diff options
| -rwxr-xr-x | wqflask/base/data_set.py | 14 | ||||
| -rw-r--r-- | wqflask/maintenance/quick_search_table.py | 6 | ||||
| -rw-r--r-- | wqflask/wqflask/correlation/correlation_functions.py | 2 | ||||
| -rw-r--r-- | wqflask/wqflask/correlation/show_corr_results.py | 545 | ||||
| -rw-r--r-- | wqflask/wqflask/templates/show_trait_calculate_correlations.html | 2 |
5 files changed, 309 insertions, 260 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py index 96e04df0..5d21c901 100755 --- a/wqflask/base/data_set.py +++ b/wqflask/base/data_set.py @@ -1055,7 +1055,6 @@ class MrnaAssayDataSet(DataSet): """ % (escape(self.name), escape(self.dataset.name)) results = g.db.execute(query).fetchone() return results[0] - def retrieve_sample_data(self, trait): query = """ @@ -1077,6 +1076,19 @@ class MrnaAssayDataSet(DataSet): results = g.db.execute(query).fetchall() return results + def retrieve_gene_symbols(self): + query = """ + select ProbeSet.Name, ProbeSet.Symbol + from ProbeSet,ProbeSetXRef + where ProbeSetXRef.ProbeSetFreezeId = %s and + ProbeSetXRef.ProbeSetId=ProbeSet.Id; + """ % (self.id) + results = g.db.execute(query).fetchall() + symbol_dict = {} + for item in results: + symbol_dict[item[0]] = item[1] + return symbol_dict + class TempDataSet(DataSet): '''Temporary user-generated data set''' diff --git a/wqflask/maintenance/quick_search_table.py b/wqflask/maintenance/quick_search_table.py index 9cd792ef..eef61857 100644 --- a/wqflask/maintenance/quick_search_table.py +++ b/wqflask/maintenance/quick_search_table.py @@ -11,10 +11,10 @@ each trait, its dataset, and several columns determined by its trait type (pheno """ -from __future__ import print_function, division, absolute_import +from __future__ import absolute_import, division, print_function -import sys -sys.path.append("../../..") +#import sys +#sys.path.append("../../..") import simplejson as json diff --git a/wqflask/wqflask/correlation/correlation_functions.py b/wqflask/wqflask/correlation/correlation_functions.py index 84d47bb5..da5c3197 100644 --- a/wqflask/wqflask/correlation/correlation_functions.py +++ b/wqflask/wqflask/correlation/correlation_functions.py @@ -805,8 +805,6 @@ def get_symbol_value_pairs(tissue_data): ######################################################################################################## def get_trait_symbol_and_tissue_values(symbol_list=None): - SymbolValuePairDict={} - tissue_data = MrnaAssayTissueData(gene_symbols=symbol_list) if len(tissue_data.gene_symbols): diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py index b9d009af..c6bc5b2a 100644 --- a/wqflask/wqflask/correlation/show_corr_results.py +++ b/wqflask/wqflask/correlation/show_corr_results.py @@ -95,8 +95,7 @@ class CorrelationResults(object): #self.this_trait = GeneralTrait(dataset=self.dataset.name, # name=start_vars['trait_id'], - # cellid=None) - #print("start_vars: ", pf(start_vars)) + # cellid=None) with Bench("Doing correlations"): helper_functions.get_species_dataset_trait(self, start_vars) self.dataset.group.read_genotype_file() @@ -104,6 +103,7 @@ class CorrelationResults(object): corr_samples_group = start_vars['corr_samples_group'] self.sample_data = {} + self.corr_type = start_vars['corr_type'] self.corr_method = start_vars['corr_sample_method'] self.return_number = 50 @@ -127,36 +127,61 @@ class CorrelationResults(object): self.target_dataset = data_set.create_dataset(start_vars['corr_dataset']) self.target_dataset.get_trait_data() + self.correlation_results = [] self.correlation_data = {} - for trait, values in self.target_dataset.trait_data.iteritems(): - 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 = values[index] - 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) - - if self.corr_method == 'pearson': - sample_r, sample_p = scipy.stats.pearsonr(this_trait_vals, target_vals) - else: - sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals) - - self.correlation_data[trait] = [sample_r, sample_p, num_overlap] - - self.correlation_data = collections.OrderedDict(sorted(self.correlation_data.items(), - key=lambda t: -abs(t[1][0]))) - self.correlation_results = [] - - #self.correlation_data_slice = collections.OrderedDict() + if self.corr_type == "tissue": + trait_symbol_dict = self.dataset.retrieve_gene_symbols() + trait_symbols = trait_symbol_dict.values + + tissue_corr_data = self.do_tissue_corr_for_all_traits(gene_symbol_list=trait_symbols) + + for trait in tissue_corr_data.keys()[:self.return_number]: + 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 = self.target_dataset.trait_data[trait][index] + 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) + + if self.corr_method == 'pearson': + sample_r, sample_p = scipy.stats.pearsonr(this_trait_vals, target_vals) + else: + sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals) + + self.correlation_data[trait] = [sample_r, sample_p, num_overlap] + + elif self.corr_type == "sample": + for trait, values in self.target_dataset.trait_data.iteritems(): + 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 = values[index] + 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) + + if self.corr_method == 'pearson': + sample_r, sample_p = scipy.stats.pearsonr(this_trait_vals, target_vals) + else: + sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals) + + self.correlation_data[trait] = [sample_r, sample_p, num_overlap] + + self.correlation_data = collections.OrderedDict(sorted(self.correlation_data.items(), + key=lambda t: -abs(t[1][0]))) - for trait_counter, trait in enumerate(self.correlation_data.keys()[:self.return_number]): + for _trait_counter, trait in enumerate(self.correlation_data.keys()[:self.return_number]): trait_object = GeneralTrait(dataset=self.dataset, name=trait, get_qtl_info=True) print("gene symbol: ", trait_object.symbol) @@ -168,63 +193,21 @@ class CorrelationResults(object): #Get symbol for trait and call function that gets each tissue value from the database (tables TissueProbeSetXRef, #TissueProbeSetData, etc) and calculates the correlation (cal_zero_order_corr_for_tissue in correlation_functions) - - - # Set some sane defaults - trait_object.tissue_corr = 0 - trait_object.tissue_pvalue = 0 - + if self.corr_method != "tissue": + # Set some sane defaults + trait_object.tissue_corr = 0 + trait_object.tissue_pvalue = 0 + else: + trait_object.tissue_corr = tissue_corr_data[trait][1] + trait_object.tissue_pvalue = tissue_corr_data[trait][2] + self.correlation_results.append(trait_object) - self.do_tissue_correlation_by_list() + if self.corr_method != "tissue": + self.do_tissue_correlation_for_trait_list() print("self.correlation_results: ", pf(self.correlation_results)) - - - #self.correlation_data_slice[trait] = self.correlation_data[trait] - #self.correlation_data_slice[trait].append(trait_object) - #if self.dataset.type == 'ProbeSet': - # trait_info = collections.OrderedDict( - # correlation = float(self.correlation_data[trait][0]), - # p_value = float(self.correlation_data[trait][1]), - # symbol = trait_object.symbol, - # alias = trait_object.alias, - # description = trait_object.description, - # chromosome = trait_object.chr, - # mb = trait_object.mb - # ) - # if trait_object.mean: - #def do_tissue_correlation_by_list(self, tissue_dataset_id):t_object.alias, # trait_info[mean] = trait_object.mean - # if hasattr(trait_object, 'mean'): - # trait_info[mean] = trait_object.mean - # if hasattr(trait_object, 'lrs'): - # trait_info[lrs] = trait_object.lrs - # if hasattr(trait_object, 'locus_chr'): - # trait_info[locus_chr] = trait_object.locus_chr - # if hasattr(trait_object, 'locus_mb'): - # trait_info[locus_mb] = trait_object.locus_mb - #elif self.dataset.type == 'Geno': - # trait_info = collections.OrderedDict( - # correlation = float(self.correlation_data[trait][0]), - # p_value = float(self.correlation_data[trait][1]), - # symbol = trait_object.symbol, - # alias = trai - #def do_tissue_correlation_by_list(self, tissue_dataset_id):t_object.alias, - # description = trait_object.description, - # chromosome = trait_object.chr, - # mb = trait_object.mb - # ) - #else: # 'Publish' - # trait_info = collections.OrderedDict( - # correlation = float(self.correlation_data[trait][0]), - # p_value = float(self.correlation_data[trait][1]), - # symbol = trait_object.symbol, - # alias = trait_object.alias, - # description = trait_object.description, - # chromosome = trait_object.chr, - # mb = trait_object.mb - # ) #XZ, 09/18/2008: get all information about the user selected database. #target_db_name = fd.corr_dataset @@ -278,6 +261,210 @@ class CorrelationResults(object): ############################################################################################################################################ + def do_tissue_correlation_for_trait_list(self, tissue_dataset_id=1): + """Given a list of correlation results (self.correlation_results), gets the tissue correlation value for each""" + + #Gets tissue expression values for the primary trait + primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( + symbol_list = [self.this_trait.symbol]) + + print("primary_trait_tissue_vals: ", pf(primary_trait_tissue_vals_dict)) + + if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict: + primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower()] + + #gene_symbol_list = [] + # + #for trait in self.correlation_results: + # if hasattr(trait, 'symbol'): + # gene_symbol_list.append(trait.symbol) + + gene_symbol_list = [trait.symbol for trait in self.correlation_results if trait.symbol] + + corr_result_tissue_vals_dict= correlation_functions.get_trait_symbol_and_tissue_values( + symbol_list=gene_symbol_list) + + print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict)) + + for trait in self.correlation_results: + if trait.symbol and trait.symbol.lower() in corr_result_tissue_vals_dict: + this_trait_tissue_values = corr_result_tissue_vals_dict[trait.symbol.lower()] + + result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values, + this_trait_tissue_values, + self.corr_method) + + trait.tissue_corr = result[0] + trait.tissue_pvalue = result[2] + + # else: + # trait.tissue_corr = None + # trait.tissue_pvalue = None + #else: + # for trait in self.correlation_results: + # trait.tissue_corr = None + # trait.tissue_pvalue = None + + #return self.correlation_results + + + def do_tissue_corr_for_all_traits(self, trait_symbols, tissue_dataset_id=1): + #Gets tissue expression values for the primary trait + primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( + symbol_list = [self.this_trait.symbol]) + + correlation_data = {} + if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict: + primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower()] + + corr_result_tissue_vals_dict= correlation_functions.get_trait_symbol_and_tissue_values( + symbol_list=trait_symbols.values) + + print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict)) + + for trait, symbol in trait_symbols.iteritems(): + if symbol.lower() in corr_result_tissue_vals_dict: + this_trait_tissue_values = corr_result_tissue_vals_dict[symbol.lower()] + + result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values, + this_trait_tissue_values, + self.corr_method) + + correlation_results[trait] = [symbol, result[0], result[2]] + + correlation_data = collections.OrderedDict(sorted(self.correlation_data.items(), + key=lambda t: -abs(t[1][1]))) + + return correlation_data + + + + def do_tissue_corr_for_all_traits_2(self): + """Comments Possibly Out of Date!!!!! + + Uses get_temp_tissue_corr_table to generate table of tissue correlations + + This function then gathers that data and pairs it with the TraitID string. + Takes as its arguments a formdata instance, and a dataset instance. + Returns a dictionary of 'TraitID':(tissueCorr, tissuePValue) + for the requested correlation + + Used when the user selects the tissue correlation method; i.e. not for the + column that is appended to all probeset trait correlation tables + + """ + + # table name string + temp_table = self.get_temp_tissue_corr_table(tissue_probesetfreeze_id=TISSUE_MOUSE_DB, + method=method) + + query = """SELECT ProbeSet.Name, {}.Correlation, {}.PValue + FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze) + LEFT JOIN {} ON {}.Symbol=ProbeSet.Symbol + WHERE ProbeSetFreeze.Name = '{}' + and ProbeSetFreeze.Id=ProbeSetXRef.ProbeSetFreezeId + and ProbeSet.Id = ProbeSetXRef.ProbeSetId + and ProbeSet.Symbol IS NOT NULL + and {}.Correlation IS NOT NULL""".format(dataset.mescape( + temp_table, temp_table, temp_table, temp_table, + self.dataset.name, temp_table)) + + results = g.db.execute(query).fetchall() + + tissue_corr_dict = {} + + for entry in results: + trait_name, tissue_corr, tissue_pvalue = entry + tissue_corr_dict[trait_name] = (tissue_corr, tissue_pvalue) + #symbolList, + #geneIdDict, + #dataIdDict, + #ChrDict, + #MbDict, + #descDict, + #pTargetDescDict = getTissueProbeSetXRefInfo( + # GeneNameLst=GeneNameLst,TissueProbeSetFreezeId=TissueProbeSetFreezeId) + + g.db.execute('DROP TEMPORARY TABLE {}'.format(escape(temp_table))) + + return tissue_corr_dict + + + #XZ, 09/23/2008: In tissue correlation tables, there is no record of GeneId1 == GeneId2 + #XZ, 09/24/2008: Note that the correlation value can be negative. + def get_temp_tissue_corr_table(self, + tissue_probesetfreeze_id=0, + method="", + return_number=0): + + + def cmp_tisscorr_absolute_value(A, B): + try: + if abs(A[1]) < abs(B[1]): return 1 + elif abs(A[1]) == abs(B[1]): + return 0 + else: return -1 + except: + return 0 + + symbol_corr_dict, symbol_pvalue_dict = self.calculate_corr_for_all_tissues( + tissue_dataset_id=TISSUE_MOUSE_DB) + + symbol_corr_list = symbol_corr_dict.items() + + symbol_corr_list.sort(cmp_tisscorr_absolute_value) + symbol_corr_list = symbol_corr_list[0 : 2*return_number] + + tmp_table_name = webqtlUtil.genRandStr(prefix="TOPTISSUE") + + q1 = 'CREATE TEMPORARY TABLE %s (Symbol varchar(100) PRIMARY KEY, Correlation float, PValue float)' % tmp_table_name + self.cursor.execute(q1) + + for one_pair in symbol_corr_list: + one_symbol = one_pair[0] + one_corr = one_pair[1] + one_p_value = symbol_pvalue_dict[one_symbol] + + self.cursor.execute( "INSERT INTO %s (Symbol, Correlation, PValue) VALUES ('%s',%f,%f)" % (tmpTableName, one_symbol, float(one_corr), float(one_p_value)) ) + + return tmp_table_name + + + def calculate_corr_for_all_tissues(self, tissue_dataset_id=None): + + symbol_corr_dict = {} + symbol_pvalue_dict = {} + + primary_trait_symbol_value_dict = correlation_functions.make_gene_tissue_value_dict( + GeneNameLst=[self.this_trait.symbol], + TissueProbeSetFreezeId=tissue_dataset_id) + primary_trait_value = primary_trait_symbol_value_dict.values()[0] + + symbol_value_dict = correlation_functions.make_gene_tissue_value_dict( + gene_name_list=[], + tissue_dataset_id=tissue_dataset_id) + + symbol_corr_dict, symbol_pvalue_dict = correlation_functions.batch_cal_tissue_corr( + primaryTraitValue, + SymbolValueDict, + method=self.corr_method) + #else: + # symbol_corr_dict, symbol_pvalue_dict = correlation_functions.batch_cal_tissue_corr( + # primaryTraitValue, + # SymbolValueDict) + + return (symbolCorrDict, symbolPvalueDict) + + ##XZ, 12/16/2008: the input geneid is of mouse type + #def checkSymbolForTissueCorr(self, tissueProbeSetFreezeId=0, symbol=""): + # q = "SELECT 1 FROM TissueProbeSetXRef WHERE TissueProbeSetFreezeId=%s and Symbol='%s' LIMIT 1" % (tissueProbeSetFreezeId,symbol) + # self.cursor.execute(q) + # try: + # x = self.cursor.fetchone() + # if x: return True + # else: raise + # except: return False + def get_all_dataset_data(self): @@ -353,6 +540,8 @@ class CorrelationResults(object): return mouse_geneid + + ##XZ, 12/16/2008: the input geneid is of mouse type #def checkForLitInfo(self,geneId): # q = 'SELECT 1 FROM LCorrRamin3 WHERE GeneId1=%s LIMIT 1' % geneId @@ -364,16 +553,6 @@ class CorrelationResults(object): # except: return False - ##XZ, 12/16/2008: the input geneid is of mouse type - #def checkSymbolForTissueCorr(self, tissueProbeSetFreezeId=0, symbol=""): - # q = "SELECT 1 FROM TissueProbeSetXRef WHERE TissueProbeSetFreezeId=%s and Symbol='%s' LIMIT 1" % (tissueProbeSetFreezeId,symbol) - # self.cursor.execute(q) - # try: - # x = self.cursor.fetchone() - # if x: return True - # else: raise - # except: return False - def fetchAllDatabaseData(self, species, GeneId, GeneSymbol, strains, db, method, returnNumber, tissueProbeSetFreezeId): @@ -545,46 +724,6 @@ class CorrelationResults(object): - #XZ, 09/23/2008: In tissue correlation tables, there is no record of GeneId1 == GeneId2 - #XZ, 09/24/2008: Note that the correlation value can be negative. - def get_temp_tissue_corr_table(self, - tissue_probesetfreeze_id=0, - method="", - return_number=0): - - - def cmp_tisscorr_absolute_value(A, B): - try: - if abs(A[1]) < abs(B[1]): return 1 - elif abs(A[1]) == abs(B[1]): - return 0 - else: return -1 - except: - return 0 - - symbol_corr_dict, symbol_pvalue_dict = self.calculate_corr_for_all_tissues( - tissue_dataset_id=TISSUE_MOUSE_DB) - - symbol_corr_list = symbol_corr_dict.items() - - symbol_corr_list.sort(cmp_tisscorr_absolute_value) - symbol_corr_list = symbol_corr_list[0 : 2*return_number] - - tmp_table_name = webqtlUtil.genRandStr(prefix="TOPTISSUE") - - q1 = 'CREATE TEMPORARY TABLE %s (Symbol varchar(100) PRIMARY KEY, Correlation float, PValue float)' % tmp_table_name - self.cursor.execute(q1) - - for one_pair in symbol_corr_list: - one_symbol = one_pair[0] - one_corr = one_pair[1] - one_p_value = symbol_pvalue_dict[one_symbol] - - self.cursor.execute( "INSERT INTO %s (Symbol, Correlation, PValue) VALUES ('%s',%f,%f)" % (tmpTableName, one_symbol, float(one_corr), float(one_p_value)) ) - - return tmp_table_name - - #XZ, 01/09/2009: This function was created by David Crowell. Xiaodong cleaned up and modified it. def fetchLitCorrelations(self, species, GeneId, db, returnNumber): ### Used to generate Lit Correlations when calculations are done from text file. dcrowell August 2008 """Uses getTempLiteratureTable to generate table of literatire correlations. This function then gathers that data and @@ -612,57 +751,6 @@ class CorrelationResults(object): return litCorrDict - def fetch_tissue_correlations(self): - """Comments Possibly Out of Date!!!!! - - - Uses getTempTissueCorrTable to generate table of tissue correlations - - This function then gathers that data and pairs it with the TraitID string. - Takes as its arguments a formdata instance, and a database instance. - Returns a dictionary of 'TraitID':(tissueCorr, tissuePValue) - for the requested correlation - - Used when the user selects the tissue correlation method; i.e. not for the - column that is appended to all probeset trait correlation tables - - """ - - # table name string - temp_table = self.get_temp_tissue_corr_table(tissue_probesetfreeze_id=TISSUE_MOUSE_DB, - method=method) - - query = """SELECT ProbeSet.Name, {}.Correlation, {}.PValue - FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze) - LEFT JOIN {} ON {}.Symbol=ProbeSet.Symbol - WHERE ProbeSetFreeze.Name = '{}' - and ProbeSetFreeze.Id=ProbeSetXRef.ProbeSetFreezeId - and ProbeSet.Id = ProbeSetXRef.ProbeSetId - and ProbeSet.Symbol IS NOT NULL - and {}.Correlation IS NOT NULL""".format(dataset.mescape( - temp_table, temp_table, temp_table, temp_table, - self.dataset.name, temp_table)) - - results = g.db.execute(query).fetchall() - - tissue_corr_dict = {} - - for entry in results: - trait_name, tissue_corr, tissue_pvalue = entry - tissue_corr_dict[trait_name] = (tissue_corr, tissue_pvalue) - #symbolList, - #geneIdDict, - #dataIdDict, - #ChrDict, - #MbDict, - #descDict, - #pTargetDescDict = getTissueProbeSetXRefInfo( - # GeneNameLst=GeneNameLst,TissueProbeSetFreezeId=TissueProbeSetFreezeId) - - g.db.execute('DROP TEMPORARY TABLE {}'.format(escape(temp_table))) - - return tissue_corr_dict - def getLiteratureCorrelationByList(self, input_trait_mouse_geneid=None, species=None, traitList=None): @@ -819,7 +907,30 @@ class CorrelationResults(object): allcorrelations.append( one_traitinfo ) _log.info("Appending the results") + def calculate_corr_for_all_tissues(self, tissue_dataset_id=None): + + symbol_corr_dict = {} + symbol_pvalue_dict = {} + primary_trait_symbol_value_dict = correlation_functions.make_gene_tissue_value_dict( + GeneNameLst=[self.this_trait.symbol], + TissueProbeSetFreezeId=tissue_dataset_id) + primary_trait_value = primary_trait_symbol_value_dict.values()[0] + + symbol_value_dict = correlation_functions.make_gene_tissue_value_dict( + gene_name_list=[], + tissue_dataset_id=tissue_dataset_id) + + symbol_corr_dict, symbol_pvalue_dict = correlation_functions.batch_cal_tissue_corr( + primaryTraitValue, + SymbolValueDict, + method=self.corr_method) + #else: + # symbol_corr_dict, symbol_pvalue_dict = correlation_functions.batch_cal_tissue_corr( + # primaryTraitValue, + # SymbolValueDict) + + return (symbolCorrDict, symbolPvalueDict) datasetFile.close() totalTraits = len(allcorrelations) _log.info("Done correlating using the fast method") @@ -939,78 +1050,6 @@ class CorrelationResults(object): return trait_list """ - def calculate_corr_for_all_tissues(self, tissue_dataset_id=None): - symbol_corr_dict = {} - symbol_pvalue_dict = {} - primary_trait_symbol_value_dict = correlation_functions.make_gene_tissue_value_dict( - GeneNameLst=[self.this_trait.symbol], - TissueProbeSetFreezeId=tissue_dataset_id) - primary_trait_value = primary_trait_symbol_value_dict.values()[0] - - symbol_value_dict = correlation_functions.make_gene_tissue_value_dict( - gene_name_list=[], - tissue_dataset_id=tissue_dataset_id) - - symbol_corr_dict, symbol_pvalue_dict = correlation_functions.batch_cal_tissue_corr( - primaryTraitValue, - SymbolValueDict, - method=self.corr_method) - #else: - # symbol_corr_dict, symbol_pvalue_dict = correlation_functions.batch_cal_tissue_corr( - # primaryTraitValue, - # SymbolValueDict) - - return (symbolCorrDict, symbolPvalueDict) - - - def do_tissue_correlation_by_list(self, tissue_dataset_id=1): - """Given a list of correlation results (self.correlation_results), gets the tissue correlation value for each""" - - #Gets tissue expression values for the primary trait - primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list = [self.this_trait.symbol]) - - print("primary_trait_tissue_vals: ", pf(primary_trait_tissue_vals_dict)) - - if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict: - primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower()] - - #gene_symbol_list = [] - # - #for trait in self.correlation_results: - # if hasattr(trait, 'symbol'): - # gene_symbol_list.append(trait.symbol) - - gene_symbol_list = [trait.symbol for trait in self.correlation_results if trait.symbol] - - corr_result_tissue_vals_dict= correlation_functions.get_trait_symbol_and_tissue_values( - symbol_list=gene_symbol_list) - - print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict)) - - for trait in self.correlation_results: - if trait.symbol and trait.symbol.lower() in corr_result_tissue_vals_dict: - this_trait_tissue_values = corr_result_tissue_vals_dict[trait.symbol.lower()] - - result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values, - this_trait_tissue_values, - self.corr_method) - - trait.tissue_corr = result[0] - trait.tissue_pvalue = result[2] - - #print("trait.tissue_corr / pvalue: ", str(trait.tissue_corr) + " :: " + str(trait.tissue_pvalue)) - - - # else: - # trait.tissue_corr = None - # trait.tissue_pvalue = None - #else: - # for trait in self.correlation_results: - # trait.tissue_corr = None - # trait.tissue_pvalue = None - - #return self.correlation_results diff --git a/wqflask/wqflask/templates/show_trait_calculate_correlations.html b/wqflask/wqflask/templates/show_trait_calculate_correlations.html index 12a064c0..73502392 100644 --- a/wqflask/wqflask/templates/show_trait_calculate_correlations.html +++ b/wqflask/wqflask/templates/show_trait_calculate_correlations.html @@ -5,7 +5,7 @@ <div class="control-group"> <label for="corr_method" class="control-label">Method</label> <div class="controls"> - <select name="corr_method"> + <select name="corr_type"> <option value="sample">Sample r</option> <option value="lit">Literature r</option> <option value="tissue">Tissue r</option> |