diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/base/mrna_assay_tissue_data.py | 2 | ||||
| -rw-r--r-- | wqflask/wqflask/correlation/show_corr_results.py | 278 | ||||
| -rw-r--r-- | wqflask/wqflask/templates/correlation_page.html | 5 |
3 files changed, 155 insertions, 130 deletions
diff --git a/wqflask/base/mrna_assay_tissue_data.py b/wqflask/base/mrna_assay_tissue_data.py index 7eb07028..be5df657 100644 --- a/wqflask/base/mrna_assay_tissue_data.py +++ b/wqflask/base/mrna_assay_tissue_data.py @@ -38,7 +38,7 @@ class MrnaAssayTissueData(object): # with highest mean value # Due to the limit size of TissueProbeSetFreezeId table in DB, # performance of inner join is acceptable.MrnaAssayTissueData(gene_symbols=symbol_list) - print("len(gene_symbols): ", len(gene_symbols)) + #print("len(gene_symbols): ", len(gene_symbols)) if len(gene_symbols) == 0: query += '''Symbol!='' and Symbol Is Not Null group by Symbol) as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py index c6bc5b2a..42d5acd6 100644 --- a/wqflask/wqflask/correlation/show_corr_results.py +++ b/wqflask/wqflask/correlation/show_corr_results.py @@ -133,58 +133,46 @@ class CorrelationResults(object): 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) + tissue_corr_data = self.do_tissue_corr_for_all_traits(trait_gene_symbols = trait_symbol_dict) + #print("tissue_corr_data: ", pf(tissue_corr_data)) 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] + self.get_sample_r_and_p_values(trait = trait, target_samples = self.target_dataset.trait_data[trait]) + #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 == "lit": + trait_symbol_dict = self.dataset.retrieve_gene_symbols() + 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.get_sample_r_and_p_values(trait = trait, target_samples = values) + self.correlation_data = collections.OrderedDict(sorted(self.correlation_data.items(), key=lambda t: -abs(t[1][0]))) + #print("correlation_data: ", pf(self.correlation_data)) + 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) + #print("gene symbol: ", trait_object.symbol) trait_object.sample_r = self.correlation_data[trait][0] trait_object.sample_p = self.correlation_data[trait][1] @@ -193,17 +181,20 @@ 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) - if self.corr_method != "tissue": + if self.corr_type == "tissue": + trait_object.tissue_corr = tissue_corr_data[trait][1] + trait_object.tissue_pvalue = tissue_corr_data[trait][2] + else: # 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) - if self.corr_method != "tissue": + if self.corr_type != "lit": + self.do_lit_correlation_for_trait_list() + + if self.corr_type != "tissue": self.do_tissue_correlation_for_trait_list() print("self.correlation_results: ", pf(self.correlation_results)) @@ -308,36 +299,138 @@ class CorrelationResults(object): #return self.correlation_results - def do_tissue_corr_for_all_traits(self, trait_symbols, tissue_dataset_id=1): + def do_tissue_corr_for_all_traits(self, trait_gene_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()] + #print("trait_gene_symbols: ", pf(trait_gene_symbols.values())) 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)) + symbol_list=trait_gene_symbols.values()) - for trait, symbol in trait_symbols.iteritems(): - if symbol.lower() in corr_result_tissue_vals_dict: + #print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict)) + + #print("trait_gene_symbols: ", pf(trait_gene_symbols)) + + tissue_corr_data = {} + for trait, symbol in trait_gene_symbols.iteritems(): + if symbol and symbol.lower() in corr_result_tissue_vals_dict: this_trait_tissue_values = corr_result_tissue_vals_dict[symbol.lower()] + #print("this_trait_tissue_values: ", pf(this_trait_tissue_values)) 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]] + tissue_corr_data[trait] = [symbol, result[0], result[2]] - correlation_data = collections.OrderedDict(sorted(self.correlation_data.items(), - key=lambda t: -abs(t[1][1]))) + tissue_corr_data = collections.OrderedDict(sorted(tissue_corr_data.items(), + key=lambda t: -abs(t[1][1]))) + + return tissue_corr_data - return correlation_data + def do_lit_correlation_for_trait_list(self): + + input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(self.dataset.group.species.lower(), self.this_trait.geneid) + + for trait in self.correlation_results: + + if trait.geneid: + trait.mouse_gene_id = self.convert_to_mouse_gene_id(self.dataset.group.species.lower(), trait.geneid) + else: + trait.mouse_gene_id = None + + if trait.mouse_gene_id and str(trait.mouse_gene_id).find(";") == -1: + result = g.db.execute( + """SELECT value + FROM LCorrRamin3 + WHERE GeneId1='%s' and + GeneId2='%s' + """ % (escape(trait.mouse_gene_id), escape(self.this_trait.geneid)) + ).fetchone() + if not result: + result = g.db.execute("""SELECT value + FROM LCorrRamin3 + WHERE GeneId2='%s' and + GeneId1='%s' + """ % (escape(trait.mouse_gene_id), escape(input_trait_mouse_gene_id)) + ).fetchone() + + if result: + lit_corr = result.value + + if lit_corr: + trait.lit_corr = lit_corr + else: + trait.lit_corr = 0 + else: + trait.lit_corr = 0 + + + def convert_to_mouse_gene_id(self, species=None, gene_id=None): + """If the species is rat or human, translate the gene_id to the mouse geneid + + If there is no input gene_id or there's no corresponding mouse gene_id, return None + + """ + if not gene_id: + return None + + mouse_gene_id = None + + if species == 'mouse': + mouse_gene_id = gene_id + + elif species == 'rat': + mouse_gene_id = g.db.execute( + """SELECT mouse + FROM GeneIDXRef + WHERE rat='%d' + """, escape(int(gene_id))).fetchone().mouse + elif species == 'human': + mouse_gene_id = g.db.execute( + """SELECT mouse + FROM GeneIDXRef + WHERE human='%d' + """, escape(int(gene_id))).fetchone().mouse + + #print("mouse_geneid:", mouse_geneid) + + return mouse_gene_id + + def get_sample_r_and_p_values(self, trait, target_samples): + """Calculates the sample r (or rho) and p-value + + Given a primary trait and a target trait's sample values, + calculates either the pearson r or spearman rho and the p-value + using the corresponding scipy functions. + + """ + + 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) + 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] + def do_tissue_corr_for_all_traits_2(self): """Comments Possibly Out of Date!!!!! @@ -508,39 +601,6 @@ class CorrelationResults(object): self.sample_data[str(sample)] = float(value) - #XZ, 12/12/2008: if the species is rat or human, translate the geneid to mouse geneid - #XZ, 12/12/2008: if the input geneid is 'None', return 0 - #XZ, 12/12/2008: if the input geneid has no corresponding mouse geneid, return 0 - def translateToMouseGeneID(self, species, geneid): - #mouse_geneid = 0 - - if not geneid: - return 0 - - #self.id, self.name, self.fullname, self.shortname = g.db.execute(""" - # SELECT Id, Name, FullName, ShortName - # FROM %s - # WHERE public > %s AND - # (Name = '%s' OR FullName = '%s' OR ShortName = '%s') - # """ % (query_args)).fetchone() - - if species == 'mouse': - mouse_geneid = geneid - elif species == 'rat': - mouse_geneid = g.db.execute( - """SELECT mouse FROM GeneIDXRef WHERE rat='%d'""", int(geneid)).fetchone().mouse - #if record: - # mouse_geneid = record[0] - elif species == 'human': - mouse_geneid = g.db.execute( - """SELECT mouse FROM GeneIDXRef WHERE human='%d'""", int(geneid)).fetchone().mouse - #if record: - # mouse_geneid = record[0] - print("mouse_geneid:", mouse_geneid) - return mouse_geneid - - - ##XZ, 12/16/2008: the input geneid is of mouse type #def checkForLitInfo(self,geneId): @@ -751,44 +811,6 @@ class CorrelationResults(object): return litCorrDict - - def getLiteratureCorrelationByList(self, input_trait_mouse_geneid=None, species=None, traitList=None): - - tmpTableName = webqtlUtil.genRandStr(prefix="LITERATURE") - - q1 = 'CREATE TEMPORARY TABLE %s (GeneId1 int(12) unsigned, GeneId2 int(12) unsigned PRIMARY KEY, value double)' % tmpTableName - q2 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId1,GeneId2,value FROM LCorrRamin3 WHERE GeneId1=%s' % (tmpTableName, input_trait_mouse_geneid) - q3 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId2,GeneId1,value FROM LCorrRamin3 WHERE GeneId2=%s AND GeneId1!=%s' % (tmpTableName, input_trait_mouse_geneid, input_trait_mouse_geneid) - - for x in [q1,q2,q3]: - self.cursor.execute(x) - - for thisTrait in traitList: - try: - if thisTrait.geneid: - thisTrait.mouse_geneid = self.translateToMouseGeneID(species, thisTrait.geneid) - else: - thisTrait.mouse_geneid = 0 - except: - thisTrait.mouse_geneid = 0 - - if thisTrait.mouse_geneid and str(thisTrait.mouse_geneid).find(";") == -1: - try: - self.cursor.execute("SELECT value FROM %s WHERE GeneId2 = %s" % (tmpTableName, thisTrait.mouse_geneid)) - result = self.cursor.fetchone() - if result: - thisTrait.LCorr = result[0] - else: - thisTrait.LCorr = None - except: - thisTrait.LCorr = None - else: - thisTrait.LCorr = None - - self.cursor.execute("DROP TEMPORARY TABLE %s" % tmpTableName) - - return traitList - def get_traits(self, vals): #Todo: Redo cached stuff using memcached diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html index 7082dbf2..4d09cf20 100644 --- a/wqflask/wqflask/templates/correlation_page.html +++ b/wqflask/wqflask/templates/correlation_page.html @@ -28,11 +28,13 @@ <th>Sample r</th> <th>N Cases</th> <th>Sample p(r)</th> + <th>Lit Corr</th> <th>Tissue r</th> <th>Tissue p(r)</th> {% else %} <th>Sample rho</th> <th>Sample p(rho)</th> + <th>Lit Corr</th> <th>Tissue rho</th> <th>Tissue p(rho)</th> {% endif %} @@ -42,7 +44,7 @@ <tbody> {% for trait in correlation_results %} <tr> - <td>{{ trait.name }}</td> + <td><a href="/show_trait?trait_id={{trait.name}}&dataset={{trait.dataset.name}}">{{ trait.name }}</a></td> <td>{{ trait.symbol }}</td> <td>{{ trait.alias }}</td> <td>{{ trait.description }}</td> @@ -53,6 +55,7 @@ <td>{{'%0.3f'|format(trait.sample_r)}}</td> <td>{{ trait.num_overlap }}</td> <td>{{'%0.3e'|format(trait.sample_p)}}</td> + <td>{{'%0.3f'|format(trait.lit_corr)}}</td> <td>{{'%0.3f'|format(trait.tissue_corr)}}</td> <td>{{'%0.3e'|format(trait.tissue_pvalue)}}</td> </tr> |