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/notes.txt | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 misc/notes.txt (limited to 'misc/notes.txt') 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 -- 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/notes.txt') 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 @@ + + {% 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 %} + +
-
+ {# @@ -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 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/notes.txt') 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/notes.txt') 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/notes.txt') 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 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", + 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], + [ + "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 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"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 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"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 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], + [ + "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 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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" + ], + [ + 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"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/notes.txt') 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 **" + ], + [ + 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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) 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"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" + ], + [ + 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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) 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"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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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 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"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 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+ "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_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 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" - ], - [ - "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 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- [ - "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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"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 **" - ] - ], - 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"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" - 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"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/notes.txt') 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 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/notes.txt') 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/notes.txt') 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 @@
    +
    + +
    + +
    +
    +