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authorZachary Sloan2013-06-20 21:14:51 +0000
committerZachary Sloan2013-06-20 21:14:51 +0000
commit56e91a003a6931de9acaeb8de9410ef91bcc1857 (patch)
treef2d7942e74d6cbaf1692b24fc0cd8bb48932e379
parent234d45189fe43e78fbec94141f7df72cf25133da (diff)
downloadgenenetwork2-56e91a003a6931de9acaeb8de9410ef91bcc1857.tar.gz
Copied over Lei's correlation code from after he pushed yesterday
-rw-r--r--wqflask/wqflask/correlation/show_corr_results.py301
-rw-r--r--wqflask/wqflask/templates/base.html2
2 files changed, 75 insertions, 228 deletions
diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 3b8b7ba2..1410ae0c 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -13,28 +13,21 @@
# This program is available from Source Forge: at GeneNetwork Project
# (sourceforge.net/projects/genenetwork/).
#
-# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
-# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
-#
+# Contact Dr. Robert W. Williams at rwilliams@uthsc.edu
#
#
# This module is used by GeneNetwork project (www.genenetwork.org)
-#
-# Created by GeneNetwork Core Team 2010/08/10
-#
-# Last updated by NL 2011/02/11
-# Last updated by Christian Fernandez 2012/04/07
-# Refactored correlation calculation into smaller functions in preparation of
-# separating html from existing code
from __future__ import absolute_import, print_function, division
+import sys
+sys.path.append(".")
+
import gc
import string
import cPickle
import os
import time
-#import pyXLWriter as xl
import pp
import math
import collections
@@ -53,7 +46,7 @@ from utility.TDCell import TDCell
from base.trait import GeneralTrait
from base import data_set
from base.templatePage import templatePage
-from utility import webqtlUtil, helper_functions
+from utility import webqtlUtil, helper_functions, corr_result_helpers
from dbFunction import webqtlDatabaseFunction
import utility.webqtlUtil #this is for parallel computing only.
from wqflask.correlation import correlationFunction
@@ -103,99 +96,112 @@ class CorrelationResults(object):
with Bench("Doing correlations"):
helper_functions.get_species_dataset_trait(self, start_vars)
self.dataset.group.read_genotype_file()
-
+
corr_samples_group = start_vars['corr_samples_group']
-
+
self.sample_data = {}
self.corr_method = start_vars['corr_sample_method']
-
+
#The two if statements below append samples to the sample list based upon whether the user
#rselected Primary Samples Only, Other Samples Only, or All Samples
-
+
primary_samples = (self.dataset.group.parlist +
self.dataset.group.f1list +
self.dataset.group.samplelist)
-
+
#If either BXD/whatever Only or All Samples, append all of that group's samplelist
if corr_samples_group != 'samples_other':
self.process_samples(start_vars, primary_samples, ())
-
+
#If either Non-BXD/whatever or All Samples, get all samples from this_trait.data and
#exclude the primary samples (because they would have been added in the previous
#if statement if the user selected All Samples)
if corr_samples_group != 'samples_primary':
self.process_samples(start_vars, self.this_trait.data.keys(), primary_samples)
-
+
self.target_dataset = data_set.create_dataset(start_vars['corr_dataset'])
self.target_dataset.get_trait_data()
self.correlation_data = {}
for trait, values in self.target_dataset.trait_data.iteritems():
- this_trait_values = []
- target_values = []
+ 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_values.append(sample_value)
- target_values.append(target_sample_value)
+ 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)
- this_trait_values, target_values = normalize_values(this_trait_values, target_values)
+ print("num_overlap:", num_overlap)
if self.corr_method == 'pearson':
- sample_r, sample_p = scipy.stats.pearsonr(this_trait_values, target_values)
+ sample_r, sample_p = scipy.stats.pearsonr(this_trait_vals, target_vals)
else:
- sample_r, sample_p = scipy.stats.spearmanr(this_trait_values, target_values)
+ sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals)
- self.correlation_data[trait] = [sample_r, sample_p]
+ 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_data_slice = collections.OrderedDict()
+ self.correlation_results = []
+
+ #self.correlation_data_slice = collections.OrderedDict()
for trait_counter, trait in enumerate(self.correlation_data.keys()[:300]):
- trait_object = GeneralTrait(dataset=self.dataset, name=trait)
- 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 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 = trait_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
- )
- self.correlation_data_slice[trait] = trait_info
+ trait_object = GeneralTrait(dataset=self.dataset, name=trait, get_qtl_info=True)
+ trait_object.sample_r = self.correlation_data[trait][0]
+ trait_object.sample_p = self.correlation_data[trait][1]
+ trait_object_num_overlap = self.correlation_data[trait][2]
+ self.correlation_results.append(trait_object)
+
+ #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:
+ # 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 = trait_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
@@ -939,162 +945,3 @@ class CorrelationResults(object):
return traitList
-
- def createExcelFileWithTitleAndFooter(self, workbook=None, identification=None, db=None, returnNumber=None):
-
- worksheet = workbook.add_worksheet()
-
- titleStyle = workbook.add_format(align = 'left', bold = 0, size=14, border = 1, border_color="gray")
-
- ##Write title Info
- # Modified by Hongqiang Li
- worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle)
- worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle)
- worksheet.write([2, 0], "Trait : %s" % identification, titleStyle)
- worksheet.write([3, 0], "Database : %s" % db.fullname, titleStyle)
- worksheet.write([4, 0], "Date : %s" % time.strftime("%B %d, %Y", time.gmtime()), titleStyle)
- worksheet.write([5, 0], "Time : %s GMT" % time.strftime("%H:%M ", time.gmtime()), titleStyle)
- worksheet.write([6, 0], "Status of data ownership: Possibly unpublished data; please see %s/statusandContact.html for details on sources, ownership, and usage of these data." % webqtlConfig.PORTADDR, titleStyle)
- #Write footer info
- worksheet.write([9 + returnNumber, 0], "Funding for The GeneNetwork: NIAAA (U01AA13499, U24AA13513), NIDA, NIMH, and NIAAA (P20-DA21131), NCI MMHCC (U01CA105417), and NCRR (U01NR 105417)", titleStyle)
- worksheet.write([10 + returnNumber, 0], "PLEASE RETAIN DATA SOURCE INFORMATION WHENEVER POSSIBLE", titleStyle)
-
- return worksheet
-
-
- def getTableHeaderForGeno(self, method=None, worksheet=None, newrow=None, headingStyle=None):
-
- tblobj_header = []
-
- if method in ["1","3","4"]:
- tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
- THCell(HT.TD('Record', HT.BR(), 'ID', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Record ID', idx=1),
- THCell(HT.TD('Location', HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Location (Chr and Mb)', idx=2),
- THCell(HT.TD(HT.Href(
- text = HT.Span('Sample',HT.BR(), 'r', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
- target = '_blank',
- url = "/correlationAnnotation.html#genetic_r"),
- Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample r", idx=3),
- THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=4),
- THCell(HT.TD(HT.Href(
- text = HT.Span('Sample',HT.BR(), 'p(r)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
- target = '_blank',
- url = "/correlationAnnotation.html#genetic_p_r"),
- Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(r)", idx=5)]]
-
- for ncol, item in enumerate(['Record ID', 'Location (Chr, Mb)', 'Sample r', 'N Cases', 'Sample p(r)']):
- worksheet.write([newrow, ncol], item, headingStyle)
- worksheet.set_column([ncol, ncol], 2*len(item))
- else:
- tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
- THCell(HT.TD('Record', HT.BR(), 'ID', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Record ID', idx=1),
- THCell(HT.TD('Location', HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Location (Chr and Mb)', idx=2),
- THCell(HT.TD(HT.Href(
- text = HT.Span('Sample',HT.BR(), 'rho', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
- target = '_blank',
- url = "/correlationAnnotation.html#genetic_rho"),
- Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample rho", idx=3),
- THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=4),
- THCell(HT.TD(HT.Href(
- text = HT.Span('Sample',HT.BR(), 'p(rho)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
- target = '_blank',
- url = "/correlationAnnotation.html#genetic_p_rho"),
- Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(rho)", idx=5)]]
-
- for ncol, item in enumerate(['Record ID', 'Location (Chr, Mb)', 'Sample rho', 'N Cases', 'Sample p(rho)']):
- worksheet.write([newrow, ncol], item, headingStyle)
- worksheet.set_column([ncol, ncol], 2*len(item))
-
-
- return tblobj_header, worksheet
-
-
- def getTableBodyForGeno(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None):
-
- tblobj_body = []
-
- for thisTrait in traitList:
- tr = []
-
- trId = str(thisTrait)
-
- corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.corr))
-
- tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-
- tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"),align="left", Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name.upper()))
-
- #XZ: trait_location_value is used for sorting
- trait_location_repr = '--'
- trait_location_value = 1000000
-
- if thisTrait.chr and thisTrait.mb:
- try:
- trait_location_value = int(thisTrait.chr)*1000 + thisTrait.mb
- except:
- if thisTrait.chr.upper() == 'X':
- trait_location_value = 20*1000 + thisTrait.mb
- else:
- trait_location_value = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb
-
- trait_location_repr = 'Chr%s: %.6f' % (thisTrait.chr, float(thisTrait.mb) )
-
- tr.append(TDCell(HT.TD(trait_location_repr, Class="fs12 fwn b1 c222", nowrap="on"), trait_location_repr, trait_location_value))
-
-
- repr='%3.3f' % thisTrait.corr
- tr.append(TDCell(HT.TD(HT.Href(text=repr, url="javascript:showCorrPlot('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"), Class="fs12 fwn ffl b1 c222", nowrap='ON', align='right'),repr,abs(thisTrait.corr)))
-
- repr = '%d' % thisTrait.nOverlap
- tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222",align='right'),repr,thisTrait.nOverlap))
-
- repr = webqtlUtil.SciFloat(thisTrait.corrPValue)
- tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue))
-
- tblobj_body.append(tr)
-
- for ncol, item in enumerate([thisTrait.name, trait_location_repr, thisTrait.corr, thisTrait.nOverlap, thisTrait.corrPValue]):
- worksheet.write([newrow, ncol], item)
- newrow += 1
-
- return tblobj_body, worksheet, corrScript
-
-def normalize_values(values_1, values_2):
- N = min(len(values_1), len(values_2))
- X = []
- Y = []
- for i in range(N):
- if values_1[i]!= None and values_2[i]!= None:
- X.append(values_1[i])
- Y.append(values_2[i])
-
- return (X, Y)
-
-
-def cal_correlation(values_1, values_2):
- N = min(len(values_1), len(values_2))
- X = []
- Y = []
- for i in range(N):
- if values_1[i]!= None and values_2[i]!= None:
- X.append(values_1[i])
- Y.append(values_2[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)
- x_mean = sx/NN
- y_mean = sy/NN
- xyd = 0.0
- sxd = 0.0
- syd = 0.0
- for i in range(NN):
- xyd += (X[i] - x_mean)*(Y[i] - y_mean)
- sxd += (X[i] - x_mean)*(X[i] - x_mean)
- syd += (Y[i] - y_mean)*(Y[i] - y_mean)
- try:
- corr = xyd/(sqrt(sxd)*sqrt(syd))
- except:
- corr = 0
- return (corr, NN)
diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index bdb1c362..741c5425 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -180,7 +180,7 @@
<script src="http://ajax.googleapis.com/ajax/libs/jqueryui/1.9.1/jquery-ui.min.js" type="text/javascript"></script>
<script language="javascript" type="text/javascript" src="/static/packages/colorbox/jquery.colorbox.js"></script>
- <script type="text/javascript" src="/static/new/javascript/login.js"></script>
+<!-- <script type="text/javascript" src="/static/new/javascript/login.js"></script>-->
{% block js %}
{% endblock %}