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-rw-r--r--wqflask/base/mrna_assay_tissue_data.py2
-rw-r--r--wqflask/wqflask/correlation/show_corr_results.py278
-rw-r--r--wqflask/wqflask/templates/correlation_page.html5
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}}&amp;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>