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-rwxr-xr-xwqflask/utility/webqtlUtil.py2
-rw-r--r--[-rwxr-xr-x]wqflask/wqflask/correlation/show_corr_results.py546
-rw-r--r--wqflask/wqflask/static/new/javascript/show_trait.js6
-rwxr-xr-xwqflask/wqflask/templates/show_trait.html2
-rwxr-xr-xwqflask/wqflask/templates/show_trait_details.html23
-rwxr-xr-xwqflask/wqflask/templates/show_trait_mapping_tools.html172
6 files changed, 467 insertions, 284 deletions
diff --git a/wqflask/utility/webqtlUtil.py b/wqflask/utility/webqtlUtil.py
index f842dde0..1108614b 100755
--- a/wqflask/utility/webqtlUtil.py
+++ b/wqflask/utility/webqtlUtil.py
@@ -509,7 +509,7 @@ def calCorrelationRank(xVals,yVals,N):
j = 0
for i in range(len(xVals)):
- if xVals[i]!= None and yVals[i]!= None:
+ if (xVals[i]!= None and yVals[i]!= None) and (xVals[i] != "None" and yVals[i] != "None"):
XX.append((j,xVals[i]))
YY.append((j,yVals[i]))
j = j+1
diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 98596ca4..0795f113 100755..100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -50,6 +50,7 @@ from dbFunction import webqtlDatabaseFunction
import utility.webqtlUtil #this is for parallel computing only.
from wqflask.correlation import correlation_functions
from utility.benchmark import Bench
+import utility.webqtlUtil
from MySQLdb import escape_string as escape
@@ -159,6 +160,9 @@ class CorrelationResults(object):
self.correlation_data = {}
+ db_filename = self.getFileName(target_db_name = self.target_dataset.name)
+ cache_available = db_filename in os.listdir(webqtlConfig.TEXTDIR)
+
if self.corr_type == "tissue":
self.trait_symbol_dict = self.dataset.retrieve_genes("Symbol")
@@ -174,9 +178,25 @@ class CorrelationResults(object):
self.get_sample_r_and_p_values(trait, self.target_dataset.trait_data[trait])
elif self.corr_type == "sample":
- # print("self.target_dataset.trait_data: %d" % len(self.target_dataset.trait_data))
- for trait, values in self.target_dataset.trait_data.iteritems():
- self.get_sample_r_and_p_values(trait, values)
+ if self.dataset.type == "ProbeSet" and cache_available:
+ dataset_file = open(webqtlConfig.TEXTDIR+db_filename,'r')
+
+ #XZ, 01/08/2009: read the first line
+ line = dataset_file.readline()
+ dataset_strains = webqtlUtil.readLineCSV(line)[1:]
+
+ self.this_trait_vals = []
+ for item in dataset_strains:
+ if item in self.sample_data:
+ self.this_trait_vals.append(self.sample_data[item])
+ else:
+ self.this_trait_vals.append("None")
+ num_overlap = len(self.this_trait_vals)
+
+ self.do_parallel_correlation(db_filename, num_overlap)
+ else:
+ for trait, values in self.target_dataset.trait_data.iteritems():
+ self.get_sample_r_and_p_values(trait, values)
self.correlation_data = collections.OrderedDict(sorted(self.correlation_data.items(),
key=lambda t: -abs(t[1][0])))
@@ -308,7 +328,7 @@ class CorrelationResults(object):
#traitList = self.correlate()
- #_log.info("Done doing correlation calculation")
+ #print("Done doing correlation calculation")
############################################################################################################################################
@@ -521,27 +541,125 @@ class CorrelationResults(object):
"""
- # print("len(self.sample_data):", len(self.sample_data))
-
- this_trait_vals = []
+ self.this_trait_vals = []
target_vals = []
for index, sample in enumerate(self.target_dataset.samplelist):
if sample in self.sample_data:
sample_value = self.sample_data[sample]
target_sample_value = target_samples[index]
- this_trait_vals.append(sample_value)
+ self.this_trait_vals.append(sample_value)
target_vals.append(target_sample_value)
- this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(
- this_trait_vals, target_vals)
+ self.this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(self.this_trait_vals, target_vals)
#ZS: 2015 could add biweight correlation, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/
if self.corr_method == 'pearson':
- sample_r, sample_p = scipy.stats.pearsonr(this_trait_vals, target_vals)
+ sample_r, sample_p = scipy.stats.pearsonr(self.this_trait_vals, target_vals)
else:
- sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals)
+ sample_r, sample_p = scipy.stats.spearmanr(self.this_trait_vals, target_vals)
self.correlation_data[trait] = [sample_r, sample_p, num_overlap]
+
+
+ """
+ correlations = []
+
+ #XZ: Use the fast method only for probeset dataset, and this dataset must have been created.
+ #XZ: Otherwise, use original method
+ #print("Entering correlation")
+
+ #db_filename = self.getFileName(target_db_name=self.target_db_name)
+ #
+ #cache_available = db_filename in os.listdir(webqtlConfig.TEXTDIR)
+
+ # If the cache file exists, do a cached correlation for probeset data
+ if self.dataset.type == "ProbeSet":
+# if self.method in [METHOD_SAMPLE_PEARSON, METHOD_SAMPLE_RANK] and cache_available:
+# traits = do_parallel_correlation()
+#
+# else:
+
+ traits = self.get_traits(self.vals)
+
+ for trait in traits:
+ trait.calculate_correlation(vals, self.method)
+
+ self.record_count = len(traits) #ZS: This isn't a good way to get this value, so I need to change it later
+
+ #XZ, 3/31/2010: Theoretically, we should create one function 'comTissueCorr'
+ #to compare each trait by their tissue corr p values.
+ #But because the tissue corr p values are generated by permutation test,
+ #the top ones always have p value 0. So comparing p values actually does nothing.
+ #In addition, for the tissue data in our database, the N is always the same.
+ #So it's safe to compare with tissue corr statistic value.
+ #That's the same as literature corr.
+ #if self.method in [METHOD_LIT, METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK] and self.gene_id:
+ # traits.sort(webqtlUtil.cmpLitCorr)
+ #else:
+ #if self.method in TISSUE_METHODS:
+ # sort(traits, key=lambda A: math.fabs(A.tissue_corr))
+ #elif self.method == METHOD_LIT:
+ # traits.sort(traits, key=lambda A: math.fabs(A.lit_corr))
+ #else:
+ traits = sortTraitCorrelations(traits, self.method)
+
+ # Strip to the top N correlations
+ traits = traits[:min(self.returnNumber, len(traits))]
+
+ addLiteratureCorr = False
+ addTissueCorr = False
+
+ trait_list = []
+ for trait in traits:
+ db_trait = webqtlTrait(db=self.db, name=trait.name, cursor=self.cursor)
+ db_trait.retrieveInfo( QTL='Yes' )
+
+ db_trait.Name = trait.name
+ db_trait.corr = trait.correlation
+ db_trait.nOverlap = trait.overlap
+ db_trait.corrPValue = trait.p_value
+
+ # NL, 07/19/2010
+ # js function changed, add a new parameter rankOrder for js function 'showTissueCorrPlot'
+ db_trait.RANK_ORDER = self.RANK_ORDERS[self.method]
+
+ #XZ, 26/09/2008: Method is 4 or 5. Have fetched tissue corr, but no literature correlation yet.
+ if self.method in TISSUE_METHODS:
+ db_trait.tissueCorr = trait.tissue_corr
+ db_trait.tissuePValue = trait.p_tissue
+ addTissueCorr = True
+
+
+ #XZ, 26/09/2008: Method is 3, Have fetched literature corr, but no tissue corr yet.
+ elif self.method == METHOD_LIT:
+ db_trait.LCorr = trait.lit_corr
+ db_trait.mouse_geneid = self.translateToMouseGeneID(self.species, db_trait.geneid)
+ addLiteratureCorr = True
+
+ #XZ, 26/09/2008: Method is 1 or 2. Have NOT fetched literature corr and tissue corr yet.
+ # Phenotype data will not have geneid, and neither will some probes
+ # we need to handle this because we will get an attribute error
+ else:
+ if self.input_trait_mouse_gene_id and self.db.type=="ProbeSet":
+ addLiteratureCorr = True
+ if self.trait_symbol and self.db.type=="ProbeSet":
+ addTissueCorr = True
+
+ trait_list.append(db_trait)
+
+ if addLiteratureCorr:
+ trait_list = self.getLiteratureCorrelationByList(self.input_trait_mouse_gene_id,
+ self.species, trait_list)
+ if addTissueCorr:
+ trait_list = self.getTissueCorrelationByList(
+ primaryTraitSymbol = self.trait_symbol,
+ traitList = trait_list,
+ TissueProbeSetFreezeId = TISSUE_MOUSE_DB,
+ method=self.method)
+
+ return trait_list
+ """
+
def do_tissue_corr_for_all_traits_2(self):
"""Comments Possibly Out of Date!!!!!
@@ -670,38 +788,6 @@ class CorrelationResults(object):
# except: return False
- def get_all_dataset_data(self):
-
- """
- SELECT ProbeSet.Name, T128.value, T129.value, T130.value, T131.value, T132.value, T134.value, T135.value, T138.value, T139.value, T140.value, T141.value, T142.value, T144
- .value, T145.value, T147.value, T148.value, T149.value, T487.value, T919.value, T920.value, T922.value
- FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze)
- left join ProbeSetData as T128 on T128.Id = ProbeSetXRef.DataId and T128.StrainId=128
- left join ProbeSetData as T129 on T129.Id = ProbeSetXRef.DataId and T129.StrainId=129
- left join ProbeSetData as T130 on T130.Id = ProbeSetXRef.DataId and T130.StrainId=130
- left join ProbeSetData as T131 on T131.Id = ProbeSetXRef.DataId and T131.StrainId=131
- left join ProbeSetData as T132 on T132.Id = ProbeSetXRef.DataId and T132.StrainId=132
- left join ProbeSetData as T134 on T134.Id = ProbeSetXRef.DataId and T134.StrainId=134
- left join ProbeSetData as T135 on T135.Id = ProbeSetXRef.DataId and T135.StrainId=135
- left join ProbeSetData as T138 on T138.Id = ProbeSetXRef.DataId and T138.StrainId=138
- left join ProbeSetData as T139 on T139.Id = ProbeSetXRef.DataId and T139.StrainId=139
- left join ProbeSetData as T140 on T140.Id = ProbeSetXRef.DataId and T140.StrainId=140
- left join ProbeSetData as T141 on T141.Id = ProbeSetXRef.DataId and T141.StrainId=141
- left join ProbeSetData as T142 on T142.Id = ProbeSetXRef.DataId and T142.StrainId=142
- left join ProbeSetData as T144 on T144.Id = ProbeSetXRef.DataId and T144.StrainId=144
- left join ProbeSetData as T145 on T145.Id = ProbeSetXRef.DataId and T145.StrainId=145
- left join ProbeSetData as T147 on T147.Id = ProbeSetXRef.DataId and T147.StrainId=147
- left join ProbeSetData as T148 on T148.Id = ProbeSetXRef.DataId and T148.StrainId=148
- left join ProbeSetData as T149 on T149.Id = ProbeSetXRef.DataId and T149.StrainId=149
- left join ProbeSetData as T487 on T487.Id = ProbeSetXRef.DataId and T487.StrainId=487
- left join ProbeSetData as T919 on T919.Id = ProbeSetXRef.DataId and T919.StrainId=919
- left join ProbeSetData as T920 on T920.Id = ProbeSetXRef.DataId and T920.StrainId=920
- left join ProbeSetData as T922 on T922.Id = ProbeSetXRef.DataId and T922.StrainId=922
- WHERE ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id and
- ProbeSetFreeze.Name = 'HC_M2_0606_P' and
- ProbeSet.Id = ProbeSetXRef.ProbeSetId order by ProbeSet.Id
- """
-
def process_samples(self, start_vars, sample_names, excluded_samples=None):
if not excluded_samples:
excluded_samples = ()
@@ -990,59 +1076,7 @@ class CorrelationResults(object):
totalTraits = len(traits) #XZ, 09/18/2008: total trait number
return traits
-
-
- def do_parallel_correlation(self):
- _log.info("Invoking parallel computing")
- input_line_list = datasetFile.readlines()
- _log.info("Read lines from the file")
- all_line_number = len(input_line_list)
-
- step = 1000
- job_number = math.ceil( float(all_line_number)/step )
-
- job_input_lists = []
-
- _log.info("Configuring jobs")
-
- for job_index in range( int(job_number) ):
- starti = job_index*step
- endi = min((job_index+1)*step, all_line_number)
-
- one_job_input_list = []
-
- for i in range( starti, endi ):
- one_job_input_list.append( input_line_list[i] )
-
- job_input_lists.append( one_job_input_list )
-
- _log.info("Creating pp servers")
-
- ppservers = ()
- # Creates jobserver with automatically detected number of workers
- job_server = pp.Server(ppservers=ppservers)
-
- _log.info("Done creating servers")
-
- jobs = []
- results = []
-
- _log.info("Starting parallel computation, submitting jobs")
- for one_job_input_list in job_input_lists: #pay attention to modules from outside
- jobs.append( job_server.submit(func=compute_corr, args=(nnCorr, _newvals, one_job_input_list, self.method), depfuncs=(), modules=("utility.webqtlUtil",)) )
- _log.info("Done submitting jobs")
-
- for one_job in jobs:
- one_result = one_job()
- results.append( one_result )
-
- _log.info("Acquiring results")
-
- for one_result in results:
- for one_traitinfo in one_result:
- allcorrelations.append( one_traitinfo )
-
- _log.info("Appending the results")
+
def calculate_corr_for_all_tissues(self, tissue_dataset_id=None):
symbol_corr_dict = {}
@@ -1067,10 +1101,7 @@ class CorrelationResults(object):
# SymbolValueDict)
return (symbolCorrDict, symbolPvalueDict)
- datasetFile.close()
- totalTraits = len(allcorrelations)
- _log.info("Done correlating using the fast method")
-
+
def correlate(self):
self.correlation_data = collections.defaultdict(list)
@@ -1085,107 +1116,254 @@ class CorrelationResults(object):
values_2.append(target_value)
correlation = calCorrelation(values_1, values_2)
self.correlation_data[trait] = correlation
+
+ def getFileName(self, target_db_name): ### dcrowell August 2008
+ """Returns the name of the reference database file with which correlations are calculated.
+ Takes argument cursor which is a cursor object of any instance of a subclass of templatePage
+ Used by correlationPage"""
+
+ dataset_id = str(self.target_dataset.id)
+ dataset_fullname = self.target_dataset.fullname.replace(' ','_')
+ dataset_fullname = dataset_fullname.replace('/','_')
+ FileName = 'ProbeSetFreezeId_' + dataset_id + '_FullName_' + dataset_fullname + '.txt'
- """
- correlations = []
-
- #XZ: Use the fast method only for probeset dataset, and this dataset must have been created.
- #XZ: Otherwise, use original method
- #_log.info("Entering correlation")
-
- #db_filename = self.getFileName(target_db_name=self.target_db_name)
- #
- #cache_available = db_filename in os.listdir(webqtlConfig.TEXTDIR)
-
- # If the cache file exists, do a cached correlation for probeset data
- if self.dataset.type == "ProbeSet":
-# if self.method in [METHOD_SAMPLE_PEARSON, METHOD_SAMPLE_RANK] and cache_available:
-# traits = do_parallel_correlation()
-#
-# else:
-
- traits = self.get_traits(self.vals)
+ return FileName
+
+ def do_parallel_correlation(self, db_filename, num_overlap):
+
+ #XZ, 01/14/2009: This method is for parallel computing only.
+ #XZ: It is supposed to be called when "Genetic Correlation, Pearson's r" (method 1)
+ #XZ: or "Genetic Correlation, Spearman's rho" (method 2) is selected
+ def compute_corr(input_nnCorr, input_trait, input_list, corr_method):
+
+ import math
+ import reaper
+
+ def calCorrelation(dbdata,userdata,N):
+ X = []
+ Y = []
+ for i in range(N):
+ if (dbdata[i] != None and userdata[i] != None) and (dbdata[i] != "None" and userdata[i] != "None"):
+ X.append(float(dbdata[i]))
+ Y.append(float(userdata[i]))
+ NN = len(X)
+ if NN <6:
+ return (0.0,NN)
+ sx = reduce(lambda x,y:x+y,X,0.0)
+ sy = reduce(lambda x,y:x+y,Y,0.0)
+ meanx = sx/NN
+ meany = sy/NN
+ xyd = 0.0
+ sxd = 0.0
+ syd = 0.0
+ for i in range(NN):
+ xyd += (X[i] - meanx)*(Y[i]-meany)
+ sxd += (X[i] - meanx)*(X[i] - meanx)
+ syd += (Y[i] - meany)*(Y[i] - meany)
+ try:
+ corr = xyd/(math.sqrt(sxd)*math.sqrt(syd))
+ except:
+ corr = 0
+ return (corr,NN)
+
+ def calCorrelationRank(xVals,yVals,N):
+ """
+ Calculated Spearman Ranked Correlation. The algorithm works
+ by setting all tied ranks to the average of those ranks (for
+ example, if ranks 5-10 all have the same value, each will be set
+ to rank 7.5).
+ """
+
+ XX = []
+ YY = []
+ j = 0
+
+ for i in range(len(xVals)):
+ if (xVals[i]!= None and yVals[i]!= None) and (xVals[i] != "None" and yVals[i] != "None"):
+ XX.append((j,float(xVals[i])))
+ YY.append((j,float(yVals[i])))
+ j = j+1
+
+ NN = len(XX)
+ if NN <6:
+ return (0.0,NN)
+ XX.sort(cmpOrder2)
+ YY.sort(cmpOrder2)
+ X = [0]*NN
+ Y = [0]*NN
+
+ j = 1
+ rank = 0.0
+ t = 0.0
+ sx = 0.0
+
+ while j < NN:
+
+ if XX[j][1] != XX[j-1][1]:
+ X[XX[j-1][0]] = j
+ j = j+1
- for trait in traits:
- trait.calculate_correlation(vals, self.method)
+ else:
+ jt = j+1
+ ji = j
+ for jt in range(j+1, NN):
+ if (XX[jt][1] != XX[j-1][1]):
+ break
+ rank = 0.5*(j+jt)
+ for ji in range(j-1, jt):
+ X[XX[ji][0]] = rank
+ t = jt-j
+ sx = sx + (t*t*t-t)
+ if (jt == NN-1):
+ if (XX[jt][1] == XX[j-1][1]):
+ X[XX[NN-1][0]] = rank
+ j = jt+1
+
+ if j == NN:
+ if X[XX[NN-1][0]] == 0:
+ X[XX[NN-1][0]] = NN
+
+ j = 1
+ rank = 0.0
+ t = 0.0
+ sy = 0.0
+
+ while j < NN:
+
+ if YY[j][1] != YY[j-1][1]:
+ Y[YY[j-1][0]] = j
+ j = j+1
+ else:
+ jt = j+1
+ ji = j
+ for jt in range(j+1, NN):
+ if (YY[jt][1] != YY[j-1][1]):
+ break
+ rank = 0.5*(j+jt)
+ for ji in range(j-1, jt):
+ Y[YY[ji][0]] = rank
+ t = jt - j
+ sy = sy + (t*t*t-t)
+ if (jt == NN-1):
+ if (YY[jt][1] == YY[j-1][1]):
+ Y[YY[NN-1][0]] = rank
+ j = jt+1
+
+ if j == NN:
+ if Y[YY[NN-1][0]] == 0:
+ Y[YY[NN-1][0]] = NN
+
+ D = 0.0
+
+ for i in range(NN):
+ D += (X[i]-Y[i])*(X[i]-Y[i])
+
+ fac = (1.0 -sx/(NN*NN*NN-NN))*(1.0-sy/(NN*NN*NN-NN))
+
+ return ((1-(6.0/(NN*NN*NN-NN))*(D+(sx+sy)/12.0))/math.sqrt(fac),NN)
+
+ # allcorrelations = []
+
+ correlation_data = {}
+ for i, line in enumerate(input_list):
+ if i == 0:
+ continue
+ tokens = line.split('","')
+ tokens[-1] = tokens[-1][:-2] #remove the last "
+ tokens[0] = tokens[0][1:] #remove the first "
+
+ traitdataName = tokens[0]
+ database_trait = tokens[1:]
+
+ #print("database_trait:", database_trait)
+
+ #ZS: 2015 could add biweight correlation, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/
+ # if corr_method == 'pearson':
+ # sample_r, sample_p = scipy.stats.pearsonr(input_trait, database_trait)
+ # else:
+ # sample_r, sample_p = scipy.stats.spearmanr(input_trait, database_trait)
+
+ if corr_method == "pearson": #XZ: Pearson's r
+ sample_r, nOverlap = calCorrelation(input_trait, database_trait, input_nnCorr)
+ else: #XZ: Spearman's rho
+ sample_r, nOverlap = calCorrelationRank(input_trait, database_trait, input_nnCorr)
+
+ #XZ: calculate corrPValue
+ if nOverlap < 3:
+ sample_p = 1.0
+ else:
+ if abs(sample_r) >= 1.0:
+ sample_p = 0.0
+ else:
+ z_value = 0.5*math.log((1.0+sample_r)/(1.0-sample_r))
+ z_value = z_value*math.sqrt(nOverlap-3)
+ sample_p = 2.0*(1.0 - reaper.normp(abs(z_value)))
+
+ correlation_data[traitdataName] = [sample_r, sample_p, nOverlap]
+
+ # traitinfo = [traitdataName, sample_r, nOverlap]
+ # allcorrelations.append(traitinfo)
- self.record_count = len(traits) #ZS: This isn't a good way to get this value, so I need to change it later
+ return correlation_data
+ # return allcorrelations
+
+
+ datasetFile = open(webqtlConfig.TEXTDIR+db_filename,'r')
+
+ print("Invoking parallel computing")
+ input_line_list = datasetFile.readlines()
+ print("Read lines from the file")
+ all_line_number = len(input_line_list)
- #XZ, 3/31/2010: Theoretically, we should create one function 'comTissueCorr'
- #to compare each trait by their tissue corr p values.
- #But because the tissue corr p values are generated by permutation test,
- #the top ones always have p value 0. So comparing p values actually does nothing.
- #In addition, for the tissue data in our database, the N is always the same.
- #So it's safe to compare with tissue corr statistic value.
- #That's the same as literature corr.
- #if self.method in [METHOD_LIT, METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK] and self.gene_id:
- # traits.sort(webqtlUtil.cmpLitCorr)
- #else:
- #if self.method in TISSUE_METHODS:
- # sort(traits, key=lambda A: math.fabs(A.tissue_corr))
- #elif self.method == METHOD_LIT:
- # traits.sort(traits, key=lambda A: math.fabs(A.lit_corr))
- #else:
- traits = sortTraitCorrelations(traits, self.method)
+ step = 1000
+ job_number = math.ceil( float(all_line_number)/step )
- # Strip to the top N correlations
- traits = traits[:min(self.returnNumber, len(traits))]
+ print("JOB NUMBER", job_number)
+
+ job_input_lists = []
- addLiteratureCorr = False
- addTissueCorr = False
+ print("Configuring jobs")
- trait_list = []
- for trait in traits:
- db_trait = webqtlTrait(db=self.db, name=trait.name, cursor=self.cursor)
- db_trait.retrieveInfo( QTL='Yes' )
+ for job_index in range( int(job_number) ):
+ starti = job_index*step
+ endi = min((job_index+1)*step, all_line_number)
- db_trait.Name = trait.name
- db_trait.corr = trait.correlation
- db_trait.nOverlap = trait.overlap
- db_trait.corrPValue = trait.p_value
+ one_job_input_list = []
- # NL, 07/19/2010
- # js function changed, add a new parameter rankOrder for js function 'showTissueCorrPlot'
- db_trait.RANK_ORDER = self.RANK_ORDERS[self.method]
+ for i in range( starti, endi ):
+ one_job_input_list.append( input_line_list[i] )
- #XZ, 26/09/2008: Method is 4 or 5. Have fetched tissue corr, but no literature correlation yet.
- if self.method in TISSUE_METHODS:
- db_trait.tissueCorr = trait.tissue_corr
- db_trait.tissuePValue = trait.p_tissue
- addTissueCorr = True
+ job_input_lists.append( one_job_input_list )
+ print("Creating pp servers")
- #XZ, 26/09/2008: Method is 3, Have fetched literature corr, but no tissue corr yet.
- elif self.method == METHOD_LIT:
- db_trait.LCorr = trait.lit_corr
- db_trait.mouse_geneid = self.translateToMouseGeneID(self.species, db_trait.geneid)
- addLiteratureCorr = True
+ ppservers = ()
+ # Creates jobserver with automatically detected number of workers
+ job_server = pp.Server(ppservers=ppservers)
- #XZ, 26/09/2008: Method is 1 or 2. Have NOT fetched literature corr and tissue corr yet.
- # Phenotype data will not have geneid, and neither will some probes
- # we need to handle this because we will get an attribute error
- else:
- if self.input_trait_mouse_gene_id and self.db.type=="ProbeSet":
- addLiteratureCorr = True
- if self.trait_symbol and self.db.type=="ProbeSet":
- addTissueCorr = True
+ print("Done creating servers")
- trait_list.append(db_trait)
+ jobs = []
+ results = []
- if addLiteratureCorr:
- trait_list = self.getLiteratureCorrelationByList(self.input_trait_mouse_gene_id,
- self.species, trait_list)
- if addTissueCorr:
- trait_list = self.getTissueCorrelationByList(
- primaryTraitSymbol = self.trait_symbol,
- traitList = trait_list,
- TissueProbeSetFreezeId = TISSUE_MOUSE_DB,
- method=self.method)
+ print("Starting parallel computation, submitting jobs")
+ for one_job_input_list in job_input_lists: #pay attention to modules from outside
+ jobs.append( job_server.submit(func=compute_corr, args=(num_overlap, self.this_trait_vals, one_job_input_list, self.corr_method), depfuncs=(), modules=("webqtlUtil",)) )
+ print("Done submitting jobs")
- return trait_list
- """
+ for one_job in jobs:
+ one_result = one_job()
+ self.correlation_data.update(one_result)
+ # one_result = one_job()
+ # results.append( one_result )
+ #print("CORRELATION DATA:", self.correlation_data)
+
+ # print("Acquiring results")
+ # for one_result in results:
+ # for one_traitinfo in one_result:
+ # allcorrelations.append( one_traitinfo )
diff --git a/wqflask/wqflask/static/new/javascript/show_trait.js b/wqflask/wqflask/static/new/javascript/show_trait.js
index 2fa77ae0..5d0fa589 100644
--- a/wqflask/wqflask/static/new/javascript/show_trait.js
+++ b/wqflask/wqflask/static/new/javascript/show_trait.js
@@ -313,6 +313,12 @@
return $("#trait_data_form").submit();
};
+ submit_corr = function(){
+ var url;
+ url = "/corr_compute";
+ return submit_special(url);
+ };
+
$(".corr_compute").on("click", (function(_this) {
return function() {
var url;
diff --git a/wqflask/wqflask/templates/show_trait.html b/wqflask/wqflask/templates/show_trait.html
index 64638fc7..5e2dc6fa 100755
--- a/wqflask/wqflask/templates/show_trait.html
+++ b/wqflask/wqflask/templates/show_trait.html
@@ -29,7 +29,7 @@
<h3>{{ this_trait.description_fmt }}</h3>
</div>
- <form method="post" target="_blank" action="/corr_compute" name="trait_page" id="trait_data_form"
+ <form method="post" action="/corr_compute" target="_blank" name="trait_page" id="trait_data_form"
class="form-horizontal">
<div id="hidden_inputs">
<input type="hidden" name="trait_hmac" value="{{ data_hmac('{}:{}'.format(this_trait.name, dataset.name)) }}">
diff --git a/wqflask/wqflask/templates/show_trait_details.html b/wqflask/wqflask/templates/show_trait_details.html
index 95a3b967..d5fb0cf2 100755
--- a/wqflask/wqflask/templates/show_trait_details.html
+++ b/wqflask/wqflask/templates/show_trait_details.html
@@ -35,8 +35,7 @@
<tr>
<td>Target Score</td>
<td>
- <a href="/blatInfo.html" target="_blank"
- title="Values higher than 2 for the specificity are good">
+ <a href="http://genenetwork.org/blatInfo.html" title="Values higher than 2 for the specificity are good">
BLAT Specificity
</a>:
{{ "%0.3f" | format(this_trait.probe_set_specificity|float) }}
@@ -51,25 +50,25 @@
<td>Resource Links</td>
<td>
{% if this_trait.geneid != None %}
- <a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids={{ this_trait.geneid }}" target="_blank" title="Info from NCBI Entrez Gene">
+ <a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids={{ this_trait.geneid }}" title="Info from NCBI Entrez Gene">
Gene
</a>
&nbsp;&nbsp;
{% endif %}
{% if this_trait.omim != None %}
- <a href="http://www.ncbi.nlm.nih.gov/omim/{{ this_trait.omim }}" target="_blank" title="Summary from On Mendelion Inheritance in Man">
+ <a href="http://www.ncbi.nlm.nih.gov/omim/{{ this_trait.omim }}" title="Summary from On Mendelion Inheritance in Man">
OMIM
</a>
&nbsp;&nbsp;
{% endif %}
{% if this_trait.genbankid != None %}
- <a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Nucleotide&cmd=search&doptcmdl=DocSum&term={{ this_trait.genbankid }}" target="_blank" title="Find the original GenBank sequence used to design the probes">
+ <a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Nucleotide&cmd=search&doptcmdl=DocSum&term={{ this_trait.genbankid }}" title="Find the original GenBank sequence used to design the probes">
GenBank
</a>
&nbsp;&nbsp;
{% endif %}
{% if this_trait.symbol != None %}
- <a href="http://www.genotation.org/Getd2g.pl?gene_list={{ this_trait.symbol }}" target="_blank" title="Related descriptive, genomic, clinical, functional and drug-therapy information">
+ <a href="http://www.genotation.org/Getd2g.pl?gene_list={{ this_trait.symbol }}" title="Related descriptive, genomic, clinical, functional and drug-therapy information">
Genotation
</a>
&nbsp;&nbsp;
@@ -87,40 +86,40 @@
</a>
{% if this_trait.dataset.type == 'ProbeSet' %}
{% if this_trait.symbol != None %}
- <a href="#redirect" onclick="window.open('http://www.genenetwork.org/webqtl/main.py?cmd=sch&amp;gene={{ this_trait.symbol }}&amp;alias=1&amp;species={{ species_name }}')">
+ <a href="http://www.genenetwork.org/webqtl/main.py?cmd=sch&amp;gene={{ this_trait.symbol }}&amp;alias=1&amp;species={{ species_name }}">
<button type="button" class="btn btn-default" title="Find similar expression data">
<i class="icon-search"></i> Find
</button>
</a>
{% endif %}
{% if UCSC_BLAT_URL != "" %}
- <a href="#redirect" onclick="window.open('{{ UCSC_BLAT_URL }}')">
+ <a href="{{ UCSC_BLAT_URL }}">
<button type="button" class="btn btn-default" title="Check probe locations at UCSC">
<i class="icon-ok"></i> Verify
</button>
</a>
{% endif %}
{% if this_trait.symbol != None %}
- <a href="#redirect" onclick="window.open('http://genenetwork.org/webqtl/main.py?FormID=geneWiki&symbol={{ this_trait.symbol }}')">
+ <a href="http://genenetwork.org/webqtl/main.py?FormID=geneWiki&symbol={{ this_trait.symbol }}">
<button type="button" class="btn btn-default" title="Write or review comments about this gene">
<i class="icon-edit"></i> GeneWiki
</button>
</a>
- <a href="#redirect" onclick="window.open('http://genenetwork.org/webqtl/main.py?FormID=SnpBrowserResultPage&submitStatus=1&diffAlleles=True&customStrain=True&geneName={{ this_trait.symbol }}')">
+ <a href="http://genenetwork.org/webqtl/main.py?FormID=SnpBrowserResultPage&submitStatus=1&diffAlleles=True&customStrain=True&geneName={{ this_trait.symbol }}">
<button type="button" class="btn btn-default" title="View SNPs and Indels">
<i class="icon-road"></i> SNPs
</button>
</a>
{% endif %}
{% if UTHSC_BLAT_URL != "" %}
- <a href="#redirect" onclick="window.open('{{ UTHSC_BLAT_URL }}')">
+ <a href="{{ UTHSC_BLAT_URL }}">
<button type="button" class="btn btn-default" title="View probes, SNPs, and RNA-seq at UTHSC">
<i class="icon-eye-close"></i> RNA-seq
</button>
</a>
{% endif %}
{% if show_probes == "True" %}
- <a href="#redirect" onclick="window.open('http://genenetwork.org/webqtl/main.py?FormID=showProbeInfo&database={{ this_trait.dataset.name }}&ProbeSetID={{ this_trait.name }}&CellID={{ this_trait.cellid }}&RISet={{ dataset.group.name }}&incparentsf1=ON')">
+ <a href="http://genenetwork.org/webqtl/main.py?FormID=showProbeInfo&database={{ this_trait.dataset.name }}&ProbeSetID={{ this_trait.name }}&CellID={{ this_trait.cellid }}&RISet={{ dataset.group.name }}&incparentsf1=ON">
<button type="button" class="btn btn-default" title="Check sequence of probes">
<i class="icon-list"></i> Probes
</button>
diff --git a/wqflask/wqflask/templates/show_trait_mapping_tools.html b/wqflask/wqflask/templates/show_trait_mapping_tools.html
index 067dfc67..3d9c2521 100755
--- a/wqflask/wqflask/templates/show_trait_mapping_tools.html
+++ b/wqflask/wqflask/templates/show_trait_mapping_tools.html
@@ -6,13 +6,13 @@
<ul class="nav nav-pills">
{% if use_pylmm_rqtl and not use_plink_gemma and dataset.group.species != "human" %}
<li class="active">
- <a href="#pylmm" data-toggle="tab">pyLMM</a>
+ <a href="#interval_mapping" data-toggle="tab">Interval Mapping</a>
</li>
<li>
- <a href="#rqtl_geno" data-toggle="tab">R/qtl</a>
+ <a href="#pylmm" data-toggle="tab">pyLMM</a>
</li>
<li>
- <a href="#interval_mapping" data-toggle="tab">Interval Mapping</a>
+ <a href="#rqtl_geno" data-toggle="tab">R/qtl</a>
</li>
{% endif %}
{% if use_plink_gemma %}
@@ -30,7 +30,89 @@
<div class="tab-content">
{% if use_pylmm_rqtl and not use_plink_gemma and dataset.group.species != "human" %}
- <div class="tab-pane active" id="pylmm">
+ <div class="tab-pane active" id="interval_mapping">
+ <div style="margin-top: 20px" class="form-horizontal">
+ <div class="mapping_method_fields form-group">
+ <label for="mapping_permutations" class="col-xs-3 control-label">Permutations</label>
+ <div style="margin-left: 20px;" class="col-xs-4 controls">
+ <input name="num_perm_reaper" value="2000" type="text" class="form-control">
+ </div>
+ </div>
+ <div class="mapping_method_fields form-group">
+ <label for="mapping_bootstraps" class="col-xs-3 control-label">Bootstraps</label>
+ <div style="margin-left: 20px;" class="col-xs-4 controls">
+ <input name="num_bootstrap" value="2000" type="text" class="form-control">
+ </div>
+ </div>
+ <div class="mapping_method_fields form-group">
+ <label for="control_for" class="col-xs-3 control-label">Control&nbsp;for</label>
+ <div style="margin-left: 20px;" class="col-xs-4 controls">
+ {% if dataset.type == 'ProbeSet' and this_trait.locus_chr != "" %}
+ <input name="control_reaper" value="{{ nearest_marker }}" type="text" style="width: 160px;" class="form-control" />
+ {% else %}
+ <input name="control_reaper" value="" type="text" class="form-control" />
+ {% endif %}
+ <label class="radio-inline">
+ <input type="radio" name="do_control_reaper" value="true">
+ Yes
+ </label>
+ <label class="radio-inline">
+ <input type="radio" name="do_control_reaper" value="false" checked="">
+ No
+ </label>
+ </div>
+ </div>
+
+<!--
+ <div class="mapping_method_fields form-group">
+ <label for="mapping_bootstraps" class="col-xs-3 control-label" title="Bootstrapping Resamples">Bootstrap Test (n=2000)</label>
+ <div class="col-xs-4 controls">
+ <label>
+ <input type="checkbox" name="bootCheck" id="bootCheck"> Bootstrap Test (n=2000)
+ </label>
+ </div>
+ </div>
+
+ <div class="mapping_method_fields form-group">
+ <label style="text-align:left;" class="col-xs-12 control-label">Display Additive Effect</label>
+ <div class="col-xs-12 controls" id="display_additive_effect">
+ <label class="radio-inline">
+ <input type="radio" name="display_additive" id="display_additive" value="yes" checked="">
+ Yes
+ </label>
+ <label class="radio-inline">
+ <input type="radio" name="display_additive" id="display_additive" value="no">
+ No
+ </label>
+ </div>
+ </div>
+-->
+
+
+ <div class="mapping_method_fields form-group">
+ <label style="text-align:left;" class="col-xs-12 control-label">Marker Regr.</label>
+ <div class="col-xs-12 controls">
+ <label class="radio-inline">
+ <input type="radio" name="manhattan_plot_reaper" value="True">
+ Yes
+ </label>
+ <label class="radio-inline">
+ <input type="radio" name="manhattan_plot_reaper" value="False" checked="">
+ No
+ </label>
+ </div>
+ </div>
+ <div class="form-group">
+ <div style="padding-left:15px;" class="controls">
+ <button id="interval_mapping_compute" class="btn submit_special btn-primary" data-url="/marker_regression" title="Compute Interval Mapping">
+ <i class="icon-ok-circle icon-white"></i> Compute
+ </button>
+ </div>
+ </div>
+ <!--<div id="alert_placeholder"></div>-->
+ </div>
+ </div>
+ <div class="tab-pane" id="pylmm">
<div style="margin-top: 20px" class="form-horizontal">
<div class="mapping_method_fields form-group">
<label for="mapping_permutations" class="col-xs-3 control-label">Permutations</label>
@@ -179,88 +261,6 @@
</div>
</div>
</div>
- <div class="tab-pane" id="interval_mapping">
- <div style="margin-top: 20px" class="form-horizontal">
- <div class="mapping_method_fields form-group">
- <label for="mapping_permutations" class="col-xs-3 control-label">Permutations</label>
- <div style="margin-left: 20px;" class="col-xs-4 controls">
- <input name="num_perm_reaper" value="2000" type="text" class="form-control">
- </div>
- </div>
- <div class="mapping_method_fields form-group">
- <label for="mapping_bootstraps" class="col-xs-3 control-label">Bootstraps</label>
- <div style="margin-left: 20px;" class="col-xs-4 controls">
- <input name="num_bootstrap" value="2000" type="text" class="form-control">
- </div>
- </div>
- <div class="mapping_method_fields form-group">
- <label for="control_for" class="col-xs-3 control-label">Control&nbsp;for</label>
- <div style="margin-left: 20px;" class="col-xs-4 controls">
- {% if dataset.type == 'ProbeSet' and this_trait.locus_chr != "" %}
- <input name="control_reaper" value="{{ nearest_marker }}" type="text" style="width: 160px;" class="form-control" />
- {% else %}
- <input name="control_reaper" value="" type="text" class="form-control" />
- {% endif %}
- <label class="radio-inline">
- <input type="radio" name="do_control_reaper" value="true">
- Yes
- </label>
- <label class="radio-inline">
- <input type="radio" name="do_control_reaper" value="false" checked="">
- No
- </label>
- </div>
- </div>
-
-<!--
- <div class="mapping_method_fields form-group">
- <label for="mapping_bootstraps" class="col-xs-3 control-label" title="Bootstrapping Resamples">Bootstrap Test (n=2000)</label>
- <div class="col-xs-4 controls">
- <label>
- <input type="checkbox" name="bootCheck" id="bootCheck"> Bootstrap Test (n=2000)
- </label>
- </div>
- </div>
-
- <div class="mapping_method_fields form-group">
- <label style="text-align:left;" class="col-xs-12 control-label">Display Additive Effect</label>
- <div class="col-xs-12 controls" id="display_additive_effect">
- <label class="radio-inline">
- <input type="radio" name="display_additive" id="display_additive" value="yes" checked="">
- Yes
- </label>
- <label class="radio-inline">
- <input type="radio" name="display_additive" id="display_additive" value="no">
- No
- </label>
- </div>
- </div>
--->
-
-
- <div class="mapping_method_fields form-group">
- <label style="text-align:left;" class="col-xs-12 control-label">Marker Regr.</label>
- <div class="col-xs-12 controls">
- <label class="radio-inline">
- <input type="radio" name="manhattan_plot_reaper" value="True">
- Yes
- </label>
- <label class="radio-inline">
- <input type="radio" name="manhattan_plot_reaper" value="False" checked="">
- No
- </label>
- </div>
- </div>
- <div class="form-group">
- <div style="padding-left:15px;" class="controls">
- <button id="interval_mapping_compute" class="btn submit_special btn-primary" data-url="/marker_regression" title="Compute Interval Mapping">
- <i class="icon-ok-circle icon-white"></i> Compute
- </button>
- </div>
- </div>
- <!--<div id="alert_placeholder"></div>-->
- </div>
- </div>
{% endif %}
{% if use_plink_gemma %}
<div class="tab-pane" id="plink">