - "Liver mRNA" - ], - [ - "Muscle", - "Muscle mRNA" - ] - ], - "BHHBF2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Adipose", - "Adipose mRNA" - ], - [ - "Brain", - "Brain mRNA" - ], - [ - "Liver", - "Liver mRNA" - ], - [ - "Muscle", - "Muscle mRNA" - ] - ], - "BXD": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Amygdala", - "Amygdala mRNA" - ], - [ - "Brain", - "Brain mRNA" - ], - [ - "Cartilage", - "Cartilage mRNA" - ], - [ - "Cerebellum", - "Cerebellum mRNA" - ], - [ - "Eye", - "Eye mRNA" - ], - [ - "Hematopoietic Cells", - "Hematopoietic Cells mRNA" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Hypothalamus", - "Hypothalamus mRNA" - ], - [ - "Kidney", - "Kidney mRNA" - ], - [ - "Leucocytes", - "Leucocytes mRNA" - ], - [ - "Liver", - "Liver mRNA" - ], - [ - "Lung", - "Lung mRNA" - ], - [ - "Midbrain", - "Midbrain mRNA" - ], - [ - "Muscle", - "Muscle mRNA" - ], - [ - "Neocortex", - "Neocortex mRNA" - ], - [ - "Nucleus Accumbens", - "Nucleus Accumbens mRNA" - ], - [ - "Prefrontal Cortex", - "Prefrontal Cortex mRNA" - ], - [ - "Retina", - "Retina mRNA" - ], - [ - "Spleen", - "Spleen mRNA" - ], - [ - "Striatum", - "Striatum mRNA" - ], - [ - "T Cell (helper)", - "T Cell (helper) mRNA" - ], - [ - "T Cell (regulatory)", - "T Cell (regulatory) mRNA" - ], - [ - "Thymus", - "Thymus mRNA" - ], - [ - "Ventral Tegmental Area", - "Ventral Tegmental Area mRNA" - ] - ], - "BXH": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Cartilage", - "Cartilage mRNA" - ] - ], - "CTB6F2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Adipose", - "Adipose mRNA" - ], - [ - "Brain", - "Brain mRNA" - ], - [ - "Liver", - "Liver mRNA" - ], - [ - "Muscle", - "Muscle mRNA" - ] - ], - "CXB": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Spleen", - "Spleen mRNA" - ] - ], - "HS": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Liver", - "Liver mRNA" - ], - [ - "Lung", - "Lung mRNA" - ] - ], - "HS-CC": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Striatum", - "Striatum mRNA" - ] - ], - "LXS": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Prefrontal Cortex", - "Prefrontal Cortex mRNA" - ] - ], - "MDP": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Liver", - "Liver mRNA" - ] - ], - "NZBXFVB-N2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Mammary Tumors", - "Mammary Tumors mRNA" - ] - ] - }, - "rat": { - "HXBBXH": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Adrenal Gland", - "Adrenal Gland mRNA" - ], - [ - "Heart", - "Heart mRNA" - ], - [ - "Hippocampus", - "Hippocampus mRNA" - ], - [ - "Kidney", - "Kidney mRNA" - ], - [ - "Liver", - "Liver mRNA" - ], - [ - "Peritoneal Fat", - "Peritoneal Fat mRNA" - ] - ], - "SRxSHRSPF2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ], - [ - "Eye", - "Eye mRNA" - ] - ] - }, - "soybean": { - "J12XJ58F2": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ] - ] - }, - "tomato": { - "LXP": [ - [ - "Phenotypes", - "Phenotypes" - ], - [ - "Genotypes", - "Genotypes" - ] - ] - } - } -} \ No newline at end of file diff --git a/wqflask/wqflask/static/new/javascript/dataset_select_menu.coffee b/wqflask/wqflask/static/new/javascript/dataset_select_menu.coffee index e2f11845..c2a9b11d 100644 --- a/wqflask/wqflask/static/new/javascript/dataset_select_menu.coffee +++ b/wqflask/wqflask/static/new/javascript/dataset_select_menu.coffee @@ -1,393 +1,449 @@ -# -#* function: based on different browser use, will have different initial actions; -#* Once the index.html page is loaded, this function will be called -# - $ -> - sArr = window.sArr # species - gArr = window.gArr # group - tArr = window.tArr - dArr = window.dArr - lArr = window.lArr - - console.log("sArr is now [jersey]:", sArr) - console.log("gArr is now [jersey]:", gArr) - - initialDatasetSelection = -> - defaultSpecies = getDefaultValue("species") - defaultSet = getDefaultValue("cross") - defaultType = getDefaultValue("tissue") - defaultDB = getDefaultValue("database") - if navigator.userAgent.indexOf("MSIE") >= 0 - sOptions = fillOptionsForIE(null, defaultSpecies) - menu0 = "" - document.getElementById("menu0").innerHTML = menu0 - gOptions = fillOptionsForIE("species", defaultSet) - menu1 = "" - document.getElementById("menu1").innerHTML = menu1 - tOptions = fillOptionsForIE("cross", defaultType) - menu2 = "" - document.getElementById("menu2").innerHTML = menu2 - dOptions = fillOptionsForIE("tissue", defaultDB) - menu3 = "" - document.getElementById("menu3").innerHTML = menu3 - else - fillOptions null - searchtip() - - - # - #* input: selectObjId (designated select menu, such as species, cross, etc... ) - #* defaultValue (default Value of species, cross,tissue or database) - #* function: special for IE browser,setting options value for select menu dynamically based on linkage array(lArr), - #* output: options string - # - fillOptionsForIE = (selectObjId, defaultValue) -> - options = "" - unless selectObjId? - len = sArr.length - i = 1 - - while i < len - - # setting Species' option - if sArr[i].val is defaultValue - options = options + "" - else - options = options + "" - i++ - else if selectObjId is "species" - speciesObj = document.getElementById("species") - len = lArr.length - arr = [] - idx = 0 - i = 1 - - while i < len - - #get group(cross) info from lArr - arr[idx++] = lArr[i][1] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and not Contains(arr, lArr[i][1]) - i++ - idx = 0 - len = arr.length - removeOptions "cross" - i = 0 - - while i < len - - # setting Group's option - if gArr[arr[i]].val is defaultValue - options = options + "" - else - options = options + "" - i++ - else if selectObjId is "cross" - speciesObj = document.getElementById("species") - groupObj = document.getElementById("cross") - len = lArr.length - arr = [] - idx = 0 - i = 1 - - while i < len - - #get type(tissue) info from lArr - arr[idx++] = lArr[i][2] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and not Contains(arr, lArr[i][2]) - i++ - idx = 0 - len = arr.length - removeOptions "tissue" - i = 0 - - while i < len - - # setting Type's option - if tArr[arr[i]].val is defaultValue - options = options + "" - else - options = options + "" - i++ - else if selectObjId is "tissue" - speciesObj = document.getElementById("species") - groupObj = document.getElementById("cross") - typeObj = document.getElementById("tissue") - len = lArr.length - arr = [] - idx = 0 - i = 1 - - while i < len - - #get dataset(database) info from lArr - arr[idx++] = lArr[i][3] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and lArr[i][2] is (getIndexByValue("tissue", typeObj.value)).toString() and not Contains(arr, lArr[i][3]) - i++ - idx = 0 - len = arr.length - removeOptions "database" - i = 0 - - while i < len - - # setting Database's option - if dArr[arr[i]].val is defaultValue - options = options + "" - else - options = options + "" - i++ - options - - # - #* input: selectObjId (designated select menu, such as species, cross, etc... ) - #* function: setting options value for select menu dynamically based on linkage array(lArr) - #* output: null - # - fillOptions = (selectObjId) -> - console.log("[vacuum] selectObjId:", selectObjId) - unless selectObjId? - speciesObj = document.getElementById("species") - console.log("speciesObj:", speciesObj) - len = sArr.length - i = 1 - - while i < len - - # setting Species' option - speciesObj.options[i - 1] = new Option(sArr[i].txt, sArr[i].val) - console.log("speciesObj.options:", speciesObj.options[i - 1]) - i++ - updateChoice "species" - else if selectObjId is "species" - speciesObj = document.getElementById("species") - console.log("speciesObj:", speciesObj) - groupObj = document.getElementById("cross") - console.log("groupObj:", groupObj) - len = lArr.length - arr = [] - idx = 0 - i = 1 - while i < len - #get group(cross) info from lArr - index_value = getIndexByValue("species", speciesObj.value).toString() - if lArr[i][0] is (index_value and not Contains(arr, lArr[i][1])) - arr[idx++] = lArr[i][1] - i++ - idx = 0 - len = arr.length - removeOptions "cross" - i = 0 - - while i < len - # setting Group's option - groupObj.options[idx++] = new Option(gArr[arr[i]].txt, gArr[arr[i]].val) - i++ - updateChoice "cross" - else if selectObjId is "cross" - speciesObj = document.getElementById("species") - groupObj = document.getElementById("cross") - typeObj = document.getElementById("tissue") - len = lArr.length - arr = [] - idx = 0 - i = 1 - - while i < len - - #get type(tissue) info from lArr - arr[idx++] = lArr[i][2] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and not Contains(arr, lArr[i][2]) - i++ - idx = 0 - len = arr.length - removeOptions "tissue" - i = 0 - - while i < len - - # setting Type's option - typeObj.options[idx++] = new Option(tArr[arr[i]].txt, tArr[arr[i]].val) - i++ - updateChoice "tissue" - else if selectObjId is "tissue" - speciesObj = document.getElementById("species") - groupObj = document.getElementById("cross") - typeObj = document.getElementById("tissue") - databaseObj = document.getElementById("database") - len = lArr.length - arr = [] - idx = 0 - i = 1 - - while i < len - - #get dataset(database) info from lArr - arr[idx++] = lArr[i][3] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and lArr[i][2] is (getIndexByValue("tissue", typeObj.value)).toString() and not Contains(arr, lArr[i][3]) - i++ - idx = 0 - len = arr.length - removeOptions "database" - i = 0 - - while i < len - - # setting Database's option - databaseObj.options[idx++] = new Option(dArr[arr[i]].txt, dArr[arr[i]].val) - i++ - updateChoice "database" - - # - #* input: arr (targeted array); obj (targeted value) - #* function: check whether targeted array contains targeted value or not - #* output: return true, if array contains targeted value, otherwise return false - # - Contains = (arr, obj) -> - i = arr.length - return true if arr[i] is obj while i-- - false - - # - #* input: selectObj (designated select menu, such as species, cross, etc... ) - #* function: clear designated select menu's option - #* output: null - # - removeOptions = (selectObj) -> - selectObj = document.getElementById(selectObj) unless typeof selectObj is "object" - len = selectObj.options.length - i = 0 - - while i < len - - # clear current selection - selectObj.options[0] = null - i++ - - # - #* input: selectObjId (designated select menu, such as species, cross, etc... ) - #* Value: target value - #* function: retrieve Index info of target value in designated array - #* output: index info - # - getIndexByValue = (selectObjId, val) -> - if selectObjId is "species" - i = 1 - - while i < sArr.length - return i if sArr[i].val is val - i++ - else if selectObjId is "cross" - i = 1 - - while i < gArr.length - return i if gArr[i].val is val - i++ - else if selectObjId is "tissue" - i = 1 - - while i < tArr.length - return i if tArr[i].val is val - i++ - else - return - - # - #* input: objId (designated select menu, such as species, cross, etc... ) - #* val(targeted value) - #* function: setting option's selected status for designated select menu based on target value, also update the following select menu in the main search page - #* output: return true if selected status has been set, otherwise return false. - # - setChoice = (objId, val) -> - console.log("objId:", objId) - console.log("val:", val) - Obj = document.getElementById(objId) - console.log("Obj:", Obj) - idx = -1 - i = 0 - while i < Obj.options.length - if Obj.options[i].value is val - idx = i - break - i++ - if idx >= 0 + process_json = (data) -> + window.jdata = data + populate_species() + + $.ajax '/static/new/javascript/dataset_menu_structure', + dataType: 'json' + success: process_json + + populate_species = -> + species_list = @jdata.species + redo_dropdown($('#species'), species_list) + populate_groups() + + populate_groups = -> + species = $('#species').val() + group_list = @jdata.groups[species] + redo_dropdown($('#group'), group_list) + populate_types() + + populate_types = -> + species = $('#species').val() + group = $('#group').val() + type_list = @jdata.types[species][group] + redo_dropdown($('#type'), type_list) + populate_datasets() + + populate_datasets = -> + species = $('#species').val() + group = $('#group').val() + type = $('#type').val() + dataset_list = @jdata.datasets[species][group][type] + redo_dropdown($('#dataset'), dataset_list) - #setting option's selected status - Obj.options[idx].selected = true - - #update the following select menu - fillOptions objId - else - Obj.options[0].selected = true - fillOptions objId - - # setting option's selected status based on default setting or cookie setting for Species, Group, Type and Database select menu in the main search page http://www.genenetwork.org/ - updateChoice = (selectObjId) -> - if selectObjId is "species" - defaultSpecies = getDefaultValue("species") - - #setting option's selected status - setChoice "species", defaultSpecies - else if selectObjId is "cross" - defaultSet = getDefaultValue("cross") - - #setting option's selected status - setChoice "cross", defaultSet - else if selectObjId is "tissue" - defaultType = getDefaultValue("tissue") - - #setting option's selected status - setChoice "tissue", defaultType - else if selectObjId is "database" - defaultDB = getDefaultValue("database") - - #setting option's selected status - setChoice "database", defaultDB - - #get default value;if cookie exists, then use cookie value, otherwise use default value - getDefaultValue = (selectObjId) -> + redo_dropdown = (dropdown, items) -> + dropdown.empty() + for item in items + dropdown.append($("" +# else +# options = options + "" +# i++ +# else if selectObjId is "species" +# speciesObj = document.getElementById("species") +# len = lArr.length +# arr = [] +# idx = 0 +# i = 1 +# +# while i < len +# +# #get group(cross) info from lArr +# arr[idx++] = lArr[i][1] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and not Contains(arr, lArr[i][1]) +# i++ +# idx = 0 +# len = arr.length +# removeOptions "cross" +# i = 0 +# +# while i < len +# +# # setting Group's option +# if gArr[arr[i]].val is defaultValue +# options = options + "" +# else +# options = options + "" +# i++ +# else if selectObjId is "cross" +# speciesObj = document.getElementById("species") +# groupObj = document.getElementById("cross") +# len = lArr.length +# arr = [] +# idx = 0 +# i = 1 +# +# while i < len +# +# #get type(tissue) info from lArr +# arr[idx++] = lArr[i][2] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and not Contains(arr, lArr[i][2]) +# i++ +# idx = 0 +# len = arr.length +# removeOptions "tissue" +# i = 0 +# +# while i < len +# +# # setting Type's option +# if tArr[arr[i]].val is defaultValue +# options = options + "" +# else +# options = options + "" +# i++ +# else if selectObjId is "tissue" +# speciesObj = document.getElementById("species") +# groupObj = document.getElementById("cross") +# typeObj = document.getElementById("tissue") +# len = lArr.length +# arr = [] +# idx = 0 +# i = 1 +# +# while i < len +# +# #get dataset(database) info from lArr +# arr[idx++] = lArr[i][3] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and lArr[i][2] is (getIndexByValue("tissue", typeObj.value)).toString() and not Contains(arr, lArr[i][3]) +# i++ +# idx = 0 +# len = arr.length +# removeOptions "database" +# i = 0 +# +# while i < len +# +# # setting Database's option +# if dArr[arr[i]].val is defaultValue +# options = options + "" +# else +# options = options + "" +# i++ +# options +# +# # +# #* input: selectObjId (designated select menu, such as species, cross, etc... ) +# #* function: setting options value for select menu dynamically based on linkage array(lArr) +# #* output: null +# # +# fillOptions = (selectObjId) -> +# console.log("[vacuum] selectObjId:", selectObjId) +# unless selectObjId? +# speciesObj = document.getElementById("species") +# console.log("speciesObj:", speciesObj) +# len = sArr.length +# i = 1 +# +# while i < len +# +# # setting Species' option +# speciesObj.options[i - 1] = new Option(sArr[i].txt, sArr[i].val) +# console.log("speciesObj.options:", speciesObj.options[i - 1]) +# i++ +# updateChoice "species" +# else if selectObjId is "species" +# speciesObj = document.getElementById("species") +# console.log("speciesObj:", speciesObj) +# groupObj = document.getElementById("cross") +# console.log("groupObj:", groupObj) +# len = lArr.length +# arr = [] +# idx = 0 +# i = 1 +# +# while i < len +# #get group(cross) info from lArr +# index_value = getIndexByValue("species", speciesObj.value).toString() +# if lArr[i][0] is (index_value and not Contains(arr, lArr[i][1])) +# arr[idx++] = lArr[i][1] +# i++ +# idx = 0 +# len = arr.length +# removeOptions "cross" +# i = 0 +# +# while i < len +# # setting Group's option +# groupObj.options[idx++] = new Option(gArr[arr[i]].txt, gArr[arr[i]].val) +# i++ +# updateChoice "cross" +# else if selectObjId is "cross" +# speciesObj = document.getElementById("species") +# groupObj = document.getElementById("cross") +# typeObj = document.getElementById("tissue") +# len = lArr.length +# arr = [] +# idx = 0 +# i = 1 +# +# while i < len +# +# #get type(tissue) info from lArr +# arr[idx++] = lArr[i][2] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and not Contains(arr, lArr[i][2]) +# i++ +# idx = 0 +# len = arr.length +# removeOptions "tissue" +# i = 0 +# +# while i < len +# +# # setting Type's option +# typeObj.options[idx++] = new Option(tArr[arr[i]].txt, tArr[arr[i]].val) +# i++ +# updateChoice "tissue" +# else if selectObjId is "tissue" +# speciesObj = document.getElementById("species") +# groupObj = document.getElementById("cross") +# typeObj = document.getElementById("tissue") +# databaseObj = document.getElementById("database") +# len = lArr.length +# arr = [] +# idx = 0 +# i = 1 +# +# while i < len +# +# #get dataset(database) info from lArr +# arr[idx++] = lArr[i][3] if lArr[i][0] is (getIndexByValue("species", speciesObj.value)).toString() and lArr[i][1] is (getIndexByValue("cross", groupObj.value)).toString() and lArr[i][2] is (getIndexByValue("tissue", typeObj.value)).toString() and not Contains(arr, lArr[i][3]) +# i++ +# idx = 0 +# len = arr.length +# removeOptions "database" +# i = 0 +# +# while i < len +# +# # setting Database's option +# databaseObj.options[idx++] = new Option(dArr[arr[i]].txt, dArr[arr[i]].val) +# i++ +# updateChoice "database" +# +# # +# #* input: arr (targeted array); obj (targeted value) +# #* function: check whether targeted array contains targeted value or not +# #* output: return true, if array contains targeted value, otherwise return false +# # +# Contains = (arr, obj) -> +# i = arr.length +# return true if arr[i] is obj while i-- +# false +# +# # +# #* input: selectObj (designated select menu, such as species, cross, etc... ) +# #* function: clear designated select menu's option +# #* output: null +# # +# removeOptions = (selectObj) -> +# selectObj = document.getElementById(selectObj) unless typeof selectObj is "object" +# len = selectObj.options.length +# i = 0 +# +# while i < len +# +# # clear current selection +# selectObj.options[0] = null +# i++ +# +# # +# #* input: selectObjId (designated select menu, such as species, cross, etc... ) +# #* Value: target value +# #* function: retrieve Index info of target value in designated array +# #* output: index info +# # +# getIndexByValue = (selectObjId, val) -> +# if selectObjId is "species" +# i = 1 +# +# while i < sArr.length +# return i if sArr[i].val is val +# i++ +# else if selectObjId is "cross" +# i = 1 +# +# while i < gArr.length +# return i if gArr[i].val is val +# i++ +# else if selectObjId is "tissue" +# i = 1 +# +# while i < tArr.length +# return i if tArr[i].val is val +# i++ +# else +# return +# +# # +# #* input: objId (designated select menu, such as species, cross, etc... ) +# #* val(targeted value) +# #* function: setting option's selected status for designated select menu based on target value, also update the following select menu in the main search page +# #* output: return true if selected status has been set, otherwise return false. +# # +# setChoice = (objId, val) -> +# console.log("objId:", objId) +# console.log("val:", val) +# Obj = document.getElementById(objId) +# console.log("Obj:", Obj) +# idx = -1 +# i = 0 +# while i < Obj.options.length +# if Obj.options[i].value is val +# idx = i +# break +# i++ +# if idx >= 0 +# +# #setting option's selected status +# Obj.options[idx].selected = true +# +# #update the following select menu +# fillOptions objId +# else +# Obj.options[0].selected = true +# fillOptions objId +# +# # setting option's selected status based on default setting or cookie setting for Species, Group, Type and Database select menu in the main search page http://www.genenetwork.org/ +# updateChoice = (selectObjId) -> +# if selectObjId is "species" +# defaultSpecies = getDefaultValue("species") +# +# #setting option's selected status +# setChoice "species", defaultSpecies +# else if selectObjId is "cross" +# defaultSet = getDefaultValue("cross") +# +# #setting option's selected status +# setChoice "cross", defaultSet +# else if selectObjId is "tissue" +# defaultType = getDefaultValue("tissue") +# +# #setting option's selected status +# setChoice "tissue", defaultType +# else if selectObjId is "database" +# defaultDB = getDefaultValue("database") +# +# #setting option's selected status +# setChoice "database", defaultDB +# +# #get default value;if cookie exists, then use cookie value, otherwise use default value +# getDefaultValue = (selectObjId) -> +# +# #define default value +# defaultSpecies = "mouse" +# defaultSet = "BXD" +# defaultType = "Hippocampus" +# defaultDB = "HC_M2_0606_P" +# if selectObjId is "species" +# +# #if cookie exists, then use cookie value, otherwise use default value +# cookieSpecies = getCookie("defaultSpecies") +# defaultSpecies = cookieSpecies if cookieSpecies +# defaultSpecies +# else if selectObjId is "cross" +# cookieSet = getCookie("defaultSet") +# defaultSet = cookieSet if cookieSet +# defaultSet +# else if selectObjId is "tissue" +# cookieType = getCookie("defaultType") +# defaultType = cookieType if cookieType +# defaultType +# else if selectObjId is "database" +# cookieDB = getCookie("defaultDB") +# defaultDB = cookieDB if cookieDB +# defaultDB +# +# #setting default value into cookies for the dropdown menus: Species,Group, Type, and Database +# setDefault = (thisform) -> +# setCookie "cookieTest", "cookieTest", 1 +# cookieTest = getCookie("cookieTest") +# delCookie "cookieTest" +# if cookieTest +# defaultSpecies = thisform.species.value +# setCookie "defaultSpecies", defaultSpecies, 10 +# defaultSet = thisform.cross.value +# setCookie "defaultSet", defaultSet, 10 +# defaultType = thisform.tissue.value +# setCookie "defaultType", defaultType, 10 +# defaultDB = thisform.database.value +# setCookie "defaultDB", defaultDB, 10 +# updateChoice "species" +# updateChoice "cross" +# updateChoice "tissue" +# updateChoice "database" +# alert "The current settings are now your default" +# else +# alert "You need to enable Cookies in your browser." +# +# # run it +# initialDatasetSelection() \ No newline at end of file diff --git a/wqflask/wqflask/static/new/javascript/dataset_select_menu.js b/wqflask/wqflask/static/new/javascript/dataset_select_menu.js index eb04839c..8f684f6a 100644 --- a/wqflask/wqflask/static/new/javascript/dataset_select_menu.js +++ b/wqflask/wqflask/static/new/javascript/dataset_select_menu.js @@ -2,372 +2,64 @@ (function() { $(function() { - var Contains, dArr, fillOptions, fillOptionsForIE, gArr, getDefaultValue, getIndexByValue, initialDatasetSelection, lArr, removeOptions, sArr, setChoice, setDefault, tArr, updateChoice; - sArr = window.sArr; - gArr = window.gArr; - tArr = window.tArr; - dArr = window.dArr; - lArr = window.lArr; - console.log("sArr is now [jersey]:", sArr); - console.log("gArr is now [jersey]:", gArr); - initialDatasetSelection = function() { - var dOptions, defaultDB, defaultSet, defaultSpecies, defaultType, gOptions, menu0, menu1, menu2, menu3, sOptions, tOptions; - defaultSpecies = getDefaultValue("species"); - defaultSet = getDefaultValue("cross"); - defaultType = getDefaultValue("tissue"); - defaultDB = getDefaultValue("database"); - if (navigator.userAgent.indexOf("MSIE") >= 0) { - sOptions = fillOptionsForIE(null, defaultSpecies); - menu0 = ""; - document.getElementById("menu0").innerHTML = menu0; - gOptions = fillOptionsForIE("species", defaultSet); - menu1 = ""; - document.getElementById("menu1").innerHTML = menu1; - tOptions = fillOptionsForIE("cross", defaultType); - menu2 = ""; - document.getElementById("menu2").innerHTML = menu2; - dOptions = fillOptionsForIE("tissue", defaultDB); - menu3 = ""; - document.getElementById("menu3").innerHTML = menu3; - } else { - fillOptions(null); - } - return searchtip(); + var populate_datasets, populate_groups, populate_species, populate_types, process_json, redo_dropdown, + _this = this; + process_json = function(data) { + window.jdata = data; + return populate_species(); }; - fillOptionsForIE = function(selectObjId, defaultValue) { - var arr, groupObj, i, idx, len, options, speciesObj, typeObj; - options = ""; - if (selectObjId == null) { - len = sArr.length; - i = 1; - while (i < len) { - if (sArr[i].val === defaultValue) { - options = options + ""; - } else { - options = options + ""; - } - i++; - } - } else if (selectObjId === "species") { - speciesObj = document.getElementById("species"); - len = lArr.length; - arr = []; - idx = 0; - i = 1; - while (i < len) { - if (lArr[i][0] === (getIndexByValue("species", speciesObj.value)).toString() && !Contains(arr, lArr[i][1])) { - arr[idx++] = lArr[i][1]; - } - i++; - } - idx = 0; - len = arr.length; - removeOptions("cross"); - i = 0; - while (i < len) { - if (gArr[arr[i]].val === defaultValue) { - options = options + ""; - } else { - options = options + ""; - } - i++; - } - } else if (selectObjId === "cross") { - speciesObj = document.getElementById("species"); - groupObj = document.getElementById("cross"); - len = lArr.length; - arr = []; - idx = 0; - i = 1; - while (i < len) { - if (lArr[i][0] === (getIndexByValue("species", speciesObj.value)).toString() && lArr[i][1] === (getIndexByValue("cross", groupObj.value)).toString() && !Contains(arr, lArr[i][2])) { - arr[idx++] = lArr[i][2]; - } - i++; - } - idx = 0; - len = arr.length; - removeOptions("tissue"); - i = 0; - while (i < len) { - if (tArr[arr[i]].val === defaultValue) { - options = options + ""; - } else { - options = options + ""; - } - i++; - } - } else if (selectObjId === "tissue") { - speciesObj = document.getElementById("species"); - groupObj = document.getElementById("cross"); - typeObj = document.getElementById("tissue"); - len = lArr.length; - arr = []; - idx = 0; - i = 1; - while (i < len) { - if (lArr[i][0] === (getIndexByValue("species", speciesObj.value)).toString() && lArr[i][1] === (getIndexByValue("cross", groupObj.value)).toString() && lArr[i][2] === (getIndexByValue("tissue", typeObj.value)).toString() && !Contains(arr, lArr[i][3])) { - arr[idx++] = lArr[i][3]; - } - i++; - } - idx = 0; - len = arr.length; - removeOptions("database"); - i = 0; - while (i < len) { - if (dArr[arr[i]].val === defaultValue) { - options = options + ""; - } else { - options = options + ""; - } - i++; - } - } - return options; + $.ajax('/static/new/javascript/dataset_menu_structure', { + dataType: 'json', + success: process_json + }); + populate_species = function() { + var species_list; + species_list = this.jdata.species; + redo_dropdown($('#species'), species_list); + return populate_groups(); }; - fillOptions = function(selectObjId) { - var arr, databaseObj, groupObj, i, idx, index_value, len, speciesObj, typeObj; - console.log("[vacuum] selectObjId:", selectObjId); - if (selectObjId == null) { - speciesObj = document.getElementById("species"); - console.log("speciesObj:", speciesObj); - len = sArr.length; - i = 1; - while (i < len) { - speciesObj.options[i - 1] = new Option(sArr[i].txt, sArr[i].val); - console.log("speciesObj.options:", speciesObj.options[i - 1]); - i++; - } - return updateChoice("species"); - } else if (selectObjId === "species") { - speciesObj = document.getElementById("species"); - console.log("speciesObj:", speciesObj); - groupObj = document.getElementById("cross"); - console.log("groupObj:", groupObj); - len = lArr.length; - arr = []; - idx = 0; - i = 1; - while (i < len) { - index_value = getIndexByValue("species", speciesObj.value).toString(); - if (lArr[i][0] === (index_value && !Contains(arr, lArr[i][1]))) { - arr[idx++] = lArr[i][1]; - } - i++; - } - idx = 0; - len = arr.length; - removeOptions("cross"); - i = 0; - while (i < len) { - groupObj.options[idx++] = new Option(gArr[arr[i]].txt, gArr[arr[i]].val); - i++; - } - return updateChoice("cross"); - } else if (selectObjId === "cross") { - speciesObj = document.getElementById("species"); - groupObj = document.getElementById("cross"); - typeObj = document.getElementById("tissue"); - len = lArr.length; - arr = []; - idx = 0; - i = 1; - while (i < len) { - if (lArr[i][0] === (getIndexByValue("species", speciesObj.value)).toString() && lArr[i][1] === (getIndexByValue("cross", groupObj.value)).toString() && !Contains(arr, lArr[i][2])) { - arr[idx++] = lArr[i][2]; - } - i++; - } - idx = 0; - len = arr.length; - removeOptions("tissue"); - i = 0; - while (i < len) { - typeObj.options[idx++] = new Option(tArr[arr[i]].txt, tArr[arr[i]].val); - i++; - } - return updateChoice("tissue"); - } else if (selectObjId === "tissue") { - speciesObj = document.getElementById("species"); - groupObj = document.getElementById("cross"); - typeObj = document.getElementById("tissue"); - databaseObj = document.getElementById("database"); - len = lArr.length; - arr = []; - idx = 0; - i = 1; - while (i < len) { - if (lArr[i][0] === (getIndexByValue("species", speciesObj.value)).toString() && lArr[i][1] === (getIndexByValue("cross", groupObj.value)).toString() && lArr[i][2] === (getIndexByValue("tissue", typeObj.value)).toString() && !Contains(arr, lArr[i][3])) { - arr[idx++] = lArr[i][3]; - } - i++; - } - idx = 0; - len = arr.length; - removeOptions("database"); - i = 0; - while (i < len) { - databaseObj.options[idx++] = new Option(dArr[arr[i]].txt, dArr[arr[i]].val); - i++; - } - return updateChoice("database"); - } + populate_groups = function() { + var group_list, species; + species = $('#species').val(); + group_list = this.jdata.groups[species]; + redo_dropdown($('#group'), group_list); + return populate_types(); }; - Contains = function(arr, obj) { - var i; - i = arr.length; - if ((function() { - var _results; - _results = []; - while (i--) { - _results.push(arr[i] === obj); - } - return _results; - })()) { - return true; - } - return false; + populate_types = function() { + var group, species, type_list; + species = $('#species').val(); + group = $('#group').val(); + type_list = this.jdata.types[species][group]; + redo_dropdown($('#type'), type_list); + return populate_datasets(); }; - removeOptions = function(selectObj) { - var i, len, _results; - if (typeof selectObj !== "object") { - selectObj = document.getElementById(selectObj); - } - len = selectObj.options.length; - i = 0; + populate_datasets = function() { + var dataset_list, group, species, type; + species = $('#species').val(); + group = $('#group').val(); + type = $('#type').val(); + dataset_list = this.jdata.datasets[species][group][type]; + return redo_dropdown($('#dataset'), dataset_list); + }; + redo_dropdown = function(dropdown, items) { + var item, _i, _len, _results; + dropdown.empty(); _results = []; - while (i < len) { - selectObj.options[0] = null; - _results.push(i++); + for (_i = 0, _len = items.length; _i < _len; _i++) { + item = items[_i]; + _results.push(dropdown.append($("
SE
{{ loop.index }} - + - {{ strain.name }} + {{ sample.name }} - -
- - - - + - +
  • Switzerland at the EPFL
    • + - - - +

    History and + Archive

    +
    +

    GeneNetwork's Time + Machine links to earlier versions that correspond to specific + publication dates.

    +
    + + +
    -

    Select and Search -

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - Species: - - - -
    - Group: - - - -
    - Type: - - - -
    - Database: - - - -
    - - -

        Databases marked with ** suffix are not public yet. -
        Access requires user login.

    -
    - Search: - - - - -
    - - - -

        Enter terms, genes, ID numbers in the Search field. -
        Use * or ? wildcards (Cyp*a?, synap*). -
        Use quotes for terms such as "tyrosine kinase".

    - -
    - - -      -      - - -
    - - - -
    - - - - - - - - -

     ______________________________________________________ - -

      - -Quick HELP Examples and - - User's Guide

    - - -  You can also use advanced commands. Copy these simple examples -
      into the Get Any or Combined search fields: -
      - -
    • POSITION=(chr1 25 30) finds genes, markers, or transcripts on chromosome 1 between 25 and 30 Mb. - -
    • MEAN=(15 16) LRS=(23 46) in the Combined field finds highly expressed genes (15 to 16 log2 units) AND with peak LRS linkage between 23 and 46. - +{% block content %} + +
    + + + - +
  • GO:0045202 LRS=(9 99 Chr4 122 155) cisLRS=(9 999 10)
    + in Combined finds synapse-associated genes with cis eQTL on Chr 4 from 122 and 155 Mb with LRS scores + between 9 and 999.
    • - +
  • RIF=diabetes LRS=(9 999 Chr2 100 105) transLRS=(9 999 10)
    + in Combined finds diabetes-associated transcripts with peak trans eQTLs on Chr 2 between 100 and 105 Mb with LRS + scores between 9 and 999.
    • + + + - +
  • Singapore at the NUS
    • - -
    +

    Select and + Search

    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + Species: + +
    Group: + +
    Type: + +
    Database: + +
    +

        Databases marked with ** + suffix are not public yet.
    +     Access requires user login.

    +
    + Search:
    +

        Enter terms, genes, ID numbers in the + Search field.
    +     Use * or ? wildcards (Cyp*a?, + synap*).
    +     Use quotes for terms such as "tyrosine + kinase".

    +
             
    +
    -
  • RIF=mitochondrial searches RNA databases for GeneRIF links. +

     ______________________________________________________

    -
  • WIKI=nicotine searches GeneWiki for genes that you or other users have annotated with the word nicotine. +

      Quick HELP + Examples and User's Guide

      You can also use advanced + commands. Copy these simple examples
    +   into the Get Any or Combined search fields: -
  • GO:0045202 searches for synapse-associated genes listed in the Gene Ontology. +
      +
    • POSITION=(chr1 25 30) finds genes, markers, or transcripts on + chromosome 1 between 25 and 30 Mb.
      • +
    • MEAN=(15 16) LRS=(23 46) in the Combined field finds + highly expressed genes (15 to 16 log2 units) AND with peak LRS linkage between 23 and 46.
      • -
    • GO:0045202 LRS=(9 99 Chr4 122 155) cisLRS=(9 999 10)
      in Combined finds synapse-associated genes with cis eQTL on Chr 4 from 122 and 155 Mb with LRS scores between 9 and 999. +
    • RIF=mitochondrial searches RNA databases for GeneRIF links.
      • -
    • RIF=diabetes LRS=(9 999 Chr2 100 105) transLRS=(9 999 10)
      in Combined finds diabetes-associated transcripts with peak trans eQTLs on Chr 2 between 100 and 105 Mb with LRS scores between 9 and 999. +
    • WIKI=nicotine searches GeneWiki for genes that you or other users have annotated + with the word nicotine.
      • +
    • GO:0045202 searches for synapse-associated genes listed in the + Gene Ontology.
      • -
    - -
    		• -

    Websites Affiliated with GeneNetwork

    -

    -

    -

    -

    ____________________________ +

    		+

    Websites Affiliated with + GeneNetwork

    -

    Getting Started   

    -
      -
    1. Select Species (or select All) -
    2. Select Group (a specific sample) -
    3. Select Type of data: -
        -
      • Phenotype (traits) -
      • Genotype (markers) -
      • Expression (mRNAs) -
      -
    4. Select a Database -
    5. Enter search terms in the Get Any or Combined field: words, genes, ID numbers, probes, advanced search commands -
    6. Click on the Search button -
    7. Optional: Use the Make Default button to save your preferences -
    +

    -

    ____________________________ +

      +
    • Genome + Browser at UTHSC
      • -

      How to Use GeneNetwork +

    • Galaxy at + UTHSC
      • -
      -

      Take a 20-40 minute GeneNetwork Tour that includes screen shots and typical steps in the analysis.

      -
      -
      -

      For information about resources and methods, select the INFO buttons.

      +
    • GeneNetwork at Amazon + Cloud (EC2)
      • +
    • GeneNetwork Source Codes at SourceForge
      • +
    • GeneNetwork Source Codes at GitHub
      • +
    -

    Try the Workstation site to explore data and features that are being implemented.

    +

    ____________________________

    +

    Getting Started +   

    -

    Review the Conditions and Contacts pages for information on the status of data sets and advice on their use and citation.

    +
      +
    1. Select Species (or select All)
      2. +
    2. Select Group (a specific sample)
      3. - +
    3. Select Type of data: +
        +
      • Phenotype (traits)
        • + +
      • Genotype (markers)
        • + +
      • Expression (mRNAs)
        • +
      +
      4. + +
    4. Select a Database
      5. + +
    5. Enter search terms in the Get Any or Combined field: words, + genes, ID numbers, probes, advanced search commands
      6. + +
    6. Click on the Search button
      7. + +
    7. Optional: Use the Make Default button to save your preferences
      8. +
    + +

    ____________________________

    + +

    How to Use + GeneNetwork

    + +
    +

    Take a 20-40 minute + GeneNetwork Tour that includes screen shots and + typical steps in the analysis.

    +
    + +
    +

    For information about + resources and methods, select the INFO buttons.

    +

    Try the Workstation site to explore data and features that are + being implemented.

    + +

    Review the Conditions + and Contacts pages for information on the status of data sets + and advice on their use and citation.

    +
    -

    Mirror and Development Sites

    +

    Mirror and Development + Sites

    - +
    -
    +
    @@ -40,7 +40,7 @@ {% endfor %} - + {% for this_trait in trait_list %} @@ -56,12 +56,7 @@ {{ this_trait.name.upper() }} - + @@ -70,20 +65,20 @@ {% endfor %} - +
    {{header}}
    - - {{ this_trait.symbol }} - - {{ this_trait.symbol }} {{ this_trait.description_display }} {{ this_trait.trait_location_repr }} {{ this_trait.mean }}
    - +
    - +
    - +
    - + {% endblock %} diff --git a/wqflask/wqflask/templates/show_trait.html b/wqflask/wqflask/templates/show_trait.html index d7b81562..79f6e78e 100644 --- a/wqflask/wqflask/templates/show_trait.html +++ b/wqflask/wqflask/templates/show_trait.html @@ -106,14 +106,6 @@


    -
    @@ -669,13 +661,7 @@

      Mapping Tools

    -

    +

    @@ -1055,7 +1041,7 @@

      Review and Edit Data

    -
    +

    @@ -1230,6 +1216,12 @@ + + + +{% endblock %} + +{% block js %} @@ -1240,13 +1232,5 @@ - - - - - - - - {% endblock %} -- cgit v1.2.3 From 1749c660a7e71f10bbdec04f8bf7bb77df3f3eef Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Wed, 21 Nov 2012 17:54:13 -0600 Subject: Changed parser to allow for bother square brackets and parentheses Changed show_trait.coffee to get the stats table working again Still need to get stats table to work with changed values --- misc/notes.txt | 2 + wqflask/wqflask/do_search.py | 6 +- wqflask/wqflask/parser.py | 40 +++++-- wqflask/wqflask/show_trait/show_trait.py | 16 +-- .../static/new/javascript/show_trait.coffee | 57 +++++---- .../wqflask/static/new/javascript/show_trait.js | 46 ++++---- .../wqflask/templates/show_trait_edit_data.html | 130 ++++++++++----------- 7 files changed, 169 insertions(+), 128 deletions(-) (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index b3678a04..59ab79cb 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -1,5 +1,7 @@ To get server running: +!If having seemingly inexplicable problems with imports, make sure I've started the environment! + Start up virtual environment: source ~/ve27/bin/activate diff --git a/wqflask/wqflask/do_search.py b/wqflask/wqflask/do_search.py index 61bfbaba..fd03f359 100644 --- a/wqflask/wqflask/do_search.py +++ b/wqflask/wqflask/do_search.py @@ -5,8 +5,12 @@ from __future__ import print_function, division from pprint import pformat as pf +import sys +sys.path.append("..") + from dbFunction import webqtlDatabaseFunction + class DoSearch(object): """Parent class containing parameters/functions used for all searches""" @@ -423,8 +427,6 @@ if __name__ == "__main__": import MySQLdb import sys - sys.path.append("/home/zas1024/gene/wqflask") - print("Path is:", sys.path) from base import webqtlConfig diff --git a/wqflask/wqflask/parser.py b/wqflask/wqflask/parser.py index 74343b8a..dc33fc52 100644 --- a/wqflask/wqflask/parser.py +++ b/wqflask/wqflask/parser.py @@ -1,12 +1,33 @@ +""" +Parses search terms input by user + +Searches take two primary forms: +- search term by itself (ex. "shh" or "brain") +- key / separator / value(s) (ex. "LRS=(9 99 Chr4 122 155)" or "GO:342533") + +In the example of "LRS=(9 99 Chr4 122 155)", the key is "LRS", the separator is "=" and the value +is everything within the parentheses. + +Both "=" and ":" can be used as separators; in the future, it would also be good to allow no +separator at all (ex. "cisLRS(9 999 10)") + +Both square brackets and parentheses can be used interchangeably. Both can also be used to +encapsulate a single value; "cisLRS=[9 999 10)" would +be acceptable.] + +""" + from __future__ import print_function, division import re from pprint import pformat as pf - def parse(pstring): - pstring = re.split(r"""(?:(\w+\s*=\s*\([^)]*\))|(\w+\s*[=:]\w+)|(\w+))""", pstring) + pstring = re.split(r"""(?:(\w+\s*=\s*[\(\[][^)]*[\)\]]) | # LRS=(1 2 3), cisLRS=[4 5 6], etc + (\w+\s*[=:]\w+) | # wiki=bar, GO:foobar, etc + (\w+)) # shh, brain, etc """, pstring, + flags=re.VERBOSE) pstring = [item.strip() for item in pstring if item and item.strip()] print(pstring) @@ -21,19 +42,12 @@ def parse(pstring): seperator = None if seperator: - if '(' in value: - assert value.startswith("("), "Invalid token" - assert value.endswith(")"), "Invalid token" + if '(' in value or '[' in value: + assert value.startswith(("(", "[")), "Invalid token" + assert value.endswith((")", "]")), "Invalid token" value = value[1:-1] # Get rid of the parenthesis values = re.split(r"""\s+|,""", value) value = [value.strip() for value in values if value.strip()] - # Brackets can also be used to encapsulate values - elif '[' in value: - assert value.startswith("["), "Invalid token" - assert value.endswith("]"), "Invalid token" - value = value[1:-1] # Get rid of the brackets - values = re.split(r"""\s+|,""", value) - value = [value.strip() for value in values if value.strip()] term = dict(key=key, seperator=seperator, search_term=value) @@ -47,6 +61,8 @@ def parse(pstring): return(items) if __name__ == '__main__': + parse("foo=[3 2 1]") + parse("foo=[3 2 1)") parse("foo=(3 2 1)") parse("shh") parse("shh grep") diff --git a/wqflask/wqflask/show_trait/show_trait.py b/wqflask/wqflask/show_trait/show_trait.py index 86a0a992..19e67c43 100755 --- a/wqflask/wqflask/show_trait/show_trait.py +++ b/wqflask/wqflask/show_trait/show_trait.py @@ -159,9 +159,9 @@ class ShowTrait(templatePage): self.hddn = hddn self.sample_group_types = OrderedDict() - self.sample_group_types['primary_only'] = fd.RISet + " Only" - self.sample_group_types['other_only'] = "Non-" + fd.RISet - self.sample_group_types['all_cases'] = "All Cases" + self.sample_group_types['samples_primary'] = fd.RISet + " Only" + self.sample_group_types['samples_other'] = "Non-" + fd.RISet + self.sample_group_types['samples_all'] = "All Cases" sample_lists = [group.sample_list for group in self.sample_groups] print("sample_lists is:", pf(sample_lists)) js_data = dict(sample_group_types = self.sample_group_types, @@ -176,16 +176,16 @@ class ShowTrait(templatePage): #if traitInfos: # database, ProbeSetID, CellID = traitInfos #else: - database = self.fd['database'] - probe_set_id = self.fd['ProbeSetID'] + dataset = self.fd['dataset'] + trait_id = self.fd['trait_id'] cell_id = self.fd.get('CellID') - this_trait = webqtlTrait(db=database, name=probe_set_id, cellid=cell_id, cursor=self.cursor) + this_trait = webqtlTrait(db=dataset, name=trait_id, cellid=cell_id, cursor=self.cursor) ##identification, etc. - self.fd.identification = '%s : %s' % (this_trait.db.shortname, probe_set_id) + self.fd.identification = '%s : %s' % (this_trait.db.shortname, trait_id) this_trait.returnURL = webqtlConfig.CGIDIR + webqtlConfig.SCRIPTFILE + '?FormID=showDatabase&database=%s\ - &ProbeSetID=%s&RISet=%s&parentsf1=on' %(database, probe_set_id, self.fd['RISet']) + &ProbeSetID=%s&RISet=%s&parentsf1=on' %(dataset, trait_id, self.fd['RISet']) if cell_id: self.fd.identification = '%s/%s'%(self.fd.identification, cell_id) diff --git a/wqflask/wqflask/static/new/javascript/show_trait.coffee b/wqflask/wqflask/static/new/javascript/show_trait.coffee index 10671e78..6e22119f 100644 --- a/wqflask/wqflask/static/new/javascript/show_trait.coffee +++ b/wqflask/wqflask/static/new/javascript/show_trait.coffee @@ -26,45 +26,62 @@ $ -> current_value = parseFloat($(in_box)).toFixed(decimal_places) + console.log("urgh:", category, value_type) the_value = sample_sets[category][value_type]() + console.log("After running sample_sets, the_value is:", the_value) if decimal_places > 0 the_value = the_value.toFixed(decimal_places) + console.log("*-* the_value:", the_value) + console.log("*-* current_value:", current_value) if the_value != current_value $(id).html(the_value).effect("highlight") update_stat_values = (sample_sets)-> - for category in ['primary_only', 'other_only', 'all_cases'] + for category in ['samples_primary', 'samples_other', 'samples_all'] change_stats_value(sample_sets, category, "n_of_samples", 0) for stat in ["mean", "median", "std_dev", "std_error"] + console.log("Calling change_stats_value") change_stats_value(sample_sets, category, stat, 2) edit_data_change = -> sample_sets = - primary_only: new Stats([]) - other_only: new Stats([]) - all_cases: new Stats([]) + samples_primary: new Stats([]) + samples_other: new Stats([]) + samples_all: new Stats([]) console.log("at beginning:", sample_sets) - values = $('#value_table').find(".edit_sample_value") - for value in values - real_value = $(value).val() - row = $(value).closest("tr") - category = row[0].id - checkbox = $(row).find(".edit_sample_checkbox") - checked = $(checkbox).attr('checked') - - if checked and is_number(real_value) and real_value != "" - real_value = parseFloat(real_value) - if _(category).startsWith("Primary") - sample_sets.primary_only.add_value(real_value) - else if _(category).startsWith("Other") - sample_sets.other_only.add_value(real_value) - sample_sets.all_cases.add_value(real_value) + # ########## + # Bug here #value_table doesn't exist and why is it a class? + # ########## + + #values = $('.value_table').find(".edit_sample_value") + + + tables = ['samples_primary', 'samples_other'] + for table in tables + rows = $("#" + table).find('tr') + console.log("[fuji3] rows:", rows) + for row in rows + real_value = $(row).find('.edit_sample_value').val() + #row = $(value).closest("tr") + #category = row[0].id + console.log("real_value:", real_value) + checkbox = $(row).find(".edit_sample_checkbox") + checked = $(checkbox).attr('checked') + + if checked and is_number(real_value) and real_value != "" + console.log("in the iffy if") + real_value = parseFloat(real_value) + #if _(category).startsWith("Primary") + sample_sets[table].add_value(real_value) + #else if _(category).startsWith("Other") + # sample_sets.other_only.add_value(real_value) + sample_sets['samples_all'].add_value(real_value) console.log("towards end:", sample_sets) update_stat_values(sample_sets) - + make_table = -> header = "" diff --git a/wqflask/wqflask/static/new/javascript/show_trait.js b/wqflask/wqflask/static/new/javascript/show_trait.js index db40b547..919bc766 100644 --- a/wqflask/wqflask/static/new/javascript/show_trait.js +++ b/wqflask/wqflask/static/new/javascript/show_trait.js @@ -32,17 +32,21 @@ console.log("the_id:", id); in_box = $(id).html; current_value = parseFloat($(in_box)).toFixed(decimal_places); + console.log("urgh:", category, value_type); the_value = sample_sets[category][value_type](); + console.log("After running sample_sets, the_value is:", the_value); if (decimal_places > 0) { the_value = the_value.toFixed(decimal_places); } + console.log("*-* the_value:", the_value); + console.log("*-* current_value:", current_value); if (the_value !== current_value) { return $(id).html(the_value).effect("highlight"); } }; update_stat_values = function(sample_sets) { var category, stat, _i, _len, _ref, _results; - _ref = ['primary_only', 'other_only', 'all_cases']; + _ref = ['samples_primary', 'samples_other', 'samples_all']; _results = []; for (_i = 0, _len = _ref.length; _i < _len; _i++) { category = _ref[_i]; @@ -53,6 +57,7 @@ _results1 = []; for (_j = 0, _len1 = _ref1.length; _j < _len1; _j++) { stat = _ref1[_j]; + console.log("Calling change_stats_value"); _results1.push(change_stats_value(sample_sets, category, stat, 2)); } return _results1; @@ -61,29 +66,30 @@ return _results; }; edit_data_change = function() { - var category, checkbox, checked, real_value, row, sample_sets, value, values, _i, _len; + var checkbox, checked, real_value, row, rows, sample_sets, table, tables, _i, _j, _len, _len1; sample_sets = { - primary_only: new Stats([]), - other_only: new Stats([]), - all_cases: new Stats([]) + samples_primary: new Stats([]), + samples_other: new Stats([]), + samples_all: new Stats([]) }; console.log("at beginning:", sample_sets); - values = $('#value_table').find(".edit_sample_value"); - for (_i = 0, _len = values.length; _i < _len; _i++) { - value = values[_i]; - real_value = $(value).val(); - row = $(value).closest("tr"); - category = row[0].id; - checkbox = $(row).find(".edit_sample_checkbox"); - checked = $(checkbox).attr('checked'); - if (checked && is_number(real_value) && real_value !== "") { - real_value = parseFloat(real_value); - if (_(category).startsWith("Primary")) { - sample_sets.primary_only.add_value(real_value); - } else if (_(category).startsWith("Other")) { - sample_sets.other_only.add_value(real_value); + tables = ['samples_primary', 'samples_other']; + for (_i = 0, _len = tables.length; _i < _len; _i++) { + table = tables[_i]; + rows = $("#" + table).find('tr'); + console.log("[fuji3] rows:", rows); + for (_j = 0, _len1 = rows.length; _j < _len1; _j++) { + row = rows[_j]; + real_value = $(row).find('.edit_sample_value').val(); + console.log("real_value:", real_value); + checkbox = $(row).find(".edit_sample_checkbox"); + checked = $(checkbox).attr('checked'); + if (checked && is_number(real_value) && real_value !== "") { + console.log("in the iffy if"); + real_value = parseFloat(real_value); + sample_sets[table].add_value(real_value); + sample_sets['samples_all'].add_value(real_value); } - sample_sets.all_cases.add_value(real_value); } } console.log("towards end:", sample_sets); diff --git a/wqflask/wqflask/templates/show_trait_edit_data.html b/wqflask/wqflask/templates/show_trait_edit_data.html index ce1642d3..6c9b1073 100644 --- a/wqflask/wqflask/templates/show_trait_edit_data.html +++ b/wqflask/wqflask/templates/show_trait_edit_data.html @@ -82,78 +82,76 @@

    {{ sample_type.header }}

    -
    -
     
    - - - - - - - {% if sample_type.se_exists() %} - - - - {% endif %} - - {% for attribute in sample_type.attributes|sort() %} - - {% endfor %} - - - {% for sample in sample_type.sample_list %} - - - - - - {# Todo: Add IDs #} - +
    IndexSampleValue SE - {{ sample_type.attributes[attribute].name }} -
    - {{ loop.index }} - - - - {{ sample.name }} - - - -
    + + + + + {% if sample_type.se_exists() %} - - - {# Todo: Add IDs #} - + + + {% endif %} - {# Loop through each attribute type and input value #} {% for attribute in sample_type.attributes|sort() %} - + {% endfor %} - - {% endfor %} - -
    IndexSampleValue - ± - - -  SE - {{ sample.extra_attributes[sample_type.attributes[attribute].name] }} - + {{ sample_type.attributes[attribute].name }} +
    -
    +
    + {{ loop.index }} + + + + {{ sample.name }} + + + + + ± + + + + {{ sample.extra_attributes[sample_type.attributes[attribute].name] }} +
    {% endfor %} -- cgit v1.2.3 From 7929d5d5aace20b50a9c5b428eea784d8eac0b77 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Fri, 30 Nov 2012 14:04:59 -0600 Subject: Got searches working for RANGE and MEAN commands Created a file where renamed variables will be listed for future reference --- misc/new_variable_names.txt | 5 ++++ wqflask/wqflask/do_search.py | 59 +++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 61 insertions(+), 3 deletions(-) create mode 100644 misc/new_variable_names.txt (limited to 'misc') diff --git a/misc/new_variable_names.txt b/misc/new_variable_names.txt new file mode 100644 index 00000000..2b10c07e --- /dev/null +++ b/misc/new_variable_names.txt @@ -0,0 +1,5 @@ +RISet/riset -> group +webqtlDataset.py -> data_set.py +webqtlDataset (class object) -> DataSet +database/db -> dataset/data_set +DataEditingPage -> show_trait.py/show_trait.html diff --git a/wqflask/wqflask/do_search.py b/wqflask/wqflask/do_search.py index 92a754e3..11411b26 100644 --- a/wqflask/wqflask/do_search.py +++ b/wqflask/wqflask/do_search.py @@ -414,8 +414,7 @@ class CisLrsSearch(CisTransLrsSearch): def run(self): return self.real_run("<") - - + class TransLrsSearch(CisTransLrsSearch): """Searches for genes on a particular chromosome with a cis-eQTL within the given LRS values @@ -443,9 +442,63 @@ class MeanSearch(ProbeSetSearch): DoSearch.search_types['MEAN'] = "MeanSearch" def run(self): + + self.search_term = [float(value) for value in self.search_term] - return None + if self.search_operator == "=": + assert isinstance(self.search_term, (list, tuple)) + self.mean_min, self.mean_max = self.search_term[:2] + self.where_clause = """ %sXRef.mean > %s and + %sXRef.mean < %s """ % self.mescape(self.dataset.type, + min(self.mean_min, self.mean_max), + self.dataset.type, + max(self.mean_min, self.mean_max)) + else: + # Deal with >, <, >=, and <= + self.where_clause = """ %sXRef.mean %s %s """ % self.mescape(self.dataset.type, + self.search_operator, + self.search_term[0]) + + print("where_clause is:", pf(self.where_clause)) + + self.query = self.compile_final_query(where_clause = self.where_clause) + + return self.execute(self.query) + +class RangeSearch(ProbeSetSearch): + """Searches for genes with a range of expression varying between two values""" + + DoSearch.search_types['RANGE'] = "RangeSearch" + + def run(self): + + self.search_term = [float(value) for value in self.search_term] + + if self.search_operator == "=": + assert isinstance(self.search_term, (list, tuple)) + self.range_min, self.range_max = self.search_term[:2] + self.where_clause = """ (SELECT Pow(2, max(value) -min(value)) + FROM ProbeSetData + WHERE ProbeSetData.Id = ProbeSetXRef.dataId) > %s AND + (SELECT Pow(2, max(value) -min(value)) + FROM ProbeSetData + WHERE ProbeSetData.Id = ProbeSetXRef.dataId) < %s + """ % self.mescape(min(self.range_min, self.range_max), + max(self.range_min, self.range_max)) + else: + # Deal with >, <, >=, and <= + self.where_clause = """ (SELECT Pow(2, max(value) -min(value)) + FROM ProbeSetData + WHERE ProbeSetData.Id = ProbeSetXRef.dataId) > %s + """ % (self.escape(self.search_term[0])) + + print("where_clause is:", pf(self.where_clause)) + + self.query = self.compile_final_query(where_clause = self.where_clause) + + return self.execute(self.query) + if __name__ == "__main__": ### Usually this will be used as a library, but call it from the command line for testing -- cgit v1.2.3 From 292d177f768e8f949bc50f8896b560879aaae178 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Wed, 5 Dec 2012 14:33:02 -0600 Subject: Continued to make changes related to getting rid of cursor/db_conn and using simple sqlalchemy Got Pheno/MrnaAssay dataset searches working again --- misc/new_variable_names.txt | 1 + wqflask/base/data_set.py | 9 ++--- wqflask/base/webqtlTrait.py | 46 +++++++++++----------- wqflask/wqflask/correlation/CorrelationPage.py | 2 +- wqflask/wqflask/correlation/correlationFunction.py | 2 +- wqflask/wqflask/do_search.py | 10 ++--- wqflask/wqflask/search_results.py | 4 +- wqflask/wqflask/show_trait/SampleList.py | 2 +- wqflask/wqflask/show_trait/show_trait.py | 2 +- 9 files changed, 38 insertions(+), 40 deletions(-) (limited to 'misc') diff --git a/misc/new_variable_names.txt b/misc/new_variable_names.txt index 2b10c07e..c11c160e 100644 --- a/misc/new_variable_names.txt +++ b/misc/new_variable_names.txt @@ -3,3 +3,4 @@ webqtlDataset.py -> data_set.py webqtlDataset (class object) -> DataSet database/db -> dataset/data_set DataEditingPage -> show_trait.py/show_trait.html +webqtlTrait -> GeneralTrait \ No newline at end of file diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py index 7833f5c1..70b33014 100755 --- a/wqflask/base/data_set.py +++ b/wqflask/base/data_set.py @@ -272,7 +272,7 @@ class GenotypeDataSet(DataSet): WHERE GenoFreeze.InbredSetId = InbredSet.Id AND GenoFreeze.Name = "%s" - ''' % self.db_conn.escape_string(self.name) + ''' % escape(self.name) def check_confidentiality(self): return geno_mrna_confidentiality(self) @@ -425,13 +425,12 @@ class MrnaAssayDataSet(DataSet): where ProbeSetXRef.ProbeSetFreezeId = %s and ProbeSet.Id = ProbeSetXRef.ProbeSetId and ProbeSet.Name = '%s' - """ % (self.db_conn.escape_string(str(this_trait.dataset.id)), - self.db_conn.escape_string(this_trait.name))) + """ % (escape(str(this_trait.dataset.id)), + escape(this_trait.name))) print("query is:", pf(query)) - self.cursor.execute(query) - result = self.cursor.fetchone() + result = g.db.execute(query).fetchone() if result: if result[0]: diff --git a/wqflask/base/webqtlTrait.py b/wqflask/base/webqtlTrait.py index dec5fa00..5367b41f 100755 --- a/wqflask/base/webqtlTrait.py +++ b/wqflask/base/webqtlTrait.py @@ -10,6 +10,7 @@ from data_set import create_dataset from dbFunction import webqtlDatabaseFunction from utility import webqtlUtil +from MySQLdb import escape_string as escape from pprint import pformat as pf from flask import Flask, g @@ -40,9 +41,7 @@ class GeneralTrait: self.dataset, self.name, self.cellid = name2 #if self.dataset and isinstance(self.dataset, basestring): - self.dataset = create_dataset(self.dataset) - - + self.dataset = create_dataset(self.dataset.name) print("self.dataset is:", self.dataset, type(self.dataset)) #if self.dataset: @@ -367,7 +366,7 @@ class GeneralTrait: def retrieve_info(self, QTL=False): assert self.dataset, "Dataset doesn't exist" if self.dataset.type == 'Publish': - traitInfo = g.db.execute(""" + query = """ SELECT PublishXRef.Id, Publication.PubMed_ID, Phenotype.Pre_publication_description, Phenotype.Post_publication_description, Phenotype.Original_description, @@ -384,47 +383,46 @@ class GeneralTrait: Phenotype.Id = PublishXRef.PhenotypeId AND Publication.Id = PublishXRef.PublicationId AND PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND - PublishFreeze.Id =%s - """, (self.name, self.dataset.id)).fetchone() + PublishFreeze.Id = %s + """ % (self.name, self.dataset.id) + traitInfo = g.db.execute(query).fetchone() #XZ, 05/08/2009: Xiaodong add this block to use ProbeSet.Id to find the probeset instead of just using ProbeSet.Name #XZ, 05/08/2009: to avoid the problem of same probeset name from different platforms. elif self.dataset.type == 'ProbeSet': - display_fields_string = ',ProbeSet.'.join(self.dataset.display_fields) + display_fields_string = ', ProbeSet.'.join(self.dataset.display_fields) display_fields_string = 'ProbeSet.' + display_fields_string - traitInfo = g.db.execute(""" + query = """ SELECT %s FROM ProbeSet, ProbeSetFreeze, ProbeSetXRef WHERE ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND ProbeSetXRef.ProbeSetId = ProbeSet.Id AND - ProbeSetFreeze.Name = %s AND - ProbeSet.Name = %s - """, (display_fields_string, self.dataset.name, self.name)).fetchone() + ProbeSetFreeze.Name = '%s' AND + ProbeSet.Name = '%s' + """ % (display_fields_string, self.dataset.name, self.name) + traitInfo = g.db.execute(query).fetchone() + print("traitInfo is: ", pf(traitInfo)) #XZ, 05/08/2009: We also should use Geno.Id to find marker instead of just using Geno.Name # to avoid the problem of same marker name from different species. elif self.dataset.type == 'Geno': display_fields_string = string.join(self.dataset.display_fields,',Geno.') display_fields_string = 'Geno.' + display_fields_string - traitInfo = g.db.execute(""" + query = """ SELECT %s FROM Geno, GenoFreeze, GenoXRef WHERE GenoXRef.GenoFreezeId = GenoFreeze.Id AND GenoXRef.GenoId = Geno.Id AND - GenoFreeze.Name = %s AND - Geno.Name = %s - """, (display_fields_string, self.dataset.name, self.name)).fetchone() + GenoFreeze.Name = '%s' AND + Geno.Name = '%s' + """ % (display_fields_string, self.dataset.name, self.name) + traitInfo = g.db.execute(query).fetchone() + print("traitInfo is: ", pf(traitInfo)) else: #Temp type - traitInfo = g.db.execute("""SELECT %s FROM %s WHERE Name = %s - """, (string.join(self.dataset.display_fields,','), - self.dataset.type, self.name)).fetchone() - - query = """SELECT %s FROM %s WHERE Name = %s + query = """SELECT %s FROM %s WHERE Name = %s """ % (string.join(self.dataset.display_fields,','), - self.dataset.type, self.name) - - print("query is:", pf(query)) - print("traitInfo is: ", pf(traitInfo)) + self.dataset.type, self.name) + traitInfo = g.db.execute(query).fetchone() #self.cursor.execute(query) diff --git a/wqflask/wqflask/correlation/CorrelationPage.py b/wqflask/wqflask/correlation/CorrelationPage.py index 8af30d1e..f1dd96ef 100644 --- a/wqflask/wqflask/correlation/CorrelationPage.py +++ b/wqflask/wqflask/correlation/CorrelationPage.py @@ -46,7 +46,7 @@ import reaper from base import webqtlConfig from utility.THCell import THCell from utility.TDCell import TDCell -from base.webqtlTrait import webqtlTrait +from base.webqtlTrait import GeneralTrait from base.data_set import create_dataset from base.templatePage import templatePage from utility import webqtlUtil diff --git a/wqflask/wqflask/correlation/correlationFunction.py b/wqflask/wqflask/correlation/correlationFunction.py index 4d62a468..8638cb1e 100644 --- a/wqflask/wqflask/correlation/correlationFunction.py +++ b/wqflask/wqflask/correlation/correlationFunction.py @@ -31,7 +31,7 @@ import pp import string from utility import webqtlUtil -from base.webqtlTrait import webqtlTrait +from base.webqtlTrait import GeneralTrait from dbFunction import webqtlDatabaseFunction diff --git a/wqflask/wqflask/do_search.py b/wqflask/wqflask/do_search.py index 2094ed14..4301fb50 100644 --- a/wqflask/wqflask/do_search.py +++ b/wqflask/wqflask/do_search.py @@ -35,7 +35,7 @@ class DoSearch(object): """Executes query and returns results""" query = self.normalize_spaces(query) print("in do_search query is:", pf(query)) - results = g.db.execute(query).fetchall() + results = g.db.execute(query, no_parameters=True).fetchall() #results = self.cursor.fetchall() return results @@ -167,7 +167,7 @@ class PhenotypeSearch(DoSearch): fields_clause = [] for field in self.search_fields: fields_clause.append('''%s REGEXP "%s"''' % (field, search_term)) - fields_clause = "(%s)" % ' OR '.join(fields_clause) + fields_clause = "(%s) and " % ' OR '.join(fields_clause) return fields_clause @@ -198,7 +198,7 @@ class PhenotypeSearch(DoSearch): def run(self): """Generates and runs a simple search of a phenotype dataset""" - self.query = self.compile_final_query(where_clause = self.get_fields_clause()) + query = self.compile_final_query(where_clause = self.get_fields_clause()) # self.query = """SELECT PublishXRef.Id, #PublishFreeze.createtime as thistable, @@ -220,7 +220,7 @@ class PhenotypeSearch(DoSearch): #and PublishFreeze.Id = 1;""" - results = g.db.execute(self.query, no_parameters=True).fetchall() + results = self.execute(query) print("in [df] run results are:", results) return results @@ -272,7 +272,7 @@ class GenotypeSearch(DoSearch): Geno.Id = GenoXRef.GenoId and GenoXRef.GenoFreezeId = GenoFreeze.Id and GenoFreeze.Id = %s"""% (where_clause, - escape(self.dataset.id))) + escape(str(self.dataset.id)))) print("query is:", pf(query)) diff --git a/wqflask/wqflask/search_results.py b/wqflask/wqflask/search_results.py index efa1c5cc..cd478110 100644 --- a/wqflask/wqflask/search_results.py +++ b/wqflask/wqflask/search_results.py @@ -30,7 +30,7 @@ from base import webqtlConfig from utility.THCell import THCell from utility.TDCell import TDCell from base.data_set import create_dataset -from base.webqtlTrait import webqtlTrait +from base.webqtlTrait import GeneralTrait from base.templatePage import templatePage from wqflask import parser from wqflask import do_search @@ -113,7 +113,7 @@ class SearchResultPage(templatePage): print("foo locals are:", locals()) trait_id = result[0] - this_trait = webqtlTrait(dataset=self.dataset, name=trait_id) + this_trait = GeneralTrait(dataset=self.dataset, name=trait_id) this_trait.retrieve_info(QTL=True) self.trait_list.append(this_trait) diff --git a/wqflask/wqflask/show_trait/SampleList.py b/wqflask/wqflask/show_trait/SampleList.py index df0dc61e..25877521 100644 --- a/wqflask/wqflask/show_trait/SampleList.py +++ b/wqflask/wqflask/show_trait/SampleList.py @@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division from base import webqtlCaseData from utility import webqtlUtil, Plot, Bunch -from base.webqtlTrait import webqtlTrait +from base.webqtlTrait import GeneralTrait from pprint import pformat as pf diff --git a/wqflask/wqflask/show_trait/show_trait.py b/wqflask/wqflask/show_trait/show_trait.py index 7060f2ea..aef9219f 100755 --- a/wqflask/wqflask/show_trait/show_trait.py +++ b/wqflask/wqflask/show_trait/show_trait.py @@ -13,7 +13,7 @@ from base import webqtlConfig from base import webqtlCaseData from wqflask.show_trait.SampleList import SampleList from utility import webqtlUtil, Plot, Bunch -from base.webqtlTrait import webqtlTrait +from base.webqtlTrait import GeneralTrait from dbFunction import webqtlDatabaseFunction from base.templatePage import templatePage from basicStatistics import BasicStatisticsFunctions -- cgit v1.2.3 From a7cc1119ebfbfab3ba5260be75c87cd4496f09b7 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Wed, 5 Dec 2012 18:03:23 -0600 Subject: Renamed webqtlTrait.py to trait.py Renamed webqtlTrait class to GeneralTrait Began process of removing fd from show_trait.py Created DatasetGroup object in data_set.py (this may end up becoming its own file later if it becomes big enough) --- misc/notes.txt | 10 +- misc/todo.txt | 4 +- wqflask/base/data_set.py | 221 +++++++++- wqflask/base/trait.py | 708 +++++++++++++++++++++++++++++++ wqflask/base/webqtlTrait.py | 695 ------------------------------ wqflask/wqflask/do_search.py | 5 +- wqflask/wqflask/search_results.py | 5 +- wqflask/wqflask/show_trait/show_trait.py | 295 ++++++++----- 8 files changed, 1129 insertions(+), 814 deletions(-) create mode 100755 wqflask/base/trait.py delete mode 100755 wqflask/base/webqtlTrait.py (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index 59ab79cb..b0c0762c 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -14,6 +14,9 @@ export TERM=screen To search for commands in history if necessary: history | grep "(whatever is being searched for)" +Run web server: +/usr/local/nginx/sbin/nginx + Run server: python runserver.py @@ -63,11 +66,16 @@ Classes should always inherit "object" htop: Gives information on processes, cpu/memory load, etc dstat: Also gives various system information, resource usage, etc df: Reports file system disk space usage - +d =========================================== tidyp - Improves/beautifies html code tidyp -m -i -w 100 index_page.html +=========================================== + +ps -ax - View processes + +kill (process #) diff --git a/misc/todo.txt b/misc/todo.txt index 609e053f..60655a71 100644 --- a/misc/todo.txt +++ b/misc/todo.txt @@ -1 +1,3 @@ -- Read about grep/locate/find \ No newline at end of file +- Check about using trait id instead of trait name in queries in data_set.py + +- Ask Rob about Probe/cellid traits \ No newline at end of file diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py index 70b33014..68f5e5ed 100755 --- a/wqflask/base/data_set.py +++ b/wqflask/base/data_set.py @@ -21,12 +21,16 @@ # This module is used by GeneNetwork project (www.genenetwork.org) from __future__ import print_function, division +import os from flask import Flask, g from htmlgen import HTMLgen2 as HT +import reaper + import webqtlConfig +from utility import webqtlUtil from MySQLdb import escape_string as escape from pprint import pformat as pf @@ -57,6 +61,74 @@ def create_dataset(dataset_name): return dataset_class(dataset_name) +class DatasetGroup(object): + """ + Each group has multiple datasets; each species has multiple groups. + + For example, Mouse has multiple groups (BXD, BXA, etc), and each group + has multiple datasets associated with it. + + """ + def __init__(self, dataset): + """This sets self.group and self.group_id""" + self.name, self.group_id = g.db.execute(dataset.query).fetchone() + if self.name == 'BXD300': + self.name = "BXD" + + self.incparentsf1 = False + + + #def read_genotype(self): + # self.read_genotype_file() + # + # if not self.genotype: # Didn'd succeed, so we try method 2 + # self.read_genotype_data() + + def read_genotype_file(self): + '''read genotype from .geno file instead of database''' + #if self.group == 'BXD300': + # self.group = 'BXD' + # + #assert self.group, "self.group needs to be set" + + #genotype_1 is Dataset Object without parents and f1 + #genotype_2 is Dataset Object with parents and f1 (not for intercross) + + self.genotype_1 = reaper.Dataset() + + # reaper barfs on unicode filenames, so here we ensure it's a string + full_filename = str(os.path.join(webqtlConfig.GENODIR, self.name + '.geno')) + self.genotype_1.read(full_filename) + + print("Got to after read") + + try: + # NL, 07/27/2010. ParInfo has been moved from webqtlForm.py to webqtlUtil.py; + _f1, _f12, _mat, _pat = webqtlUtil.ParInfo[self.name] + except KeyError: + _f1 = _f12 = _mat = _pat = None + + self.genotype_2 = self.genotype_1 + if self.genotype_1.type == "group" and _mat and _pat: + self.genotype_2 = self.genotype_1.add(Mat=_mat, Pat=_pat) #, F1=_f1) + + #determine default genotype object + if self.incparentsf1 and self.genotype_1.type != "intercross": + self.genotype = self.genotype_2 + else: + self.incparentsf1 = 0 + self.genotype = self.genotype_1 + + self.samplelist = list(self.genotype.prgy) + self.f1list = [] + self.parlist = [] + + if _f1 and _f12: + self.f1list = [_f1, _f12] + if _mat and _pat: + self.parlist = [_mat, _pat] + + class DataSet(object): """ DataSet class defines a dataset in webqtl, can be either Microarray, @@ -70,27 +142,35 @@ class DataSet(object): self.name = name self.id = None self.type = None - self.group = None self.setup() self.check_confidentiality() self.retrieve_name() - self.get_group() + self.group = DatasetGroup(self) # sets self.group and self.group_id + + + def get_desc(self): + """Gets overridden later, at least for Temp...used by trait's get_given_name""" + return None # Delete this eventually @property def riset(): Weve_Renamed_This_As_Group + + + #@property + #def group(self): + # if not self._group: + # self.get_group() + # + # return self._group + - def get_group(self): - self.group, self.group_id = g.db.execute(self.query).fetchone() - if self.group == 'BXD300': - self.group = "BXD" - #return group def retrieve_name(self): @@ -176,7 +256,7 @@ class PhenotypeDataSet(DataSet): self.type = 'Publish' - self.query = ''' + self.query_for_group = ''' SELECT InbredSet.Name, InbredSet.Id FROM @@ -239,7 +319,29 @@ class PhenotypeDataSet(DataSet): this_trait.LRS_score_repr = LRS_score_repr = '%3.1f' % this_trait.lrs this_trait.LRS_score_value = LRS_score_value = this_trait.lrs - this_trait.LRS_location_repr = LRS_location_repr = 'Chr %s: %.4f Mb' % (LRS_Chr, float(LRS_Mb) ) + this_trait.LRS_location_repr = LRS_location_repr = 'Chr %s: %.4f Mb' % (LRS_Chr, float(LRS_Mb)) + + def retrieve_sample_data(self, trait): + query = """ + SELECT + Strain.Name, PublishData.value, PublishSE.error, NStrain.count, PublishData.Id + FROM + (PublishData, Strain, PublishXRef, PublishFreeze) + left join PublishSE on + (PublishSE.DataId = PublishData.Id AND PublishSE.StrainId = PublishData.StrainId) + left join NStrain on + (NStrain.DataId = PublishData.Id AND + NStrain.StrainId = PublishData.StrainId) + WHERE + PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND + PublishData.Id = PublishXRef.DataId AND PublishXRef.Id = %s AND + PublishFreeze.Id = %d AND PublishData.StrainId = Strain.Id + Order BY + Strain.Name + """ % (self.trait.name, self.id) + results = g.db.execute(query).fetchall() + return results + class GenotypeDataSet(DataSet): DS_NAME_MAP['Geno'] = 'GenotypeDataSet' @@ -297,6 +399,26 @@ class GenotypeDataSet(DataSet): this_trait.location_repr = 'Chr%s: %.4f' % (this_trait.chr, float(this_trait.mb) ) this_trait.location_value = trait_location_value + + def retrieve_sample_data(self, trait): + query = """ + SELECT + Strain.Name, GenoData.value, GenoSE.error, GenoData.Id + FROM + (GenoData, GenoFreeze, Strain, Geno, GenoXRef) + left join GenoSE on + (GenoSE.DataId = GenoData.Id AND GenoSE.StrainId = GenoData.StrainId) + WHERE + Geno.SpeciesId = %s AND Geno.Name = '%s' AND GenoXRef.GenoId = Geno.Id AND + GenoXRef.GenoFreezeId = GenoFreeze.Id AND + GenoFreeze.Name = '%s' AND + GenoXRef.DataId = GenoData.Id AND + GenoData.StrainId = Strain.Id + Order BY + Strain.Name + """ % (webqtlDatabaseFunction.retrieve_species_id(self.group), trait.name, self.name) + results = g.db.execute(query).fetchall() + return results class MrnaAssayDataSet(DataSet): @@ -476,6 +598,42 @@ class MrnaAssayDataSet(DataSet): this_trait.LRS_score_repr = LRS_score_repr = '%3.1f' % this_trait.lrs this_trait.LRS_score_value = LRS_score_value = this_trait.lrs this_trait.LRS_location_repr = LRS_location_repr = 'Chr %s: %.4f Mb' % (LRS_Chr, float(LRS_Mb) ) + + def get_sequence(self): + query = """ + SELECT + ProbeSet.BlatSeq + FROM + ProbeSet, ProbeSetFreeze, ProbeSetXRef + WHERE + ProbeSet.Id=ProbeSetXRef.ProbeSetId and + ProbeSetFreeze.Id = ProbeSetXRef.ProbSetFreezeId and + ProbeSet.Name = %s + ProbeSetFreeze.Name = %s + """ % (escape(self.name), escape(self.dataset.name)) + results = g.db.execute(query).fetchone() + + return results[0] + + def retrieve_sample_data(self, trait): + query = """ + SELECT + Strain.Name, ProbeSetData.value, ProbeSetSE.error, ProbeSetData.Id + FROM + (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef) + left join ProbeSetSE on + (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId) + WHERE + ProbeSet.Name = '%s' AND ProbeSetXRef.ProbeSetId = ProbeSet.Id AND + ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND + ProbeSetFreeze.Name = '%s' AND + ProbeSetXRef.DataId = ProbeSetData.Id AND + ProbeSetData.StrainId = Strain.Id + Order BY + Strain.Name + """ % (escape(trait.name), escape(self.name)) + results = g.db.execute(query).fetchall() + return results class TempDataSet(DataSet): @@ -497,6 +655,51 @@ class TempDataSet(DataSet): self.id = 1 self.fullname = 'Temporary Storage' self.shortname = 'Temp' + + + @staticmethod + def handle_pca(desc): + if 'PCA' in desc: + # Todo: Modernize below lines + desc = desc[desc.rindex(':')+1:].strip() + else: + desc = desc[:desc.index('entered')].strip() + return desc + + def get_desc(self): + g.db.execute('SELECT description FROM Temp WHERE Name=%s', self.name) + desc = g.db.fetchone()[0] + desc = self.handle_pca(desc) + return desc + + def get_group(self): + self.cursor.execute(""" + SELECT + InbredSet.Name, InbredSet.Id + FROM + InbredSet, Temp + WHERE + Temp.InbredSetId = InbredSet.Id AND + Temp.Name = "%s" + """, self.name) + self.group, self.group_id = self.cursor.fetchone() + #return self.group + + def retrieve_sample_data(self, trait): + query = """ + SELECT + Strain.Name, TempData.value, TempData.SE, TempData.NStrain, TempData.Id + FROM + TempData, Temp, Strain + WHERE + TempData.StrainId = Strain.Id AND + TempData.Id = Temp.DataId AND + Temp.name = '%s' + Order BY + Strain.Name + """ % escape(trait.name) + + results = g.db.execute(query).fetchall() def geno_mrna_confidentiality(ob): diff --git a/wqflask/base/trait.py b/wqflask/base/trait.py new file mode 100755 index 00000000..d3753fc1 --- /dev/null +++ b/wqflask/base/trait.py @@ -0,0 +1,708 @@ +from __future__ import division, print_function + +import string + +from htmlgen import HTMLgen2 as HT + +import webqtlConfig +from webqtlCaseData import webqtlCaseData +from data_set import create_dataset +from dbFunction import webqtlDatabaseFunction +from utility import webqtlUtil + +from MySQLdb import escape_string as escape +from pprint import pformat as pf + +from flask import Flask, g + +class GeneralTrait: + """ + Trait class defines a trait in webqtl, can be either Microarray, + Published phenotype, genotype, or user input trait + + """ + + def __init__(self, **kw): + print("in GeneralTrait") + self.dataset = kw.get('dataset', None) # database object + self.name = kw.get('name', None) # Trait ID, ProbeSet ID, Published ID, etc. + self.cellid = kw.get('cellid', None) + self.identification = kw.get('identification', 'un-named trait') + #self.group = kw.get('group', None) + self.haveinfo = kw.get('haveinfo', False) + self.sequence = kw.get('sequence', None) # Blat sequence, available for ProbeSet + self.data = kw.get('data', {}) + + if kw.get('fullname'): + name2 = value.split("::") + if len(name2) == 2: + self.dataset, self.name = name2 + elif len(name2) == 3: + self.dataset, self.name, self.cellid = name2 + + #if self.dataset and isinstance(self.dataset, basestring): + self.dataset = create_dataset(self.dataset) + + print("self.dataset is:", self.dataset, type(self.dataset)) + #if self.dataset: + + #self.dataset.get_group() + + #if self.dataset.type == "Temp": + # self.cursor.execute(''' + # SELECT + # InbredSet.Name + # FROM + # InbredSet, Temp + # WHERE + # Temp.InbredSetId = InbredSet.Id AND + # Temp.Name = "%s" + # ''', self.name) + # self.group = self.cursor.fetchone()[0] + #else: + # self.group = self.dataset.get_group() + + #print("trinity, self.group is:", self.group) + + # + # In ProbeSet, there are maybe several annotations match one sequence + # so we need use sequence(BlatSeq) as the identification, when we update + # one annotation, we update the others who match the sequence also. + # + # Hongqiang Li, 3/3/2008 + # + + #XZ, 05/08/2009: This block is not neccessary. We can add 'BlatSeq' into disfield. + # The variable self.sequence should be changed to self.BlatSeq + # It also should be changed in other places where it are used. + + #if self.dataset: + #if self.dataset.type == 'ProbeSet': + # print("Doing ProbeSet Query") + # query = ''' + # SELECT + # ProbeSet.BlatSeq + # FROM + # ProbeSet, ProbeSetFreeze, ProbeSetXRef + # WHERE + # ProbeSet.Id=ProbeSetXRef.ProbeSetId and + # ProbeSetFreeze.Id = ProbeSetXRef.ProbeSetFreezeId and + # ProbeSet.Name = %s and + # ProbeSetFreeze.Name = %s + # ''', (self.name, self.dataset.name) + # print("query is:", query) + # self.sequence = g.db.execute(*query).fetchone()[0] + # #self.sequence = self.cursor.fetchone()[0] + # print("self.sequence is:", self.sequence) + + + def get_name(self): + stringy = "" + if self.dataset and self.name: + stringy = "%s::%s" % (self.dataset, self.name) + if self.cellid: + stringy += "::" + self.cellid + else: + stringy = self.description + return stringy + + + def get_given_name(self): + """ + when user enter a trait or GN generate a trait, user want show the name + not the name that generated by GN randomly, the two follow function are + used to give the real name and the database. displayName() will show the + database also, getGivenName() just show the name. + For other trait, displayName() as same as getName(), getGivenName() as + same as self.name + + Hongqiang 11/29/07 + + """ + stringy = self.name + if self.dataset and self.name: + desc = self.dataset.get_desc() + if desc: + #desc = self.handle_pca(desc) + stringy = desc + return stringy + + + + def display_name(self): + stringy = "" + if self.dataset and self.name: + desc = self.dataset.get_desc() + #desc = self.handle_pca(desc) + if desc: + #desc = self.handle_pca(desc) + #stringy = desc + #if desc.__contains__('PCA'): + # desc = desc[desc.rindex(':')+1:].strip() + #else: + # desc = desc[:desc.index('entered')].strip() + #desc = self.handle_pca(desc) + stringy = "%s::%s" % (self.dataset, desc) + else: + stringy = "%s::%s" % (self.dataset, self.name) + if self.cellid: + stringy += "::" + self.cellid + else: + stringy = self.description + + return stringy + + + #def __str__(self): + # #return "%s %s" % (self.getName(), self.group) + # return self.getName() + #__str__ = getName + #__repr__ = __str__ + + def export_data(self, samplelist, the_type="val"): + """ + export data according to samplelist + mostly used in calculating correlation + + """ + result = [] + for sample in samplelist: + if self.data.has_key(sample): + if the_type=='val': + result.append(self.data[sample].val) + elif the_type=='var': + result.append(self.data[sample].var) + elif the_type=='N': + result.append(self.data[sample].N) + else: + raise KeyError, `the_type`+' the_type is incorrect.' + else: + result.append(None) + return result + + def export_informative(self, incVar=0): + """ + export informative sample + mostly used in qtl regression + + """ + samples = [] + vals = [] + the_vars = [] + for sample, value in self.data.items(): + if value.val != None: + if not incVar or value.var != None: + samples.append(sample) + vals.append(value.val) + the_vars.append(value.var) + return samples, vals, the_vars + + + # + # In ProbeSet, there are maybe several annotations match one sequence + # so we need use sequence(BlatSeq) as the identification, when we update + # one annotation, we update the others who match the sequence also. + # + # Hongqiang Li, 3/3/2008 + # + #def getSequence(self): + # assert self.cursor + # if self.dataset.type == 'ProbeSet': + # self.cursor.execute(''' + # SELECT + # ProbeSet.BlatSeq + # FROM + # ProbeSet, ProbeSetFreeze, ProbeSetXRef + # WHERE + # ProbeSet.Id=ProbeSetXRef.ProbeSetId and + # ProbeSetFreeze.Id = ProbeSetXRef.ProbSetFreezeId and + # ProbeSet.Name = %s + # ProbeSetFreeze.Name = %s + # ''', self.name, self.dataset.name) + # #self.cursor.execute(query) + # results = self.fetchone() + # + # return results[0] + + + + def retrieve_sample_data(self, samplelist=None): + if samplelist == None: + samplelist = [] + + assert self.dataset + + #if self.cellid: + # #Probe Data + # query = ''' + # SELECT + # Strain.Name, ProbeData.value, ProbeSE.error, ProbeData.Id + # FROM + # (ProbeData, ProbeFreeze, ProbeSetFreeze, ProbeXRef, + # Strain, Probe, ProbeSet) + # left join ProbeSE on + # (ProbeSE.DataId = ProbeData.Id AND ProbeSE.StrainId = ProbeData.StrainId) + # WHERE + # Probe.Name = '%s' AND ProbeSet.Name = '%s' AND + # Probe.ProbeSetId = ProbeSet.Id AND + # ProbeXRef.ProbeId = Probe.Id AND + # ProbeXRef.ProbeFreezeId = ProbeFreeze.Id AND + # ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id AND + # ProbeSetFreeze.Name = '%s' AND + # ProbeXRef.DataId = ProbeData.Id AND + # ProbeData.StrainId = Strain.Id + # Order BY + # Strain.Name + # ''' % (self.cellid, self.name, self.dataset.name) + # + #else: + results = self.dataset.retrieve_sample_data(self) + + #if self.dataset.type == 'Temp': + # query = ''' + # SELECT + # Strain.Name, TempData.value, TempData.SE, TempData.NStrain, TempData.Id + # FROM + # TempData, Temp, Strain + # WHERE + # TempData.StrainId = Strain.Id AND + # TempData.Id = Temp.DataId AND + # Temp.name = '%s' + # Order BY + # Strain.Name + # ''' % self.name + ##XZ, 03/02/2009: Xiaodong changed Data to PublishData, SE to PublishSE + #elif self.dataset.type == 'Publish': + # query = ''' + # SELECT + # Strain.Name, PublishData.value, PublishSE.error, NStrain.count, PublishData.Id + # FROM + # (PublishData, Strain, PublishXRef, PublishFreeze) + # left join PublishSE on + # (PublishSE.DataId = PublishData.Id AND PublishSE.StrainId = PublishData.StrainId) + # left join NStrain on + # (NStrain.DataId = PublishData.Id AND + # NStrain.StrainId = PublishData.StrainId) + # WHERE + # PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND + # PublishData.Id = PublishXRef.DataId AND PublishXRef.Id = %s AND + # PublishFreeze.Id = %d AND PublishData.StrainId = Strain.Id + # Order BY + # Strain.Name + # ''' % (self.name, self.dataset.id) + + #XZ, 03/02/2009: Xiaodong changed Data to ProbeData, SE to ProbeSE + #elif self.cellid: + + #XZ, 03/02/2009: Xiaodong added this block for ProbeSetData and ProbeSetSE + #elif self.dataset.type == 'ProbeSet': + # #ProbeSet Data + # query = ''' + # SELECT + # Strain.Name, ProbeSetData.value, ProbeSetSE.error, ProbeSetData.Id + # FROM + # (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef) + # left join ProbeSetSE on + # (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId) + # WHERE + # ProbeSet.Name = '%s' AND ProbeSetXRef.ProbeSetId = ProbeSet.Id AND + # ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND + # ProbeSetFreeze.Name = '%s' AND + # ProbeSetXRef.DataId = ProbeSetData.Id AND + # ProbeSetData.StrainId = Strain.Id + # Order BY + # Strain.Name + # ''' % (self.name, self.dataset.name) + ##XZ, 03/02/2009: Xiaodong changeded Data to GenoData, SE to GenoSE + #else: + # #Geno Data + # #XZ: The SpeciesId is not necessary, but it's nice to keep it to speed up database search. + # query = ''' + # SELECT + # Strain.Name, GenoData.value, GenoSE.error, GenoData.Id + # FROM + # (GenoData, GenoFreeze, Strain, Geno, GenoXRef) + # left join GenoSE on + # (GenoSE.DataId = GenoData.Id AND GenoSE.StrainId = GenoData.StrainId) + # WHERE + # Geno.SpeciesId = %s AND Geno.Name = '%s' AND GenoXRef.GenoId = Geno.Id AND + # GenoXRef.GenoFreezeId = GenoFreeze.Id AND + # GenoFreeze.Name = '%s' AND + # GenoXRef.DataId = GenoData.Id AND + # GenoData.StrainId = Strain.Id + # Order BY + # Strain.Name + # ''' % (webqtlDatabaseFunction.retrieveSpeciesId(self.cursor, self.dataset.group), self.name, self.dataset.name) + + + #self.cursor.execute(query) + #results = self.cursor.fetchall() + + # Todo: is this necessary? If not remove + self.data.clear() + + if results: + #self.mysqlid = results[0][-1] + #if samplelist: + for item in results: + #name, value, variance, num_cases = item + if not samplelist or (samplelist and name in samplelist): + #if value != None: + # num_cases = None + # if self.dataset.type in ('Publish', 'Temp'): + # ndata = item[3] + name = item[0] + self.data[name] = webqtlCaseData(*item) #name, value, variance, num_cases) + #end for + # else: + # for item in results: + # val = item[1] + # if val != None: + # var = item[2] + # ndata = None + # if self.dataset.type in ('Publish', 'Temp'): + # ndata = item[3] + # self.data[item[0]] = webqtlCaseData(val, var, ndata) + # #end for + # #end if + + #def keys(self): + # return self.__dict__.keys() + # + #def has_key(self, key): + # return self.__dict__.has_key(key) + # + #def items(self): + # return self.__dict__.items() + + def retrieve_info(self, QTL=False): + assert self.dataset, "Dataset doesn't exist" + if self.dataset.type == 'Publish': + query = """ + SELECT + PublishXRef.Id, Publication.PubMed_ID, + Phenotype.Pre_publication_description, Phenotype.Post_publication_description, Phenotype.Original_description, + Phenotype.Pre_publication_abbreviation, Phenotype.Post_publication_abbreviation, + Phenotype.Lab_code, Phenotype.Submitter, Phenotype.Owner, Phenotype.Authorized_Users, + Publication.Authors, Publication.Title, Publication.Abstract, + Publication.Journal, Publication.Volume, Publication.Pages, + Publication.Month, Publication.Year, PublishXRef.Sequence, + Phenotype.Units, PublishXRef.comments + FROM + PublishXRef, Publication, Phenotype, PublishFreeze + WHERE + PublishXRef.Id = %s AND + Phenotype.Id = PublishXRef.PhenotypeId AND + Publication.Id = PublishXRef.PublicationId AND + PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND + PublishFreeze.Id = %s + """ % (self.name, self.dataset.id) + traitInfo = g.db.execute(query).fetchone() + #XZ, 05/08/2009: Xiaodong add this block to use ProbeSet.Id to find the probeset instead of just using ProbeSet.Name + #XZ, 05/08/2009: to avoid the problem of same probeset name from different platforms. + elif self.dataset.type == 'ProbeSet': + display_fields_string = ', ProbeSet.'.join(self.dataset.display_fields) + display_fields_string = 'ProbeSet.' + display_fields_string + query = """ + SELECT %s + FROM ProbeSet, ProbeSetFreeze, ProbeSetXRef + WHERE + ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND + ProbeSetXRef.ProbeSetId = ProbeSet.Id AND + ProbeSetFreeze.Name = '%s' AND + ProbeSet.Name = '%s' + """ % (escape(display_fields_string), + escape(self.dataset.name), + escape(self.name)) + traitInfo = g.db.execute(query).fetchone() + print("traitInfo is: ", pf(traitInfo)) + #XZ, 05/08/2009: We also should use Geno.Id to find marker instead of just using Geno.Name + # to avoid the problem of same marker name from different species. + elif self.dataset.type == 'Geno': + display_fields_string = string.join(self.dataset.display_fields,',Geno.') + display_fields_string = 'Geno.' + display_fields_string + query = """ + SELECT %s + FROM Geno, GenoFreeze, GenoXRef + WHERE + GenoXRef.GenoFreezeId = GenoFreeze.Id AND + GenoXRef.GenoId = Geno.Id AND + GenoFreeze.Name = '%s' AND + Geno.Name = '%s' + """ % (escape(display_fields_string), escape(self.dataset.name), escape(self.name)) + traitInfo = g.db.execute(query).fetchone() + print("traitInfo is: ", pf(traitInfo)) + else: #Temp type + query = """SELECT %s FROM %s WHERE Name = %s + """ % (string.join(self.dataset.display_fields,','), + self.dataset.type, self.name) + traitInfo = g.db.execute(query).fetchone() + + + #self.cursor.execute(query) + #traitInfo = self.cursor.fetchone() + if traitInfo: + self.haveinfo = True + + #XZ: assign SQL query result to trait attributes. + for i, field in enumerate(self.dataset.display_fields): + setattr(self, field, traitInfo[i]) + + if self.dataset.type == 'Publish': + self.confidential = 0 + if self.pre_publication_description and not self.pubmed_id: + self.confidential = 1 + + self.homologeneid = None + if self.dataset.type == 'ProbeSet' and self.dataset.group and self.geneid: + #XZ, 05/26/2010: From time to time, this query get error message because some geneid values in database are not number. + #XZ: So I have to test if geneid is number before execute the query. + #XZ: The geneid values in database should be cleaned up. + try: + junk = float(self.geneid) + geneidIsNumber = 1 + except: + geneidIsNumber = 0 + + if geneidIsNumber: + query = """ + SELECT + HomologeneId + FROM + Homologene, Species, InbredSet + WHERE + Homologene.GeneId =%s AND + InbredSet.Name = '%s' AND + InbredSet.SpeciesId = Species.Id AND + Species.TaxonomyId = Homologene.TaxonomyId + """ % (escape(str(self.geneid)), escape(self.dataset.group.name)) + result = g.db.execute(query).fetchone() + else: + result = None + + if result: + self.homologeneid = result[0] + + if QTL: + if self.dataset.type == 'ProbeSet' and not self.cellid: + traitQTL = g.db.execute(""" + SELECT + ProbeSetXRef.Locus, ProbeSetXRef.LRS, ProbeSetXRef.pValue, ProbeSetXRef.mean + FROM + ProbeSetXRef, ProbeSet + WHERE + ProbeSetXRef.ProbeSetId = ProbeSet.Id AND + ProbeSet.Name = "%s" AND + ProbeSetXRef.ProbeSetFreezeId =%s + """, (self.name, self.dataset.id)).fetchone() + #self.cursor.execute(query) + #traitQTL = self.cursor.fetchone() + if traitQTL: + self.locus, self.lrs, self.pvalue, self.mean = traitQTL + else: + self.locus = self.lrs = self.pvalue = self.mean = "" + if self.dataset.type == 'Publish': + traitQTL = g.db.execute(""" + SELECT + PublishXRef.Locus, PublishXRef.LRS + FROM + PublishXRef, PublishFreeze + WHERE + PublishXRef.Id = %s AND + PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND + PublishFreeze.Id =%s + """, (self.name, self.dataset.id)).fetchone() + #self.cursor.execute(query) + #traitQTL = self.cursor.fetchone() + if traitQTL: + self.locus, self.lrs = traitQTL + else: + self.locus = self.lrs = "" + else: + raise KeyError, `self.name`+' information is not found in the database.' + + def genHTML(self, formName = "", dispFromDatabase=0, privilege="guest", userName="Guest", authorized_users=""): + if not self.haveinfo: + self.retrieveInfo() + + if self.dataset.type == 'Publish': + PubMedLink = "" + if self.pubmed_id: + PubMedLink = HT.Href(text="PubMed %d : " % self.pubmed_id, + target = "_blank", url = webqtlConfig.PUBMEDLINK_URL % self.pubmed_id) + else: + PubMedLink = HT.Span("Unpublished : ", Class="fs15") + + if formName: + setDescription2 = HT.Href(url="javascript:showDatabase3('%s','%s','%s','')" % + (formName, self.dataset.name, self.name), Class = "fs14") + else: + setDescription2 = HT.Href(url="javascript:showDatabase2('%s','%s','')" % + (self.dataset.name,self.name), Class = "fs14") + + if self.confidential and not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=privilege, userName=userName, authorized_users=authorized_users): + setDescription2.append('RecordID/%s - %s' % (self.name, self.pre_publication_description)) + else: + setDescription2.append('RecordID/%s - %s' % (self.name, self.post_publication_description)) + + #XZ 03/26/2011: Xiaodong comment out the following two lins as Rob asked. Need to check with Rob why in PublishXRef table, there are few row whose Sequence > 1. + #if self.sequence > 1: + # setDescription2.append(' btach %d' % self.sequence) + if self.authors: + a1 = string.split(self.authors,',')[0] + while a1[0] == '"' or a1[0] == "'" : + a1 = a1[1:] + setDescription2.append(' by ') + setDescription2.append(HT.Italic('%s, and colleagues' % a1)) + setDescription = HT.Span(PubMedLink, setDescription2) + + elif self.dataset.type == 'Temp': + setDescription = HT.Href(text="%s" % (self.description),url="javascript:showDatabase2\ + ('%s','%s','')" % (self.dataset.name,self.name), Class = "fs14") + setDescription = HT.Span(setDescription) + + elif self.dataset.type == 'Geno': # Genome DB only available for single search + if formName: + setDescription = HT.Href(text="Locus %s [Chr %s @ %s Mb]" % (self.name,self.chr,\ + '%2.3f' % self.mb),url="javascript:showDatabase3('%s','%s','%s','')" % \ + (formName, self.dataset.name, self.name), Class = "fs14") + else: + setDescription = HT.Href(text="Locus %s [Chr %s @ %s Mb]" % (self.name,self.chr,\ + '%2.3f' % self.mb),url="javascript:showDatabase2('%s','%s','')" % \ + (self.dataset.name,self.name), Class = "fs14") + + setDescription = HT.Span(setDescription) + + else: + if self.cellid: + if formName: + setDescription = HT.Href(text="ProbeSet/%s/%s" % (self.name, self.cellid),url=\ + "javascript:showDatabase3('%s','%s','%s','%s')" % (formName, self.dataset.name,self.name,self.cellid), \ + Class = "fs14") + else: + setDescription = HT.Href(text="ProbeSet/%s/%s" % (self.name,self.cellid),url=\ + "javascript:showDatabase2('%s','%s','%s')" % (self.dataset.name,self.name,self.cellid), \ + Class = "fs14") + else: + if formName: + setDescription = HT.Href(text="ProbeSet/%s" % self.name, url=\ + "javascript:showDatabase3('%s','%s','%s','')" % (formName, self.dataset.name,self.name), \ + Class = "fs14") + else: + setDescription = HT.Href(text="ProbeSet/%s" % self.name, url=\ + "javascript:showDatabase2('%s','%s','')" % (self.dataset.name,self.name), \ + Class = "fs14") + if self.symbol and self.chr and self.mb: + setDescription.append(' [') + setDescription.append(HT.Italic('%s' % self.symbol,Class="cdg fwb")) + setDescription.append(' on Chr %s @ %s Mb]' % (self.chr,self.mb)) + if self.description: + setDescription.append(': %s' % self.description) + if self.probe_target_description: + setDescription.append('; %s' % self.probe_target_description) + setDescription = HT.Span(setDescription) + + if self.dataset.type != 'Temp' and dispFromDatabase: + setDescription.append( ' --- FROM : ') + setDescription.append(self.dataset.genHTML(Class='cori')) + return setDescription + + @property + def description_fmt(self): + '''Return a text formated description''' + if self.description: + formatted = self.description + if self.probe_target_description: + formatted += "; " + self.probe_target_description + else: + formatted = "Not available" + return formatted.capitalize() + + @property + def alias_fmt(self): + '''Return a text formatted alias''' + if self.alias: + alias = string.replace(self.alias, ";", " ") + alias = string.join(string.split(alias), ", ") + return alias + + + @property + def location_fmt(self): + '''Return a text formatted location + + While we're at it we set self.location in case we need it later (do we?) + + ''' + + if self.chr and self.mb: + self.location = 'Chr %s @ %s Mb' % (self.chr,self.mb) + elif self.chr: + self.location = 'Chr %s @ Unknown position' % (self.chr) + else: + self.location = 'Not available' + + fmt = self.location + ##XZ: deal with direction + if self.strand_probe == '+': + fmt += (' on the plus strand ') + elif self.strand_probe == '-': + fmt += (' on the minus strand ') + + return fmt + + + def get_database(self): + """ + Returns the database, and the url referring to the database if it exists + + We're going to to return two values here, and we don't want to have to call this twice from + the template. So it's not a property called from the template, but instead is called from the view + + """ + if self.cellid: + self.cursor.execute(""" + select ProbeFreeze.Name from ProbeFreeze, ProbeSetFreeze + where + ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId AND + ProbeSetFreeze.Id = %d""" % thisTrait.dataset.id) + probeDBName = self.cursor.fetchone()[0] + return dict(name = probeDBName, + url = None) + else: + return dict(name = self.dataset.fullname, + url = webqtlConfig.INFOPAGEHREF % self.dataset.name) + + def calculate_correlation(self, values, method): + """Calculate the correlation value and p value according to the method specified""" + + #ZS: This takes the list of values of the trait our selected trait is being correlated against and removes the values of the samples our trait has no value for + #There's probably a better way of dealing with this, but I'll have to ask Christian + updated_raw_values = [] + updated_values = [] + for i in range(len(values)): + if values[i] != "None": + updated_raw_values.append(self.raw_values[i]) + updated_values.append(values[i]) + + self.raw_values = updated_raw_values + values = updated_values + + if method == METHOD_SAMPLE_PEARSON or method == METHOD_LIT or method == METHOD_TISSUE_PEARSON: + corr, nOverlap = webqtlUtil.calCorrelation(self.raw_values, values, len(values)) + else: + corr, nOverlap = webqtlUtil.calCorrelationRank(self.raw_values, values, len(values)) + + self.correlation = corr + self.overlap = nOverlap + + if self.overlap < 3: + self.p_value = 1.0 + else: + #ZS - This is probably the wrong way to deal with this. Correlation values of 1.0 definitely exist (the trait correlated against itself), so zero division needs to br prevented. + if abs(self.correlation) >= 1.0: + self.p_value = 0.0 + else: + ZValue = 0.5*log((1.0+self.correlation)/(1.0-self.correlation)) + ZValue = ZValue*sqrt(self.overlap-3) + self.p_value = 2.0*(1.0 - reaper.normp(abs(ZValue))) diff --git a/wqflask/base/webqtlTrait.py b/wqflask/base/webqtlTrait.py deleted file mode 100755 index 5367b41f..00000000 --- a/wqflask/base/webqtlTrait.py +++ /dev/null @@ -1,695 +0,0 @@ -from __future__ import division, print_function - -import string - -from htmlgen import HTMLgen2 as HT - -import webqtlConfig -from webqtlCaseData import webqtlCaseData -from data_set import create_dataset -from dbFunction import webqtlDatabaseFunction -from utility import webqtlUtil - -from MySQLdb import escape_string as escape -from pprint import pformat as pf - -from flask import Flask, g - -class GeneralTrait: - """ - Trait class defines a trait in webqtl, can be either Microarray, - Published phenotype, genotype, or user input trait - - """ - - def __init__(self, **kw): - print("in GeneralTrait") - self.dataset = kw.get('dataset', None) # database object - self.name = kw.get('name', None) # Trait ID, ProbeSet ID, Published ID, etc. - self.cellid = kw.get('cellid', None) - self.identification = kw.get('identification', 'un-named trait') - self.group = kw.get('group', None) - self.haveinfo = kw.get('haveinfo', False) - self.sequence = kw.get('sequence', None) # Blat sequence, available for ProbeSet - self.data = kw.get('data', {}) - - if kw.get('fullname'): - name2 = value.split("::") - if len(name2) == 2: - self.dataset, self.name = name2 - elif len(name2) == 3: - self.dataset, self.name, self.cellid = name2 - - #if self.dataset and isinstance(self.dataset, basestring): - self.dataset = create_dataset(self.dataset.name) - - print("self.dataset is:", self.dataset, type(self.dataset)) - #if self.dataset: - - self.dataset.get_group() - - if self.dataset.type == "Temp": - self.cursor.execute(''' - SELECT - InbredSet.Name - FROM - InbredSet, Temp - WHERE - Temp.InbredSetId = InbredSet.Id AND - Temp.Name = "%s" - ''', self.name) - self.group = self.cursor.fetchone()[0] - else: - self.group = self.dataset.get_group() - - print("trinity, self.group is:", self.group) - - # - # In ProbeSet, there are maybe several annotations match one sequence - # so we need use sequence(BlatSeq) as the identification, when we update - # one annotation, we update the others who match the sequence also. - # - # Hongqiang Li, 3/3/2008 - # - - #XZ, 05/08/2009: This block is not neccessary. We can add 'BlatSeq' into disfield. - # The variable self.sequence should be changed to self.BlatSeq - # It also should be changed in other places where it are used. - - #if self.dataset: - if self.dataset.type == 'ProbeSet': - print("Doing ProbeSet Query") - query = ''' - SELECT - ProbeSet.BlatSeq - FROM - ProbeSet, ProbeSetFreeze, ProbeSetXRef - WHERE - ProbeSet.Id=ProbeSetXRef.ProbeSetId and - ProbeSetFreeze.Id = ProbeSetXRef.ProbeSetFreezeId and - ProbeSet.Name = %s and - ProbeSetFreeze.Name = %s - ''', (self.name, self.dataset.name) - print("query is:", query) - self.sequence = g.db.execute(*query).fetchone()[0] - #self.sequence = self.cursor.fetchone()[0] - print("self.sequence is:", self.sequence) - - - def getName(self): - str = "" - if self.dataset and self.name: - str = "%s::%s" % (self.dataset, self.name) - if self.cellid: - str += "::" + self.cellid - else: - str = self.description - return str - - # - # when user enter a trait or GN generate a trait, user want show the name - # not the name that generated by GN randomly, the two follow function are - # used to give the real name and the database. displayName() will show the - # database also, getGivenName() just show the name. - # For other trait, displayName() as same as getName(), getGivenName() as - # same as self.name - # - # Hongqiang 11/29/07 - # - def getGivenName(self): - str = self.name - if self.dataset and self.name: - if self.dataset.type=='Temp': - self.cursor.execute('SELECT description FROM Temp WHERE Name=%s', self.name) - desc = self.cursor.fetchone()[0] - if desc.__contains__('PCA'): - desc = desc[desc.rindex(':')+1:].strip() - else: - desc = desc[:desc.index('entered')].strip() - str = desc - return str - - def displayName(self): - str = "" - if self.dataset and self.name: - if self.dataset.type=='Temp': - desc = self.description - if desc.__contains__('PCA'): - desc = desc[desc.rindex(':')+1:].strip() - else: - desc = desc[:desc.index('entered')].strip() - str = "%s::%s" % (self.dataset, desc) - else: - str = "%s::%s" % (self.dataset, self.name) - if self.cellid: - str += "::" + self.cellid - else: - str = self.description - - return str - - - #def __str__(self): - # #return "%s %s" % (self.getName(), self.group) - # return self.getName() - #__str__ = getName - #__repr__ = __str__ - - def exportData(self, samplelist, type="val"): - """ - export data according to samplelist - mostly used in calculating correlation - """ - result = [] - for sample in samplelist: - if self.data.has_key(sample): - if type=='val': - result.append(self.data[sample].val) - elif type=='var': - result.append(self.data[sample].var) - elif type=='N': - result.append(self.data[sample].N) - else: - raise KeyError, `type`+' type is incorrect.' - else: - result.append(None) - return result - - def exportInformative(self, incVar=0): - """ - export informative sample - mostly used in qtl regression - """ - samples = [] - vals = [] - vars = [] - for sample, value in self.data.items(): - if value.val != None: - if not incVar or value.var != None: - samples.append(sample) - vals.append(value.val) - vars.append(value.var) - return samples, vals, vars - - - # - # In ProbeSet, there are maybe several annotations match one sequence - # so we need use sequence(BlatSeq) as the identification, when we update - # one annotation, we update the others who match the sequence also. - # - # Hongqiang Li, 3/3/2008 - # - def getSequence(self): - assert self.cursor - if self.dataset.type == 'ProbeSet': - self.cursor.execute(''' - SELECT - ProbeSet.BlatSeq - FROM - ProbeSet, ProbeSetFreeze, ProbeSetXRef - WHERE - ProbeSet.Id=ProbeSetXRef.ProbeSetId and - ProbeSetFreeze.Id = ProbeSetXRef.ProbSetFreezeId and - ProbeSet.Name = %s - ProbeSetFreeze.Name = %s - ''', self.name, self.dataset.name) - #self.cursor.execute(query) - results = self.fetchone() - - return results[0] - - - - def retrieveData(self, samplelist=None): - - if samplelist == None: - samplelist = [] - assert self.dataset and self.cursor - - if self.dataset.type == 'Temp': - query = ''' - SELECT - Strain.Name, TempData.value, TempData.SE, TempData.NStrain, TempData.Id - FROM - TempData, Temp, Strain - WHERE - TempData.StrainId = Strain.Id AND - TempData.Id = Temp.DataId AND - Temp.name = '%s' - Order BY - Strain.Name - ''' % self.name - #XZ, 03/02/2009: Xiaodong changed Data to PublishData, SE to PublishSE - elif self.dataset.type == 'Publish': - query = ''' - SELECT - Strain.Name, PublishData.value, PublishSE.error, NStrain.count, PublishData.Id - FROM - (PublishData, Strain, PublishXRef, PublishFreeze) - left join PublishSE on - (PublishSE.DataId = PublishData.Id AND PublishSE.StrainId = PublishData.StrainId) - left join NStrain on - (NStrain.DataId = PublishData.Id AND - NStrain.StrainId = PublishData.StrainId) - WHERE - PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND - PublishData.Id = PublishXRef.DataId AND PublishXRef.Id = %s AND - PublishFreeze.Id = %d AND PublishData.StrainId = Strain.Id - Order BY - Strain.Name - ''' % (self.name, self.dataset.id) - - #XZ, 03/02/2009: Xiaodong changed Data to ProbeData, SE to ProbeSE - elif self.cellid: - #Probe Data - query = ''' - SELECT - Strain.Name, ProbeData.value, ProbeSE.error, ProbeData.Id - FROM - (ProbeData, ProbeFreeze, ProbeSetFreeze, ProbeXRef, - Strain, Probe, ProbeSet) - left join ProbeSE on - (ProbeSE.DataId = ProbeData.Id AND ProbeSE.StrainId = ProbeData.StrainId) - WHERE - Probe.Name = '%s' AND ProbeSet.Name = '%s' AND - Probe.ProbeSetId = ProbeSet.Id AND - ProbeXRef.ProbeId = Probe.Id AND - ProbeXRef.ProbeFreezeId = ProbeFreeze.Id AND - ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id AND - ProbeSetFreeze.Name = '%s' AND - ProbeXRef.DataId = ProbeData.Id AND - ProbeData.StrainId = Strain.Id - Order BY - Strain.Name - ''' % (self.cellid, self.name, self.dataset.name) - #XZ, 03/02/2009: Xiaodong added this block for ProbeSetData and ProbeSetSE - elif self.dataset.type == 'ProbeSet': - #ProbeSet Data - query = ''' - SELECT - Strain.Name, ProbeSetData.value, ProbeSetSE.error, ProbeSetData.Id - FROM - (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef) - left join ProbeSetSE on - (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId) - WHERE - ProbeSet.Name = '%s' AND ProbeSetXRef.ProbeSetId = ProbeSet.Id AND - ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND - ProbeSetFreeze.Name = '%s' AND - ProbeSetXRef.DataId = ProbeSetData.Id AND - ProbeSetData.StrainId = Strain.Id - Order BY - Strain.Name - ''' % (self.name, self.dataset.name) - #XZ, 03/02/2009: Xiaodong changeded Data to GenoData, SE to GenoSE - else: - #Geno Data - #XZ: The SpeciesId is not necessary, but it's nice to keep it to speed up database search. - query = ''' - SELECT - Strain.Name, GenoData.value, GenoSE.error, GenoData.Id - FROM - (GenoData, GenoFreeze, Strain, Geno, GenoXRef) - left join GenoSE on - (GenoSE.DataId = GenoData.Id AND GenoSE.StrainId = GenoData.StrainId) - WHERE - Geno.SpeciesId = %s AND Geno.Name = '%s' AND GenoXRef.GenoId = Geno.Id AND - GenoXRef.GenoFreezeId = GenoFreeze.Id AND - GenoFreeze.Name = '%s' AND - GenoXRef.DataId = GenoData.Id AND - GenoData.StrainId = Strain.Id - Order BY - Strain.Name - ''' % (webqtlDatabaseFunction.retrieveSpeciesId(self.cursor, self.dataset.group), self.name, self.dataset.name) - - - self.cursor.execute(query) - results = self.cursor.fetchall() - self.data.clear() - - if results: - self.mysqlid = results[0][-1] - #if samplelist: - for item in results: - #name, value, variance, num_cases = item - if not samplelist or (samplelist and name in samplelist): - #if value != None: - # num_cases = None - # if self.dataset.type in ('Publish', 'Temp'): - # ndata = item[3] - name = item[0] - self.data[name] = webqtlCaseData(*item) #name, value, variance, num_cases) - #end for - # else: - # for item in results: - # val = item[1] - # if val != None: - # var = item[2] - # ndata = None - # if self.dataset.type in ('Publish', 'Temp'): - # ndata = item[3] - # self.data[item[0]] = webqtlCaseData(val, var, ndata) - # #end for - # #end if - #else: - # pass - - #def keys(self): - # return self.__dict__.keys() - # - #def has_key(self, key): - # return self.__dict__.has_key(key) - # - #def items(self): - # return self.__dict__.items() - - def retrieve_info(self, QTL=False): - assert self.dataset, "Dataset doesn't exist" - if self.dataset.type == 'Publish': - query = """ - SELECT - PublishXRef.Id, Publication.PubMed_ID, - Phenotype.Pre_publication_description, Phenotype.Post_publication_description, Phenotype.Original_description, - Phenotype.Pre_publication_abbreviation, Phenotype.Post_publication_abbreviation, - Phenotype.Lab_code, Phenotype.Submitter, Phenotype.Owner, Phenotype.Authorized_Users, - Publication.Authors, Publication.Title, Publication.Abstract, - Publication.Journal, Publication.Volume, Publication.Pages, - Publication.Month, Publication.Year, PublishXRef.Sequence, - Phenotype.Units, PublishXRef.comments - FROM - PublishXRef, Publication, Phenotype, PublishFreeze - WHERE - PublishXRef.Id = %s AND - Phenotype.Id = PublishXRef.PhenotypeId AND - Publication.Id = PublishXRef.PublicationId AND - PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND - PublishFreeze.Id = %s - """ % (self.name, self.dataset.id) - traitInfo = g.db.execute(query).fetchone() - #XZ, 05/08/2009: Xiaodong add this block to use ProbeSet.Id to find the probeset instead of just using ProbeSet.Name - #XZ, 05/08/2009: to avoid the problem of same probeset name from different platforms. - elif self.dataset.type == 'ProbeSet': - display_fields_string = ', ProbeSet.'.join(self.dataset.display_fields) - display_fields_string = 'ProbeSet.' + display_fields_string - query = """ - SELECT %s - FROM ProbeSet, ProbeSetFreeze, ProbeSetXRef - WHERE - ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND - ProbeSetXRef.ProbeSetId = ProbeSet.Id AND - ProbeSetFreeze.Name = '%s' AND - ProbeSet.Name = '%s' - """ % (display_fields_string, self.dataset.name, self.name) - traitInfo = g.db.execute(query).fetchone() - print("traitInfo is: ", pf(traitInfo)) - #XZ, 05/08/2009: We also should use Geno.Id to find marker instead of just using Geno.Name - # to avoid the problem of same marker name from different species. - elif self.dataset.type == 'Geno': - display_fields_string = string.join(self.dataset.display_fields,',Geno.') - display_fields_string = 'Geno.' + display_fields_string - query = """ - SELECT %s - FROM Geno, GenoFreeze, GenoXRef - WHERE - GenoXRef.GenoFreezeId = GenoFreeze.Id AND - GenoXRef.GenoId = Geno.Id AND - GenoFreeze.Name = '%s' AND - Geno.Name = '%s' - """ % (display_fields_string, self.dataset.name, self.name) - traitInfo = g.db.execute(query).fetchone() - print("traitInfo is: ", pf(traitInfo)) - else: #Temp type - query = """SELECT %s FROM %s WHERE Name = %s - """ % (string.join(self.dataset.display_fields,','), - self.dataset.type, self.name) - traitInfo = g.db.execute(query).fetchone() - - - #self.cursor.execute(query) - #traitInfo = self.cursor.fetchone() - if traitInfo: - self.haveinfo = True - - #XZ: assign SQL query result to trait attributes. - for i, field in enumerate(self.dataset.display_fields): - setattr(self, field, traitInfo[i]) - - if self.dataset.type == 'Publish': - self.confidential = 0 - if self.pre_publication_description and not self.pubmed_id: - self.confidential = 1 - - self.homologeneid = None - if self.dataset.type == 'ProbeSet' and self.group and self.geneid: - #XZ, 05/26/2010: From time to time, this query get error message because some geneid values in database are not number. - #XZ: So I have to test if geneid is number before execute the query. - #XZ: The geneid values in database should be cleaned up. - try: - junk = float(self.geneid) - geneidIsNumber = 1 - except: - geneidIsNumber = 0 - - if geneidIsNumber: - result = g.db.execute(""" - SELECT - HomologeneId - FROM - Homologene, Species, InbredSet - WHERE - Homologene.GeneId =%s AND - InbredSet.Name = '%s' AND - InbredSet.SpeciesId = Species.Id AND - Species.TaxonomyId = Homologene.TaxonomyId - """, (self.geneid, self.group)).fetchone() - #self.cursor.execute(query) - #result = self.cursor.fetchone() - else: - result = None - - if result: - self.homologeneid = result[0] - - if QTL: - if self.dataset.type == 'ProbeSet' and not self.cellid: - traitQTL = g.db.execute(""" - SELECT - ProbeSetXRef.Locus, ProbeSetXRef.LRS, ProbeSetXRef.pValue, ProbeSetXRef.mean - FROM - ProbeSetXRef, ProbeSet - WHERE - ProbeSetXRef.ProbeSetId = ProbeSet.Id AND - ProbeSet.Name = "%s" AND - ProbeSetXRef.ProbeSetFreezeId =%s - """, (self.name, self.dataset.id)).fetchone() - #self.cursor.execute(query) - #traitQTL = self.cursor.fetchone() - if traitQTL: - self.locus, self.lrs, self.pvalue, self.mean = traitQTL - else: - self.locus = self.lrs = self.pvalue = self.mean = "" - if self.dataset.type == 'Publish': - traitQTL = g.db.execute(""" - SELECT - PublishXRef.Locus, PublishXRef.LRS - FROM - PublishXRef, PublishFreeze - WHERE - PublishXRef.Id = %s AND - PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND - PublishFreeze.Id =%s - """, (self.name, self.dataset.id)).fetchone() - #self.cursor.execute(query) - #traitQTL = self.cursor.fetchone() - if traitQTL: - self.locus, self.lrs = traitQTL - else: - self.locus = self.lrs = "" - else: - raise KeyError, `self.name`+' information is not found in the database.' - - def genHTML(self, formName = "", dispFromDatabase=0, privilege="guest", userName="Guest", authorized_users=""): - if not self.haveinfo: - self.retrieveInfo() - - if self.dataset.type == 'Publish': - PubMedLink = "" - if self.pubmed_id: - PubMedLink = HT.Href(text="PubMed %d : " % self.pubmed_id, - target = "_blank", url = webqtlConfig.PUBMEDLINK_URL % self.pubmed_id) - else: - PubMedLink = HT.Span("Unpublished : ", Class="fs15") - - if formName: - setDescription2 = HT.Href(url="javascript:showDatabase3('%s','%s','%s','')" % - (formName, self.dataset.name, self.name), Class = "fs14") - else: - setDescription2 = HT.Href(url="javascript:showDatabase2('%s','%s','')" % - (self.dataset.name,self.name), Class = "fs14") - - if self.confidential and not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=privilege, userName=userName, authorized_users=authorized_users): - setDescription2.append('RecordID/%s - %s' % (self.name, self.pre_publication_description)) - else: - setDescription2.append('RecordID/%s - %s' % (self.name, self.post_publication_description)) - - #XZ 03/26/2011: Xiaodong comment out the following two lins as Rob asked. Need to check with Rob why in PublishXRef table, there are few row whose Sequence > 1. - #if self.sequence > 1: - # setDescription2.append(' btach %d' % self.sequence) - if self.authors: - a1 = string.split(self.authors,',')[0] - while a1[0] == '"' or a1[0] == "'" : - a1 = a1[1:] - setDescription2.append(' by ') - setDescription2.append(HT.Italic('%s, and colleagues' % a1)) - setDescription = HT.Span(PubMedLink, setDescription2) - - elif self.dataset.type == 'Temp': - setDescription = HT.Href(text="%s" % (self.description),url="javascript:showDatabase2\ - ('%s','%s','')" % (self.dataset.name,self.name), Class = "fs14") - setDescription = HT.Span(setDescription) - - elif self.dataset.type == 'Geno': # Genome DB only available for single search - if formName: - setDescription = HT.Href(text="Locus %s [Chr %s @ %s Mb]" % (self.name,self.chr,\ - '%2.3f' % self.mb),url="javascript:showDatabase3('%s','%s','%s','')" % \ - (formName, self.dataset.name, self.name), Class = "fs14") - else: - setDescription = HT.Href(text="Locus %s [Chr %s @ %s Mb]" % (self.name,self.chr,\ - '%2.3f' % self.mb),url="javascript:showDatabase2('%s','%s','')" % \ - (self.dataset.name,self.name), Class = "fs14") - - setDescription = HT.Span(setDescription) - - else: - if self.cellid: - if formName: - setDescription = HT.Href(text="ProbeSet/%s/%s" % (self.name, self.cellid),url=\ - "javascript:showDatabase3('%s','%s','%s','%s')" % (formName, self.dataset.name,self.name,self.cellid), \ - Class = "fs14") - else: - setDescription = HT.Href(text="ProbeSet/%s/%s" % (self.name,self.cellid),url=\ - "javascript:showDatabase2('%s','%s','%s')" % (self.dataset.name,self.name,self.cellid), \ - Class = "fs14") - else: - if formName: - setDescription = HT.Href(text="ProbeSet/%s" % self.name, url=\ - "javascript:showDatabase3('%s','%s','%s','')" % (formName, self.dataset.name,self.name), \ - Class = "fs14") - else: - setDescription = HT.Href(text="ProbeSet/%s" % self.name, url=\ - "javascript:showDatabase2('%s','%s','')" % (self.dataset.name,self.name), \ - Class = "fs14") - if self.symbol and self.chr and self.mb: - setDescription.append(' [') - setDescription.append(HT.Italic('%s' % self.symbol,Class="cdg fwb")) - setDescription.append(' on Chr %s @ %s Mb]' % (self.chr,self.mb)) - if self.description: - setDescription.append(': %s' % self.description) - if self.probe_target_description: - setDescription.append('; %s' % self.probe_target_description) - setDescription = HT.Span(setDescription) - - if self.dataset.type != 'Temp' and dispFromDatabase: - setDescription.append( ' --- FROM : ') - setDescription.append(self.dataset.genHTML(Class='cori')) - return setDescription - - @property - def description_fmt(self): - '''Return a text formated description''' - if self.description: - formatted = self.description - if self.probe_target_description: - formatted += "; " + self.probe_target_description - else: - formatted = "Not available" - return formatted.capitalize() - - @property - def alias_fmt(self): - '''Return a text formatted alias''' - if self.alias: - alias = string.replace(self.alias, ";", " ") - alias = string.join(string.split(alias), ", ") - return alias - - - @property - def location_fmt(self): - '''Return a text formatted location - - While we're at it we set self.location in case we need it later (do we?) - - ''' - - if self.chr and self.mb: - self.location = 'Chr %s @ %s Mb' % (self.chr,self.mb) - elif self.chr: - self.location = 'Chr %s @ Unknown position' % (self.chr) - else: - self.location = 'Not available' - - fmt = self.location - ##XZ: deal with direction - if self.strand_probe == '+': - fmt += (' on the plus strand ') - elif self.strand_probe == '-': - fmt += (' on the minus strand ') - - return fmt - - - def get_database(self): - """ - Returns the database, and the url referring to the database if it exists - - We're going to to return two values here, and we don't want to have to call this twice from - the template. So it's not a property called from the template, but instead is called from the view - - """ - if self.cellid: - self.cursor.execute(""" - select ProbeFreeze.Name from ProbeFreeze, ProbeSetFreeze - where - ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId AND - ProbeSetFreeze.Id = %d""" % thisTrait.dataset.id) - probeDBName = self.cursor.fetchone()[0] - return dict(name = probeDBName, - url = None) - else: - return dict(name = self.dataset.fullname, - url = webqtlConfig.INFOPAGEHREF % self.dataset.name) - - def calculate_correlation(self, values, method): - """Calculate the correlation value and p value according to the method specified""" - - #ZS: This takes the list of values of the trait our selected trait is being correlated against and removes the values of the samples our trait has no value for - #There's probably a better way of dealing with this, but I'll have to ask Christian - updated_raw_values = [] - updated_values = [] - for i in range(len(values)): - if values[i] != "None": - updated_raw_values.append(self.raw_values[i]) - updated_values.append(values[i]) - - self.raw_values = updated_raw_values - values = updated_values - - if method == METHOD_SAMPLE_PEARSON or method == METHOD_LIT or method == METHOD_TISSUE_PEARSON: - corr, nOverlap = webqtlUtil.calCorrelation(self.raw_values, values, len(values)) - else: - corr, nOverlap = webqtlUtil.calCorrelationRank(self.raw_values, values, len(values)) - - self.correlation = corr - self.overlap = nOverlap - - if self.overlap < 3: - self.p_value = 1.0 - else: - #ZS - This is probably the wrong way to deal with this. Correlation values of 1.0 definitely exist (the trait correlated against itself), so zero division needs to br prevented. - if abs(self.correlation) >= 1.0: - self.p_value = 0.0 - else: - ZValue = 0.5*log((1.0+self.correlation)/(1.0-self.correlation)) - ZValue = ZValue*sqrt(self.overlap-3) - self.p_value = 2.0*(1.0 - reaper.normp(abs(ZValue))) diff --git a/wqflask/wqflask/do_search.py b/wqflask/wqflask/do_search.py index 4301fb50..69602748 100644 --- a/wqflask/wqflask/do_search.py +++ b/wqflask/wqflask/do_search.py @@ -26,10 +26,11 @@ class DoSearch(object): assert search_operator in (None, "=", "<", ">", "<=", ">="), "Bad search operator" self.search_operator = search_operator self.dataset = dataset + print("self.dataset is boo: ", type(self.dataset), pf(self.dataset)) + print("self.dataset.group is: ", pf(self.dataset.group)) #Get group information for dataset and the species id - self.dataset.get_group() - self.species_id = webqtlDatabaseFunction.retrieve_species_id(self.dataset.group) + self.species_id = webqtlDatabaseFunction.retrieve_species_id(self.dataset.group.name) def execute(self, query): """Executes query and returns results""" diff --git a/wqflask/wqflask/search_results.py b/wqflask/wqflask/search_results.py index cd478110..7c50dfeb 100644 --- a/wqflask/wqflask/search_results.py +++ b/wqflask/wqflask/search_results.py @@ -30,7 +30,7 @@ from base import webqtlConfig from utility.THCell import THCell from utility.TDCell import TDCell from base.data_set import create_dataset -from base.webqtlTrait import GeneralTrait +from base.trait import GeneralTrait from base.templatePage import templatePage from wqflask import parser from wqflask import do_search @@ -99,8 +99,7 @@ class SearchResultPage(templatePage): """ self.trait_list = [] - group = self.dataset.group - species = webqtlDatabaseFunction.retrieve_species(group=group) + species = webqtlDatabaseFunction.retrieve_species(self.dataset.group.name) # result_set represents the results for each search term; a search of # "shh grin2b" would have two sets of results, one for each term diff --git a/wqflask/wqflask/show_trait/show_trait.py b/wqflask/wqflask/show_trait/show_trait.py index aef9219f..2bc4fc9c 100755 --- a/wqflask/wqflask/show_trait/show_trait.py +++ b/wqflask/wqflask/show_trait/show_trait.py @@ -13,7 +13,8 @@ from base import webqtlConfig from base import webqtlCaseData from wqflask.show_trait.SampleList import SampleList from utility import webqtlUtil, Plot, Bunch -from base.webqtlTrait import GeneralTrait +from base.trait import GeneralTrait +from base.data_set import create_dataset from dbFunction import webqtlDatabaseFunction from base.templatePage import templatePage from basicStatistics import BasicStatisticsFunctions @@ -33,105 +34,111 @@ class ShowTrait(templatePage): def __init__(self, args): print("in ShowTrait, args are:", args) - self.group = args.group - self.trait_id = trait_id - self.dataset = dataset + #self.group = args.group + self.trait_id = args['trait_id'] + self.dataset = create_dataset(args['dataset']) + self.cell_id = None #assert self.openMysql(), "No database!" #print("red3 fd.group:", fd.group) this_trait = self.get_this_trait() - print("red4 fd.group:", fd.group) + #print("red4 fd.group:", fd.group) ##read genotype file - fd.group = this_trait.group + #fd.group = this_trait.group - print("[red5] fd.group is:", fd.group) - fd.readGenotype() + #print("[red5] fd.group is:", fd.group) + self.dataset.group.read_genotype_file() + #fd.readGenotype() - if not fd.genotype: - fd.readData(incf1=1) + if not self.dataset.group.genotype: + self.read_data(incf1=1) - # determine data editing page format - variance_data_page = 0 - if fd.formID == 'varianceChoice': - variance_data_page = 1 - - if variance_data_page: - fmID='dataEditing' - else: - if fd.enablevariance: - fmID='pre_dataEditing' - else: - fmID='dataEditing' - - # Some fields, like method, are defaulted to None; otherwise in IE the field can't be changed using jquery - hddn = OrderedDict( - FormID = fmID, - group = fd.group, - submitID = '', - scale = 'physic', - additiveCheck = 'ON', - showSNP = 'ON', - showGenes = 'ON', - method = None, - parentsf14regression = 'OFF', - stats_method = '1', - chromosomes = '-1', - topten = '', - viewLegend = 'ON', - intervalAnalystCheck = 'ON', - valsHidden = 'OFF', - database = '', - criteria = None, - MDPChoice = None, - bootCheck = None, - permCheck = None, - applyVarianceSE = None, - sampleNames = '_', - sampleVals = '_', - sampleVars = '_', - otherStrainNames = '_', - otherStrainVals = '_', - otherStrainVars = '_', - extra_attributes = '_', - other_extra_attributes = '_', - export_data = None - ) - - if fd.enablevariance: - hddn['enablevariance']='ON' - if fd.incparentsf1: - hddn['incparentsf1']='ON' - - if this_trait: - hddn['fullname'] = str(this_trait) - try: - hddn['normalPlotTitle'] = this_trait.symbol - hddn['normalPlotTitle'] += ": " - hddn['normalPlotTitle'] += this_trait.name - except: - hddn['normalPlotTitle'] = str(this_trait.name) - hddn['fromDataEditingPage'] = 1 - if this_trait.dataset and this_trait.dataset.type and this_trait.dataset.type == 'ProbeSet': - hddn['trait_type'] = this_trait.dataset.type - if this_trait.cellid: - hddn['cellid'] = this_trait.cellid - else: - self.cursor.execute("SELECT h2 from ProbeSetXRef WHERE DataId = %d" % - this_trait.mysqlid) - heritability = self.cursor.fetchone() - hddn['heritability'] = heritability - - hddn['attribute_names'] = "" - - hddn['mappingMethodId'] = webqtlDatabaseFunction.getMappingMethod (cursor=self.cursor, - groupName=fd.group) - - if fd.identification: - hddn['identification'] = fd.identification - else: - hddn['identification'] = "Un-named trait" #If no identification, set identification to un-named + ## determine data editing page format + #variance_data_page = 0 + #if fd.formID == 'varianceChoice': + # variance_data_page = 1 + # + #if variance_data_page: + # fmID='dataEditing' + #else: + # if fd.enablevariance: + # fmID='pre_dataEditing' + # else: + # fmID='dataEditing' + + # Todo: Add back in the ones we actually need from below, as we discover we need them + hddn = OrderedDict() + + + ## Some fields, like method, are defaulted to None; otherwise in IE the field can't be changed using jquery + #hddn = OrderedDict( + # FormID = fmID, + # group = fd.group, + # submitID = '', + # scale = 'physic', + # additiveCheck = 'ON', + # showSNP = 'ON', + # showGenes = 'ON', + # method = None, + # parentsf14regression = 'OFF', + # stats_method = '1', + # chromosomes = '-1', + # topten = '', + # viewLegend = 'ON', + # intervalAnalystCheck = 'ON', + # valsHidden = 'OFF', + # database = '', + # criteria = None, + # MDPChoice = None, + # bootCheck = None, + # permCheck = None, + # applyVarianceSE = None, + # sampleNames = '_', + # sampleVals = '_', + # sampleVars = '_', + # otherStrainNames = '_', + # otherStrainVals = '_', + # otherStrainVars = '_', + # extra_attributes = '_', + # other_extra_attributes = '_', + # export_data = None + # ) + + #if fd.enablevariance: + # hddn['enablevariance']='ON' + #if fd.incparentsf1: + # hddn['incparentsf1']='ON' + + #if this_trait: + # hddn['fullname'] = str(this_trait) + # try: + # hddn['normalPlotTitle'] = this_trait.symbol + # hddn['normalPlotTitle'] += ": " + # hddn['normalPlotTitle'] += this_trait.name + # except: + # hddn['normalPlotTitle'] = str(this_trait.name) + # hddn['fromDataEditingPage'] = 1 + # if this_trait.dataset and this_trait.dataset.type and this_trait.dataset.type == 'ProbeSet': + # hddn['trait_type'] = this_trait.dataset.type + # if this_trait.cellid: + # hddn['cellid'] = this_trait.cellid + # else: + # self.cursor.execute("SELECT h2 from ProbeSetXRef WHERE DataId = %d" % + # this_trait.mysqlid) + # heritability = self.cursor.fetchone() + # hddn['heritability'] = heritability + # + # hddn['attribute_names'] = "" + # + #hddn['mappingMethodId'] = webqtlDatabaseFunction.getMappingMethod (cursor=self.cursor, + # groupName=fd.group) + # + #if fd.identification: + # hddn['identification'] = fd.identification + #else: + # hddn['identification'] = "Un-named trait" #If no identification, set identification to un-named self.dispTraitInformation(fd, "", hddn, this_trait) #Display trait information + function buttons @@ -186,27 +193,109 @@ class ShowTrait(templatePage): #trait_id = self.fd['trait_id'] #cell_id = self.fd.get('CellID') - this_trait = webqtlTrait(dataset=dataset, - name=trait_id, - cellid=cell_id) + this_trait = GeneralTrait(dataset=self.dataset.name, + name=self.trait_id, + cellid=self.cell_id) ##identification, etc. - self.fd.identification = '%s : %s' % (this_trait.dataset.shortname, trait_id) + self.identification = '%s : %s' % (self.dataset.shortname, self.trait_id) this_trait.returnURL = webqtlConfig.CGIDIR + webqtlConfig.SCRIPTFILE + '?FormID=showDatabase&database=%s\ - &ProbeSetID=%s&group=%s&parentsf1=on' %(dataset, trait_id, self.fd['group']) + &ProbeSetID=%s&group=%s&parentsf1=on' %(self.dataset, self.trait_id, self.dataset.group.name) - if cell_id: - self.fd.identification = '%s/%s'%(self.fd.identification, cell_id) - this_trait.returnURL = '%s&CellID=%s' % (this_trait.returnURL, cell_id) + if self.cell_id: + self.identification = '%s/%s'%(self.identification, self.cell_id) + this_trait.returnURL = '%s&CellID=%s' % (this_trait.returnURL, self.cell_id) - print("yellow1:", self.group) - this_trait.retrieveInfo() - print("yellow2:", self.group) - this_trait.retrieveData() - print("yellow3:", self.group) + print("yellow1:", self.dataset.group) + this_trait.retrieve_info() + print("yellow2:", self.dataset.group) + this_trait.retrieve_sample_data() + print("yellow3:", self.dataset.group) return this_trait + def read_data(self): + '''read user input data or from trait data and analysis form''' + + if incf1 == None: + incf1 = [] + + if not self.genotype: + self.readGenotype() + if not samplelist: + if incf1: + samplelist = self.f1list + self.samplelist + else: + samplelist = self.samplelist + + #print("before traitfiledata self.traitfile is:", pf(self.traitfile)) + + traitfiledata = getattr(self, "traitfile", None) + traitpastedata = getattr(self, "traitpaste", None) + variancefiledata = getattr(self, "variancefile", None) + variancepastedata = getattr(self, "variancepaste", None) + Nfiledata = getattr(self, "Nfile", None) + + #### Todo: Rewrite below when we get to someone submitting their own trait ##### + + def to_float(item): + try: + return float(item) + except ValueError: + return None + + print("bottle samplelist is:", samplelist) + if traitfiledata: + tt = traitfiledata.split() + values = map(webqtlUtil.StringAsFloat, tt) + elif traitpastedata: + tt = traitpastedata.split() + values = map(webqtlUtil.StringAsFloat, tt) + else: + print("mapping formdataasfloat") + #values = map(self.FormDataAsFloat, samplelist) + values = [to_float(getattr(self, key)) for key in samplelist] + print("rocket values is:", values) + + + if len(values) < len(samplelist): + values += [None] * (len(samplelist) - len(values)) + elif len(values) > len(samplelist): + values = values[:len(samplelist)] + print("now values is:", values) + + + if variancefiledata: + tt = variancefiledata.split() + variances = map(webqtlUtil.StringAsFloat, tt) + elif variancepastedata: + tt = variancepastedata.split() + variances = map(webqtlUtil.StringAsFloat, tt) + else: + variances = map(self.FormVarianceAsFloat, samplelist) + + if len(variances) < len(samplelist): + variances += [None]*(len(samplelist) - len(variances)) + elif len(variances) > len(samplelist): + variances = variances[:len(samplelist)] + + if Nfiledata: + tt = string.split(Nfiledata) + nsamples = map(webqtlUtil.IntAsFloat, tt) + if len(nsamples) < len(samplelist): + nsamples += [None]*(len(samplelist) - len(nsamples)) + else: + nsamples = map(self.FormNAsFloat, samplelist) + + ##values, variances, nsamples is obsolete + self.allTraitData = {} + for i, _sample in enumerate(samplelist): + if values[i] != None: + self.allTraitData[_sample] = webqtlCaseData( + _sample, values[i], variances[i], nsamples[i]) + print("allTraitData is:", pf(self.allTraitData)) + + def dispTraitInformation(self, fd, title1Body, hddn, this_trait): _Species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, group=fd.group) -- cgit v1.2.3 From 2520342c689233a82b70b5328a044add87169d84 Mon Sep 17 00:00:00 2001 From: Zachary Sloan Date: Fri, 4 Jan 2013 17:09:27 -0600 Subject: Correct results are being returned from reaper for the marker regression page --- misc/notes.txt | 13 +- misc/todo.txt | 3 +- .../wqflask/marker_regression/marker_regression.py | 255 ++++++++++++--------- .../wqflask/templates/show_trait_edit_data.html | 4 +- .../templates/show_trait_mapping_tools.html | 13 +- 5 files changed, 173 insertions(+), 115 deletions(-) (limited to 'misc') diff --git a/misc/notes.txt b/misc/notes.txt index b0c0762c..306cadeb 100644 --- a/misc/notes.txt +++ b/misc/notes.txt @@ -57,12 +57,6 @@ unset SSH_ASKPASS =========================================== -Python stuff: - -Classes should always inherit "object" - -=========================================== - htop: Gives information on processes, cpu/memory load, etc dstat: Also gives various system information, resource usage, etc df: Reports file system disk space usage @@ -78,4 +72,11 @@ ps -ax - View processes kill (process #) +=========================================== + +Python stuff: + +Classes should always inherit "object" +To iterate through dictionary items: for X, Y in MyDictionary.items(): + diff --git a/misc/todo.txt b/misc/todo.txt index 60655a71..1d781b13 100644 --- a/misc/todo.txt +++ b/misc/todo.txt @@ -1,3 +1,2 @@ -- Check about using trait id instead of trait name in queries in data_set.py - +- Ask Rob about potentially recoding qtlreaper - Ask Rob about Probe/cellid traits \ No newline at end of file diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py index ed01a3fa..30860376 100755 --- a/wqflask/wqflask/marker_regression/marker_regression.py +++ b/wqflask/wqflask/marker_regression/marker_regression.py @@ -52,7 +52,36 @@ class MarkerRegression(object): #if not self.openMysql(): # return + print("start_vars are: ", pf(start_vars)) + self.dataset = create_dataset(start_vars['dataset_name']) + self.num_perm = int(start_vars['num_perm']) + + # Passed in by the form (user might have edited) + #samples = start_vars['allsamples'].split() + + self.samples = [] # Want only ones with values + self.vals = [] + self.variances = [] + + self.dataset.group.read_genotype_file() + self.genotype = self.dataset.group.genotype + + for sample in self.dataset.group.samplelist: + value = start_vars['value:' + sample] + variance = start_vars['variance:' + sample] + if variance.strip().lower() == 'x': + variance = 0 + else: + variance = float(variance) + if value.strip().lower() != 'x': + self.samples.append(str(sample)) + self.vals.append(float(value)) + self.variances.append(variance) + + print("self.samples is:", pf(self.samples)) + print("self.vals is:", pf(self.vals)) + print("self.variances is:", pf(self.variances)) #self.initializeParameters(start_vars) @@ -162,111 +191,114 @@ class MarkerRegression(object): #if fd.parentsf14regression and fd.genotype_2: # _genotype = fd.genotype_2 #else: - genotype = self.dataset.group.read_genotype_file() - print("[black]:", genotype) + #print("[black]:", self.genotype) - _strains, _vals, _vars, N = fd.informativeStrains(_genotype.prgy, weightedRegression) + #_strains, _vals, _vars, N = fd.informativeStrains(_genotype.prgy, weightedRegression) - if fd.identification: - heading2 = HT.Paragraph('Trait ID: %s' % fd.identification) - heading2.__setattr__("class","subtitle") - self.dict['title'] = '%s: Genome Association' % fd.identification - else: - heading2 = "" - self.dict['title'] = 'Genome Association' - - if fd.traitInfo: - symbol,chromosome,MB = string.split(fd.traitInfo,'\t') - heading3 = HT.Paragraph('[ ',HT.Strong(HT.Italic('%s' % symbol,id="green")),' on Chr %s @ %s Mb ]' % (chromosome,MB)) - else: - heading3 = "" - - if N < webqtlConfig.KMININFORMATIVE: - heading = "Genome Association" - detail = ['Fewer than %d strain data were entered for %s data set. No mapping attempted.' % (webqtlConfig.KMININFORMATIVE, fd.RISet)] - self.error(heading=heading,detail=detail) - return - else: - heading = HT.Paragraph('Trait Data Entered for %s Set' % fd.RISet) - heading.__setattr__("class","title") - - datadiv = HT.TD(heading, heading2,heading3, width='45%',valign='top', align='left', bgColor='#eeeeee') - resultstable,tblobj,bottomInfo = self.GenReport(ChrNameOrderIdDict,fd, _genotype, _strains, _vals, _vars) - #resultstable = self.GenReport(fd, _genotype, _strains, _vals, _vars) - - # creat object for result table for sort function - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() + #if fd.identification: + # heading2 = HT.Paragraph('Trait ID: %s' % fd.identification) + # heading2.__setattr__("class","subtitle") + # self.dict['title'] = '%s: Genome Association' % fd.identification + #else: + # heading2 = "" + # self.dict['title'] = 'Genome Association' - sortby = ("Index", "up") - reportTable =HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "0"), Id="sortable") + #if fd.traitInfo: + # symbol, chromosome, MB = string.split(fd.traitInfo,'\t') + # heading3 = HT.Paragraph('[ ',HT.Strong(HT.Italic('%s' % symbol,id="green")),' on Chr %s @ %s Mb ]' % (chromosome,MB)) + #else: + # heading3 = "" - descriptionTable = HT.TableLite(border=0, cellpadding=0, cellspacing=0) - descriptionTable.append(HT.TR(HT.TD(reportTable, colspan=3))) - descriptionTable.append(HT.TR(HT.TD(HT.BR(),HT.BR()))) - descriptionTable.append(bottomInfo) + ### Todo in 2013: Don't allow marker regression in show trait page when number of samples + ### with values < 5 - self.traitList=_vals - - ##########################plot####################### - - ################################################################ - # Generate Chr list and Retrieve Length Information - ################################################################ - self.genotype= _genotype - self.ChrList = [("All", -1)] - - for i, indChr in enumerate(self.genotype): - self.ChrList.append((indChr.name, i)) - - self.cursor.execute(""" - Select - Length from Chr_Length, InbredSet - where - Chr_Length.SpeciesId = InbredSet.SpeciesId AND - InbredSet.Name = '%s' AND - Chr_Length.Name in (%s) - Order by - OrderId - """ % (fd.RISet, string.join(map(lambda X: "'%s'" % X[0], self.ChrList[1:]), ", "))) - - self.ChrLengthMbList = self.cursor.fetchall() - self.ChrLengthMbList = map(lambda x: x[0]/1000000.0, self.ChrLengthMbList) - self.ChrLengthMbSum = reduce(lambda x, y:x+y, self.ChrLengthMbList, 0.0) - if self.ChrLengthMbList: - self.MbGraphInterval = self.ChrLengthMbSum/(len(self.ChrLengthMbList)*12) #Empirical Mb interval - else: - self.MbGraphInterval = 1 + #if N < webqtlConfig.KMININFORMATIVE: + # heading = "Genome Association" + # detail = ['Fewer than %d strain data were entered for %s data set. No mapping attempted.' % (webqtlConfig.KMININFORMATIVE, fd.RISet)] + # self.error(heading=heading,detail=detail) + # return + #else: + # heading = HT.Paragraph('Trait Data Entered for %s Set' % fd.RISet) + # heading.__setattr__("class","title") + + #datadiv = HT.TD(heading, heading2,heading3, width='45%',valign='top', align='left', bgColor='#eeeeee') + #resultstable,tblobj,bottomInfo = self.GenReport(ChrNameOrderIdDict,fd, _genotype, _strains, _vals, _vars) + resultstable, tblobj, bottomInfo = self.gen_data() + #resultstable = self.GenReport(fd, _genotype, _strains, _vals, _vars) + + # creat object for result table for sort function + objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') + cPickle.dump(tblobj, objfile) + objfile.close() + + sortby = ("Index", "up") + reportTable =HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "0"), Id="sortable") + + descriptionTable = HT.TableLite(border=0, cellpadding=0, cellspacing=0) + descriptionTable.append(HT.TR(HT.TD(reportTable, colspan=3))) + descriptionTable.append(HT.TR(HT.TD(HT.BR(),HT.BR()))) + descriptionTable.append(bottomInfo) + + self.traitList=_vals + + ##########################plot####################### + + ################################################################ + # Generate Chr list and Retrieve Length Information + ################################################################ + self.genotype= _genotype + self.ChrList = [("All", -1)] + + for i, indChr in enumerate(self.genotype): + self.ChrList.append((indChr.name, i)) + + self.cursor.execute(""" + Select + Length from Chr_Length, InbredSet + where + Chr_Length.SpeciesId = InbredSet.SpeciesId AND + InbredSet.Name = '%s' AND + Chr_Length.Name in (%s) + Order by + OrderId + """ % (fd.RISet, string.join(map(lambda X: "'%s'" % X[0], self.ChrList[1:]), ", "))) + + self.ChrLengthMbList = self.cursor.fetchall() + self.ChrLengthMbList = map(lambda x: x[0]/1000000.0, self.ChrLengthMbList) + self.ChrLengthMbSum = reduce(lambda x, y:x+y, self.ChrLengthMbList, 0.0) + if self.ChrLengthMbList: + self.MbGraphInterval = self.ChrLengthMbSum/(len(self.ChrLengthMbList)*12) #Empirical Mb interval + else: + self.MbGraphInterval = 1 - self.ChrLengthCMList = [] - for i, _chr in enumerate(self.genotype): - self.ChrLengthCMList.append(_chr[-1].cM - _chr[0].cM) - self.ChrLengthCMSum = reduce(lambda x, y:x+y, self.ChrLengthCMList, 0.0)# used for calculate plot scale + self.ChrLengthCMList = [] + for i, _chr in enumerate(self.genotype): + self.ChrLengthCMList.append(_chr[-1].cM - _chr[0].cM) + self.ChrLengthCMSum = reduce(lambda x, y:x+y, self.ChrLengthCMList, 0.0)# used for calculate plot scale - self.GraphInterval = self.MbGraphInterval #Mb + self.GraphInterval = self.MbGraphInterval #Mb - # begin: common part with human data - intCanvas = pid.PILCanvas(size=(self.graphWidth,self.graphHeight)) - gifmap = self.plotIntMapping(fd, intCanvas, startMb = self.startMb, endMb = self.endMb, showLocusForm= "") - filename= webqtlUtil.genRandStr("Itvl_") - intCanvas.save(os.path.join(webqtlConfig.IMGDIR, filename), format='png') - intImg=HT.Image('/image/'+filename+'.png', border=0, usemap='#WebQTLImageMap') + # begin: common part with human data + intCanvas = pid.PILCanvas(size=(self.graphWidth,self.graphHeight)) + gifmap = self.plotIntMapping(fd, intCanvas, startMb = self.startMb, endMb = self.endMb, showLocusForm= "") + filename= webqtlUtil.genRandStr("Itvl_") + intCanvas.save(os.path.join(webqtlConfig.IMGDIR, filename), format='png') + intImg=HT.Image('/image/'+filename+'.png', border=0, usemap='#WebQTLImageMap') - ################################################################ - # footnote goes here - ################################################################ - btminfo = HT.Paragraph(Id="smallsize") #Small('More information about this graph is available here.') + ################################################################ + # footnote goes here + ################################################################ + btminfo = HT.Paragraph(Id="smallsize") #Small('More information about this graph is available here.') - if (self.additiveChecked): - btminfo.append(HT.BR(), 'A positive additive coefficient (', HT.Font('green', color='green'), ' line) indicates that %s alleles increase trait values. In contrast, a negative additive coefficient (' % fd.ppolar, HT.Font('red', color='red'), ' line) indicates that %s alleles increase trait values.' % fd.mpolar) + if (self.additiveChecked): + btminfo.append(HT.BR(), 'A positive additive coefficient (', HT.Font('green', color='green'), ' line) indicates that %s alleles increase trait values. In contrast, a negative additive coefficient (' % fd.ppolar, HT.Font('red', color='red'), ' line) indicates that %s alleles increase trait values.' % fd.mpolar) - TD_LR = HT.TR(HT.TD(HT.Blockquote(gifmap,intImg, HT.P()), bgColor='#eeeeee', height = 200)) + TD_LR = HT.TR(HT.TD(HT.Blockquote(gifmap,intImg, HT.P()), bgColor='#eeeeee', height = 200)) - self.dict['body'] = str(datadiv)+str(TD_LR)+str(resultstable)+str(HT.TR(HT.TD(descriptionTable))) + self.dict['body'] = str(datadiv)+str(TD_LR)+str(resultstable)+str(HT.TR(HT.TD(descriptionTable))) - # end: common part with human data + # end: common part with human data @@ -410,18 +442,37 @@ class MarkerRegression(object): return rv, tblobj,bottomInfo - def GenReport(self, ChrNameOrderIdDict,fd, _genotype, _strains, _vals, _vars= []): - 'Create an HTML division which reports any loci which are significantly associated with the submitted trait data.' + #def GenReport(self, ChrNameOrderIdDict,fd, _genotype, _strains, _vals, _vars= []): + def gen_data(self): + """Todo: Fill this in here""" + #'Create an HTML division which reports any loci which are significantly associated with the submitted trait data.' + #calculate QTL for each trait - self.qtlresults = [] - if webqtlUtil.ListNotNull(_vars): - qtlresults = _genotype.regression(strains = _strains, trait = _vals, variance = _vars) - LRSArray = _genotype.permutation(strains = _strains, trait = _vals, variance = _vars, nperm=fd.nperm) - else: - qtlresults = _genotype.regression(strains = _strains, trait = _vals) - LRSArray = _genotype.permutation(strains = _strains, trait = _vals,nperm=fd.nperm) - - self.qtlresults.append(qtlresults) + #self.qtlresults = [] + #if webqtlUtil.ListNotNull(_vars): + + #strains = + #vals = + #variances = + + #if any(self.variances): + # self.qtl_results = self.genotype.regression(strains = self.samples, + # trait = self.vals, + # variance = self.variances) + # self.lrs_array = self.genotype.permutation(strains = self.samples, + # trait = self.vals, + # variance = self.variances, + # nperm = self.num_perm) + #else: + self.qtl_results = self.genotype.regression(strains = self.samples, + trait = self.vals) + self.lrs_array = self.genotype.permutation(strains = self.samples, + trait = self.vals, + nperm=self.num_perm) + + print("[yellow] self.__dict__ is:", pf(self.__dict__)) + + #self.qtlresults.append(qtlresults) filename= webqtlUtil.genRandStr("GenomeAsscociation_") diff --git a/wqflask/wqflask/templates/show_trait_edit_data.html b/wqflask/wqflask/templates/show_trait_edit_data.html index 9023b34d..84c606b9 100644 --- a/wqflask/wqflask/templates/show_trait_edit_data.html +++ b/wqflask/wqflask/templates/show_trait_edit_data.html @@ -120,7 +120,7 @@ {# Todo: Add IDs #} - - Chromosome
    @@ -30,7 +30,7 @@
    - +
    @@ -125,7 +125,7 @@ No
    - +
    @@ -134,6 +134,13 @@
    +
    + +
    + +
    +
    +