diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/wqflask/correlation/show_corr_results.py | 301 | ||||
| -rw-r--r-- | wqflask/wqflask/templates/base.html | 2 |
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 %} |