diff options
| author | zsloan | 2015-03-27 20:28:51 +0000 |
|---|---|---|
| committer | zsloan | 2015-03-27 20:28:51 +0000 |
| commit | d0911a04958a04042da02a334ccc528dae79cc17 (patch) | |
| tree | 3c48e2e937c1dbeaf00a5697c87ed251afa5c8f1 /web/webqtl/correlation | |
| parent | a840ad18e1fe3db98a359a159e9b9b72367a2839 (diff) | |
| download | genenetwork2-d0911a04958a04042da02a334ccc528dae79cc17.tar.gz | |
Removed everything from 'web' directory except genofiles and renamed the directory to 'genotype_files'
Diffstat (limited to 'web/webqtl/correlation')
| -rwxr-xr-x | web/webqtl/correlation/CorrelationPage.py | 2065 | ||||
| -rwxr-xr-x | web/webqtl/correlation/PartialCorrDBPage.py | 1359 | ||||
| -rwxr-xr-x | web/webqtl/correlation/PartialCorrInputPage.py | 484 | ||||
| -rwxr-xr-x | web/webqtl/correlation/PartialCorrTraitPage.py | 310 | ||||
| -rwxr-xr-x | web/webqtl/correlation/PlotCorrelationPage.py | 683 | ||||
| -rwxr-xr-x | web/webqtl/correlation/__init__.py | 0 | ||||
| -rwxr-xr-x | web/webqtl/correlation/correlationFunction.py | 923 |
7 files changed, 0 insertions, 5824 deletions
diff --git a/web/webqtl/correlation/CorrelationPage.py b/web/webqtl/correlation/CorrelationPage.py deleted file mode 100755 index 0c98f032..00000000 --- a/web/webqtl/correlation/CorrelationPage.py +++ /dev/null @@ -1,2065 +0,0 @@ -## Copyright (C) University of Tennessee Health Science Center, Memphis, TN. -# -# This program is free software: you can redistribute it and/or modify it -# under the terms of the GNU Affero General Public License -# as published by the Free Software Foundation, either version 3 of the -# License, or (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. -# See the GNU Affero General Public License for more details. -# -# 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 -# -# -# -# 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 - -import string -from math import * -import cPickle -import os -import time -import pyXLWriter as xl -import pp -import math - -from htmlgen import HTMLgen2 as HT -import reaper - -from base import webqtlConfig -from utility.THCell import THCell -from utility.TDCell import TDCell -from base.webqtlTrait import webqtlTrait -from base.webqtlDataset import webqtlDataset -from base.templatePage import templatePage -from utility import webqtlUtil -from dbFunction import webqtlDatabaseFunction -import utility.webqtlUtil #this is for parallel computing only. -from correlation import correlationFunction - -import logging -logging.basicConfig(filename="/tmp/gn_log", level=logging.INFO) -_log = logging.getLogger("correlation") - -METHOD_SAMPLE_PEARSON = "1" -METHOD_SAMPLE_RANK = "2" -METHOD_LIT = "3" -METHOD_TISSUE_PEARSON = "4" -METHOD_TISSUE_RANK = "5" - -TISSUE_METHODS = [METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK] - -TISSUE_MOUSE_DB = 1 - -class AuthException(Exception): pass - - -class Trait(object): - def __init__(self, name, raw_values = None, lit_corr = None, tissue_corr = None, p_tissue = None): - self.name = name - self.raw_values = raw_values - self.lit_corr = lit_corr - self.tissue_corr = tissue_corr - self.p_tissue = p_tissue - self.correlation = 0 - self.p_value = 0 - - @staticmethod - def from_csv(line, data_start = 1): - name = line[0] - numbers = line[data_start:] - # _log.info(numbers) - numbers = [ float(number) for number in numbers ] - - return Trait(name, raw_values = numbers) - - def calculate_correlation(self, values, method): - """Calculate the correlation value and p value according to the method specified""" - - #ZS: This takes the list of values of the trait our selected trait is being correlated against and removes the values of the samples our trait has no value for - #There's probably a better way of dealing with this, but I'll have to ask Christian - updated_raw_values = [] - updated_values = [] - for i in range(len(values)): - if values[i] != "None": - updated_raw_values.append(self.raw_values[i]) - updated_values.append(values[i]) - - self.raw_values = updated_raw_values - values = updated_values - - if method == METHOD_SAMPLE_PEARSON or method == METHOD_LIT or method == METHOD_TISSUE_PEARSON: - corr,nOverlap = webqtlUtil.calCorrelation(self.raw_values, values, len(values)) - else: - corr,nOverlap = webqtlUtil.calCorrelationRank(self.raw_values, values, len(values)) - - self.correlation = corr - self.overlap = nOverlap - - if self.overlap < 3: - self.p_value = 1.0 - else: - #ZS - This is probably the wrong way to deal with this. Correlation values of 1.0 definitely exist (the trait correlated against itself), so zero division needs to br prevented. - if abs(self.correlation) >= 1.0: - self.p_value = 0.0 - else: - ZValue = 0.5*log((1.0+self.correlation)/(1.0-self.correlation)) - ZValue = ZValue*sqrt(self.overlap-3) - self.p_value = 2.0*(1.0 - reaper.normp(abs(ZValue))) - - - -#XZ, 01/14/2009: This method is for parallel computing only. -#XZ: It is supposed to be called when "Genetic Correlation, Pearson's r" (method 1) -#XZ: or "Genetic Correlation, Spearman's rho" (method 2) is selected -def compute_corr( input_nnCorr, input_trait, input_list, computing_method): - - allcorrelations = [] - - for line in input_list: - tokens = line.split('","') - tokens[-1] = tokens[-1][:-2] #remove the last " - tokens[0] = tokens[0][1:] #remove the first " - - traitdataName = tokens[0] - database_trait = tokens[1:] - - if computing_method == "1": #XZ: Pearson's r - corr,nOverlap = utility.webqtlUtil.calCorrelationText(input_trait, database_trait, input_nnCorr) - else: #XZ: Spearman's rho - corr,nOverlap = utility.webqtlUtil.calCorrelationRankText(input_trait, database_trait, input_nnCorr) - traitinfo = [traitdataName,corr,nOverlap] - allcorrelations.append(traitinfo) - - return allcorrelations - -def get_correlation_method_key(form_data): - #XZ, 09/28/2008: if user select "1", then display 1, 3 and 4. - #XZ, 09/28/2008: if user select "2", then display 2, 3 and 5. - #XZ, 09/28/2008: if user select "3", then display 1, 3 and 4. - #XZ, 09/28/2008: if user select "4", then display 1, 3 and 4. - #XZ, 09/28/2008: if user select "5", then display 2, 3 and 5. - - method = form_data.formdata.getvalue("method") - if method not in ["1", "2", "3" ,"4", "5"]: - return "1" - - return method - - -def get_custom_trait(form_data, cursor): - """Pulls the custom trait, if it exists, out of the form data""" - trait_name = form_data.formdata.getvalue('fullname') - - if trait_name: - trait = webqtlTrait(fullname=trait_name, cursor=cursor) - trait.retrieveInfo() - return trait - else: - return None - - -#XZ, 09/18/2008: get the information such as value, variance of the input strain names from the form. -def get_sample_data(form_data): - if form_data.allstrainlist: - mdpchoice = form_data.formdata.getvalue('MDPChoice') - #XZ, in HTML source code, it is "BXD Only" or "BXH only", and so on - if mdpchoice == "1": - strainlist = form_data.f1list + form_data.strainlist - #XZ, in HTML source code, it is "MDP Only" - elif mdpchoice == "2": - strainlist = [] - strainlist2 = form_data.f1list + form_data.strainlist - for strain in form_data.allstrainlist: - if strain not in strainlist2: - strainlist.append(strain) - #So called MDP Panel - if strainlist: - strainlist = form_data.f1list+form_data.parlist+strainlist - #XZ, in HTML source code, it is "All Cases" - else: - strainlist = form_data.allstrainlist - #XZ, 09/18/2008: put the trait data into dictionary form_data.allTraitData - form_data.readData(form_data.allstrainlist) - else: - mdpchoice = None - strainlist = form_data.strainlist - #XZ, 09/18/2008: put the trait data into dictionary form_data.allTraitData - form_data.readData() - - return strainlist - - -def get_mdp_choice(form_data): - if form_data.allstrainlist: - return form_data.formdata.getvalue("MDPChoice") - else: - return None - - -def get_species(fd, cursor): - #XZ, 3/16/2010: variable RISet must be pass by the form - RISet = fd.RISet - #XZ, 12/12/2008: get species infomation - species = webqtlDatabaseFunction.retrieveSpecies(cursor=cursor, RISet=RISet) - return species - - -def sortTraitCorrelations(traits, method="1"): - if method in TISSUE_METHODS: - traits.sort(key=lambda trait: trait.tissue_corr != None and abs(trait.tissue_corr), reverse=True) - elif method == METHOD_LIT: - traits.sort(key=lambda trait: trait.lit_corr != None and abs(trait.lit_corr), reverse=True) - else: - traits.sort(key=lambda trait: trait.correlation != None and abs(trait.correlation), reverse=True) - - return traits - - -def auth_user_for_db(db, cursor, target_db_name, privilege, username): - """Authorize a user for access to a database if that database is - confidential. A db (identified by a record in ProbeSetFreeze) contains a - list of authorized users who may access it, as well as its confidentiality - level. - - If the current user's privilege level is greater than 'user', ie: root or - admin, then they are automatically authed, otherwise, check the - AuthorizedUsers field for the presence of their name.""" - - if db.type == 'ProbeSet': - cursor.execute('SELECT Id, Name, FullName, confidentiality, AuthorisedUsers FROM ProbeSetFreeze WHERE Name = "%s"' % target_db_name) - indId, indName, indFullName, confidential, AuthorisedUsers = cursor.fetchall()[0] - - if confidential: - authorized = 0 - - #for the dataset that confidentiality is 1 - #1. 'admin' and 'root' can see all of the dataset - #2. 'user' can see the dataset that AuthorisedUsers contains his id(stored in the Id field of User table) - if webqtlConfig.USERDICT[privilege] > webqtlConfig.USERDICT['user']: - authorized = 1 - else: - if username in AuthorisedUsers.split(","): - authorized = 1 - - if not authorized: - raise AuthException("The %s database you selected is not open to the public at this time, please go back and select other database." % indFullName) - - -class CorrelationPage(templatePage): - - corrMinInformative = 4 - - PAGE_HEADING = "Correlation Table" - CORRELATION_METHODS = {"1" : "Genetic Correlation (Pearson's r)", - "2" : "Genetic Correlation (Spearman's rho)", - "3" : "SGO Literature Correlation", - "4" : "Tissue Correlation (Pearson's r)", - "5" : "Tissue Correlation (Spearman's rho)"} - - RANK_ORDERS = {"1": 0, "2": 1, "3": 0, "4": 0, "5": 1} - - - def error(self, message, error="Error", heading = None): - heading = heading or self.PAGE_HEADING - return templatePage.error(heading = heading, detail = [message], error=error) - - def __init__(self, fd): - - # Call the superclass constructor - templatePage.__init__(self, fd) - - # Connect to the database - if not self.openMysql(): - return - - # Read the genotype from a file - if not fd.genotype: - fd.readGenotype() - - sample_list = get_sample_data(fd) - mdp_choice = get_mdp_choice(fd) # No idea what this is yet - self.species = get_species(fd, self.cursor) - - #XZ, 09/18/2008: get all information about the user selected database. - target_db_name = fd.formdata.getvalue('database') - self.target_db_name = target_db_name - - try: - self.db = webqtlDataset(target_db_name, self.cursor) - except: - detail = ["The database you just requested has not been established yet."] - self.error(detail) - return - - # Auth if needed - try: - auth_user_for_db(self.db, self.cursor, target_db_name, self.privilege, self.userName) - except AuthException, e: - detail = [e.message] - return self.error(detail) - - #XZ, 09/18/2008: filter out the strains that have no value. - self.sample_names, vals, vars, N = fd.informativeStrains(sample_list) - - #CF - If less than a minimum number of strains/cases in common, don't calculate anything - if len(self.sample_names) < self.corrMinInformative: - detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, fd.RISet)] - self.error(heading=PAGE_HEADING,detail=detail) - - - self.method = get_correlation_method_key(fd) - correlation_method = self.CORRELATION_METHODS[self.method] - rankOrder = self.RANK_ORDERS[self.method] - - # CF - Number of results returned - self.returnNumber = int(fd.formdata.getvalue('criteria')) - - self.record_count = 0 - - myTrait = get_custom_trait(fd, self.cursor) - - - # We will not get Literature Correlations if there is no GeneId because there is nothing to look against - self.gene_id = int(fd.formdata.getvalue('GeneId') or 0) - - # We will not get Tissue Correlations if there is no gene symbol because there is nothing to look against - self.trait_symbol = myTrait.symbol - - - #XZ, 12/12/2008: if the species is rat or human, translate the geneid to mouse geneid - self.input_trait_mouse_gene_id = self.translateToMouseGeneID(self.species, self.gene_id) - - #XZ: As of Nov/13/2010, this dataset is 'UTHSC Illumina V6.2 RankInv B6 D2 average CNS GI average (May 08)' - self.tissue_probeset_freeze_id = 1 - - traitList = self.correlate(vals) - - _log.info("Done doing correlation calculation") - -############################################################################################################################################ - - TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee') - - mainfmName = webqtlUtil.genRandStr("fm_") - form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden')) - hddn = {'FormID': 'showDatabase', - 'ProbeSetID': '_', - 'database': self.target_db_name, - 'databaseFull': self.db.fullname, - 'CellID': '_', - 'RISet': fd.RISet, - 'identification': fd.identification} - - if myTrait: - hddn['fullname']=fd.formdata.getvalue('fullname') - if mdp_choice: - hddn['MDPChoice']=mdp_choice - - - #XZ, 09/18/2008: pass the trait data to next page by hidden parameters. - webqtlUtil.exportData(hddn, fd.allTraitData) - - if fd.incparentsf1: - hddn['incparentsf1']='ON' - - if fd.allstrainlist: - hddn['allstrainlist'] = string.join(fd.allstrainlist, ' ') - - - for key in hddn.keys(): - form.append(HT.Input(name=key, value=hddn[key], type='hidden')) - - #XZ, 11/21/2008: add two parameters to form - form.append(HT.Input(name="X_geneSymbol", value="", type='hidden')) - form.append(HT.Input(name="Y_geneSymbol", value="", type='hidden')) - - #XZ, 3/11/2010: add one parameter to record if the method is rank order. - form.append(HT.Input(name="rankOrder", value="%s" % rankOrder, type='hidden')) - - form.append(HT.Input(name="TissueProbeSetFreezeId", value="%s" % self.tissue_probeset_freeze_id, type='hidden')) - - #################################### - # generate the info on top of page # - #################################### - - info = self.getTopInfo(myTrait=myTrait, method=self.method, db=self.db, target_db_name=self.target_db_name, returnNumber=self.returnNumber, methodDict=self.CORRELATION_METHODS, totalTraits=traitList, identification=fd.identification ) - - ############## - # Excel file # - ############## - filename= webqtlUtil.genRandStr("Corr_") - xlsUrl = HT.Input(type='button', value = 'Download Table', onClick= "location.href='/tmp/%s.xls'" % filename, Class='button') - # Create a new Excel workbook - workbook = xl.Writer('%s.xls' % (webqtlConfig.TMPDIR+filename)) - headingStyle = workbook.add_format(align = 'center', bold = 1, border = 1, size=13, fg_color = 0x1E, color="white") - - #XZ, 3/18/2010: pay attention to the line number of header in this file. As of today, there are 7 lines. - worksheet = self.createExcelFileWithTitleAndFooter(workbook=workbook, identification=fd.identification, db=self.db, returnNumber=self.returnNumber) - - newrow = 7 - - -##################################################################### - - - #Select All, Deselect All, Invert Selection, Add to Collection - mintmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'showIntMap');" % mainfmName) - mintmap_img = HT.Image("/images/multiple_interval_mapping1_final.jpg", name='mintmap', alt="Multiple Interval Mapping", title="Multiple Interval Mapping", style="border:none;") - mintmap.append(mintmap_img) - mcorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'compCorr');" % mainfmName) - mcorr_img = HT.Image("/images/compare_correlates2_final.jpg", alt="Compare Correlates", title="Compare Correlates", style="border:none;") - mcorr.append(mcorr_img) - cormatrix = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'corMatrix');" % mainfmName) - cormatrix_img = HT.Image("/images/correlation_matrix1_final.jpg", alt="Correlation Matrix and PCA", title="Correlation Matrix and PCA", style="border:none;") - cormatrix.append(cormatrix_img) - networkGraph = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'networkGraph');" % mainfmName) - networkGraph_img = HT.Image("/images/network_graph1_final.jpg", name='mintmap', alt="Network Graphs", title="Network Graphs", style="border:none;") - networkGraph.append(networkGraph_img) - heatmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'heatmap');" % mainfmName) - heatmap_img = HT.Image("/images/heatmap2_final.jpg", name='mintmap', alt="QTL Heat Map and Clustering", title="QTL Heatmap and Clustering", style="border:none;") - heatmap.append(heatmap_img) - partialCorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'partialCorrInput');" % mainfmName) - partialCorr_img = HT.Image("/images/partial_correlation_final.jpg", name='partialCorr', alt="Partial Correlation", title="Partial Correlation", style="border:none;") - partialCorr.append(partialCorr_img) - addselect = HT.Href(url="#redirect", onClick="addRmvSelection('%s', document.getElementsByName('%s')[0], 'addToSelection');" % (fd.RISet, mainfmName)) - addselect_img = HT.Image("/images/add_collection1_final.jpg", name="addselect", alt="Add To Collection", title="Add To Collection", style="border:none;") - addselect.append(addselect_img) - selectall = HT.Href(url="#redirect", onClick="checkAll(document.getElementsByName('%s')[0]);" % mainfmName) - selectall_img = HT.Image("/images/select_all2_final.jpg", name="selectall", alt="Select All", title="Select All", style="border:none;") - selectall.append(selectall_img) - selectinvert = HT.Href(url="#redirect", onClick = "checkInvert(document.getElementsByName('%s')[0]);" % mainfmName) - selectinvert_img = HT.Image("/images/invert_selection2_final.jpg", name="selectinvert", alt="Invert Selection", title="Invert Selection", style="border:none;") - selectinvert.append(selectinvert_img) - reset = HT.Href(url="#redirect", onClick="checkNone(document.getElementsByName('%s')[0]); return false;" % mainfmName) - reset_img = HT.Image("/images/select_none2_final.jpg", alt="Select None", title="Select None", style="border:none;") - reset.append(reset_img) - selecttraits = HT.Input(type='button' ,name='selecttraits',value='Select Traits', onClick="checkTraits(this.form);",Class="button") - selectgt = HT.Input(type='text' ,name='selectgt',value='-1.0', size=6,maxlength=10,onChange="checkNumeric(this,1.0,'-1.0','gthan','greater than filed')") - selectlt = HT.Input(type='text' ,name='selectlt',value='1.0', size=6,maxlength=10,onChange="checkNumeric(this,-1.0,'1.0','lthan','less than field')") - selectandor = HT.Select(name='selectandor') - selectandor.append(('AND','and')) - selectandor.append(('OR','or')) - selectandor.selected.append('AND') - - - #External analysis tools - GCATButton = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GCAT');" % mainfmName) - GCATButton_img = HT.Image("/images/GCAT_logo_final.jpg", name="GCAT", alt="GCAT", title="GCAT", style="border:none") - GCATButton.append(GCATButton_img) - - ODE = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'ODE');" % mainfmName) - ODE_img = HT.Image("/images/ODE_logo_final.jpg", name="ode", alt="ODE", title="ODE", style="border:none") - ODE.append(ODE_img) - - ''' - #XZ, 07/07/2010: I comment out this block of code. - WebGestaltScript = HT.Script(language="Javascript") - WebGestaltScript.append(""" -setTimeout('openWebGestalt()', 2000); -function openWebGestalt(){ -var thisForm = document['WebGestalt']; -makeWebGestaltTree(thisForm, '%s', %d, 'edag_only.php'); -} - """ % (mainfmName, len(traitList))) - ''' - - self.cursor.execute('SELECT GeneChip.GO_tree_value FROM GeneChip, ProbeFreeze, ProbeSetFreeze WHERE GeneChip.Id = ProbeFreeze.ChipId and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeSetFreeze.Name = "%s"' % self.db.name) - result = self.cursor.fetchone() - - if result: - GO_tree_value = result[0] - - if GO_tree_value: - - WebGestalt = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GOTree');" % mainfmName) - WebGestalt_img = HT.Image("/images/webgestalt_icon_final.jpg", name="webgestalt", alt="Gene Set Analysis Toolkit", title="Gene Set Analysis Toolkit", style="border:none") - WebGestalt.append(WebGestalt_img) - - hddnWebGestalt = { - 'id_list':'', - 'correlation':'', - 'id_value':'', - 'llid_list':'', - 'id_type':GO_tree_value, - 'idtype':'', - 'species':'', - 'list':'', - 'client':''} - - hddnWebGestalt['ref_type'] = hddnWebGestalt['id_type'] - hddnWebGestalt['cat_type'] = 'GO' - hddnWebGestalt['significancelevel'] = 'Top10' - - if self.species == 'rat': - hddnWebGestalt['org'] = 'Rattus norvegicus' - elif self.species == 'human': - hddnWebGestalt['org'] = 'Homo sapiens' - elif self.species == 'mouse': - hddnWebGestalt['org'] = 'Mus musculus' - else: - hddnWebGestalt['org'] = '' - - for key in hddnWebGestalt.keys(): - form.append(HT.Input(name=key, value=hddnWebGestalt[key], type='hidden')) - - - #Create tables with options, etc - - pageTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="100%", border=0, align="Left") - - containerTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="90%",border=0, align="Left") - - - if not GO_tree_value: - optionsTable = HT.TableLite(cellSpacing=2, cellPadding=0,width="480", height="80", border=0, align="Left") - optionsTable.append(HT.TR(HT.TD(selectall), HT.TD(reset), HT.TD(selectinvert), HT.TD(addselect), HT.TD(GCATButton), HT.TD(ODE), align="left")) - optionsTable.append(HT.TR(HT.TD(" "*1,"Select"), HT.TD("Deselect"), HT.TD(" "*1,"Invert"), HT.TD(" "*3,"Add"), HT.TD("Gene Set"), HT.TD(" "*2,"GCAT"))) - else: - optionsTable = HT.TableLite(cellSpacing=2, cellPadding=0,width="560", height="80", border=0, align="Left") - optionsTable.append(HT.TR(HT.TD(selectall), HT.TD(reset), HT.TD(selectinvert), HT.TD(addselect), HT.TD(GCATButton), HT.TD(ODE), HT.TD(WebGestalt), align="left")) - optionsTable.append(HT.TR(HT.TD(" "*1,"Select"), HT.TD("Deselect"), HT.TD(" "*1,"Invert"), HT.TD(" "*3,"Add"), HT.TD("Gene Set"), HT.TD(" "*2,"GCAT"), HT.TD(" "*3, "ODE"))) - containerTable.append(HT.TR(HT.TD(optionsTable))) - - functionTable = HT.TableLite(cellSpacing=2,cellPadding=0,width="480",height="80", border=0, align="Left") - functionRow = HT.TR(HT.TD(networkGraph, width="16.7%"), HT.TD(cormatrix, width="16.7%"), HT.TD(partialCorr, width="16.7%"), HT.TD(mcorr, width="16.7%"), HT.TD(mintmap, width="16.7%"), HT.TD(heatmap), align="left") - labelRow = HT.TR(HT.TD(" "*1,HT.Text("Graph")), HT.TD(" "*1,HT.Text("Matrix")), HT.TD(" "*1,HT.Text("Partial")), HT.TD(HT.Text("Compare")), HT.TD(HT.Text("QTL Map")), HT.TD(HT.Text(text="Heat Map"))) - functionTable.append(functionRow, labelRow) - containerTable.append(HT.TR(HT.TD(functionTable), HT.BR())) - - #more_options = HT.Image("/images/more_options1_final.jpg", name='more_options', alt="Expand Options", title="Expand Options", style="border:none;", Class="toggleShowHide") - - #containerTable.append(HT.TR(HT.TD(more_options, HT.BR(), HT.BR()))) - - moreOptions = HT.Input(type='button',name='options',value='More Options', onClick="",Class="toggle") - fewerOptions = HT.Input(type='button',name='options',value='Fewer Options', onClick="",Class="toggle") - - """ - if (fd.formdata.getvalue('showHideOptions') == 'less'): - containerTable.append(HT.TR(HT.TD(" "), height="10"), HT.TR(HT.TD(HT.Div(fewerOptions, Class="toggleShowHide")))) - containerTable.append(HT.TR(HT.TD(" "))) - else: - containerTable.append(HT.TR(HT.TD(" "), height="10"), HT.TR(HT.TD(HT.Div(moreOptions, Class="toggleShowHide")))) - containerTable.append(HT.TR(HT.TD(" "))) - """ - - containerTable.append(HT.TR(HT.TD(HT.Span(selecttraits,' with r > ',selectgt, ' ',selectandor, ' r < ',selectlt,Class="bd1 cbddf fs11")), style="display:none;", Class="extra_options")) - - chrMenu = HT.Input(type='hidden',name='chromosomes',value='all') - - corrHeading = HT.Paragraph('Correlation Table', Class="title") - - - tblobj = {} - - if self.db.type=="Geno": - containerTable.append(HT.TR(HT.TD(xlsUrl, height=60))) - - pageTable.append(HT.TR(HT.TD(containerTable))) - - tblobj['header'], worksheet = self.getTableHeaderForGeno( method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle) - newrow += 1 - - sortby = self.getSortByValue( calculationMethod = self.method ) - - corrScript = HT.Script(language="Javascript") - corrScript.append("var corrArray = new Array();") - - tblobj['body'], worksheet, corrScript = self.getTableBodyForGeno(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript) - - workbook.close() - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable") - - pageTable.append(HT.TR(HT.TD(div))) - - form.append(HT.Input(name='ShowStrains',type='hidden', value =1), - HT.Input(name='ShowLine',type='hidden', value =1), - HT.P(), HT.P(), pageTable) - TD_LR.append(corrHeading, info, form, HT.P()) - - self.dict['body'] = str(TD_LR) - self.dict['js1'] = '' - self.dict['title'] = 'Correlation' - - elif self.db.type=="Publish": - - containerTable.append(HT.TR(HT.TD(xlsUrl, height=40))) - - pageTable.append(HT.TR(HT.TD(containerTable))) - - tblobj['header'], worksheet = self.getTableHeaderForPublish(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle) - newrow += 1 - - sortby = self.getSortByValue( calculationMethod = self.method ) - - corrScript = HT.Script(language="Javascript") - corrScript.append("var corrArray = new Array();") - - tblobj['body'], worksheet, corrScript = self.getTableBodyForPublish(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript, species=self.species) - - workbook.close() - - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - # NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py; - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable") - - pageTable.append(HT.TR(HT.TD(div))) - - form.append( - HT.Input(name='ShowStrains',type='hidden', value =1), - HT.Input(name='ShowLine',type='hidden', value =1), - HT.P(), pageTable) - TD_LR.append(corrHeading, info, form, HT.P()) - - self.dict['body'] = str(TD_LR) - self.dict['js1'] = '' - self.dict['title'] = 'Correlation' - - - elif self.db.type=="ProbeSet": - tblobj['header'], worksheet = self.getTableHeaderForProbeSet(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle) - newrow += 1 - - sortby = self.getSortByValue( calculationMethod = self.method ) - - corrScript = HT.Script(language="Javascript") - corrScript.append("var corrArray = new Array();") - - tblobj['body'], worksheet, corrScript = self.getTableBodyForProbeSet(traitList=traitList, primaryTrait=myTrait, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript, species=self.species) - - workbook.close() - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - - #XZ: here is the table of traits - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1", hiddenColumns=["Gene ID","Homologene ID"]), corrScript, Id="sortable") - - - #XZ, 01/12/2009: create database menu for 'Add Correlation' - self.cursor.execute(""" - select - ProbeSetFreeze.FullName, ProbeSetFreeze.Id, Tissue.name - from - ProbeSetFreeze, ProbeFreeze, ProbeSetFreeze as ps2, ProbeFreeze as p2, Tissue - where - ps2.Id = %d - and ps2.ProbeFreezeId = p2.Id - and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id - and (ProbeFreeze.InbredSetId = p2.InbredSetId or (ProbeFreeze.InbredSetId in (1, 3) and p2.InbredSetId in (1, 3))) - and p2.ChipId = ProbeFreeze.ChipId - and ps2.Id != ProbeSetFreeze.Id - and ProbeFreeze.TissueId = Tissue.Id - and ProbeSetFreeze.public > %d - order by - ProbeFreeze.TissueId, ProbeSetFreeze.CreateTime desc - """ % (self.db.id, webqtlConfig.PUBLICTHRESH)) - - results = self.cursor.fetchall() - dbCustomizer = HT.Select(results, name = "customizer") - databaseMenuSub = preTissue = "" - for item in results: - TName, TId, TTissue = item - if TTissue != preTissue: - if databaseMenuSub: - dbCustomizer.append(databaseMenuSub) - databaseMenuSub = HT.Optgroup(label = '%s mRNA ------' % TTissue) - preTissue = TTissue - - databaseMenuSub.append(item[:2]) - if databaseMenuSub: - dbCustomizer.append(databaseMenuSub) - - #updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js - #variables: filename, strainIds and vals are required by getquerystring function - strainIds=self.getStrainIds(species=self.species, strains=self.sample_names) - var1 = HT.Input(name="filename", value=filename, type='hidden') - var2 = HT.Input(name="strainIds", value=strainIds, type='hidden') - var3 = HT.Input(name="vals", value=vals, type='hidden') - customizerButton = HT.Input(type="button", Class="button", value="Add Correlation", onClick = "xmlhttpPost('%smain.py?FormID=AJAX_table', 'sortable', (getquerystring(this.form)))" % webqtlConfig.CGIDIR) - - containerTable.append(HT.TR(HT.TD(HT.Span(var1,var2,var3,customizerButton, "with", dbCustomizer, Class="bd1 cbddf fs11"), HT.BR(), HT.BR()), style="display:none;", Class="extra_options")) - - containerTable.append(HT.TR(HT.TD(xlsUrl, HT.BR(), HT.BR()))) - - pageTable.append(HT.TR(HT.TD(containerTable))) - - pageTable.append(HT.TR(HT.TD(div))) - - if self.species == 'human': - heatmap = "" - - form.append(HT.Input(name='ShowStrains',type='hidden', value =1), - HT.Input(name='ShowLine',type='hidden', value =1), - info, HT.BR(), pageTable, HT.BR()) - - TD_LR.append(corrHeading, form, HT.P()) - - - self.dict['body'] = str(TD_LR) - self.dict['title'] = 'Correlation' - # updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js - self.dict['js1'] = '' - self.dict['js2'] = 'onLoad="pageOffset()"' - self.dict['layer'] = self.generateWarningLayer() - else: - self.dict['body'] = "" - - -############################# -# # -# CorrelationPage Functions # -# # -############################# - - - def getSortByValue(self, calculationMethod): - - if calculationMethod == "1": - sortby = ("Sample p(r)", "up") - elif calculationMethod == "2": - sortby = ("Sample p(rho)", "up") - elif calculationMethod == "3": #XZ: literature correlation - sortby = ("Lit Corr","down") - elif calculationMethod == "4": #XZ: tissue correlation - sortby = ("Tissue r", "down") - elif calculationMethod == "5": - sortby = ("Tissue rho", "down") - - return sortby - - - - def generateWarningLayer(self): - - layerString = """ -<!-- BEGIN FLOATING LAYER CODE //--> -<div id="warningLayer" style="padding:3px; border: 1px solid #222; - background-color: #fff; position:absolute;width:250px;left:100;top:100;visibility:hidden"> -<table border="0" width="250" class="cbrb" cellspacing="0" cellpadding="5"> -<tr> -<td width="100%"> - <table border="0" width="100%" cellspacing="0" cellpadding="0" height="36"> - <tr> - <td class="cbrb cw ff15 fwb" align="Center" width="100%" style="padding:4px"> - Sort Table - </td> - </tr> - <tr> - <td width="100%" bgcolor="#eeeeee" align="Center" style="padding:4px"> -<!-- PLACE YOUR CONTENT HERE //--> -Resorting this table <br> -<!-- END OF CONTENT AREA //--> - </td> - </tr> - </table> -</td> -</tr> -</table> -</div> -<!-- END FLOATING LAYER CODE //--> - - """ - - return layerString - - - #XZ, 01/07/2009: In HTML code, the variable 'database' corresponds to the column 'Name' in database table. - def getFileName(self, target_db_name): ### dcrowell August 2008 - """Returns the name of the reference database file with which correlations are calculated. - Takes argument cursor which is a cursor object of any instance of a subclass of templatePage - Used by correlationPage""" - - query = 'SELECT Id, FullName FROM ProbeSetFreeze WHERE Name = "%s"' % target_db_name - self.cursor.execute(query) - result = self.cursor.fetchone() - Id = result[0] - FullName = result[1] - FullName = FullName.replace(' ','_') - FullName = FullName.replace('/','_') - - FileName = 'ProbeSetFreezeId_' + str(Id) + '_FullName_' + FullName + '.txt' - - return FileName - - - #XZ, 01/29/2009: I modified this function. - #XZ: Note that the type of StrainIds must be number, not string. - def getStrainIds(self, species=None, strains=[]): - StrainIds = [] - for item in strains: - self.cursor.execute('''SELECT Strain.Id FROM Strain, Species WHERE - Strain.Name="%s" and Strain.SpeciesId=Species.Id and Species.name = "%s" ''' % (item, species)) - Id = self.cursor.fetchone()[0] - StrainIds.append(Id) - - return StrainIds - - - #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 input geneid is None, return 0. - if not geneid: - return mouse_geneid - - if species == 'mouse': - mouse_geneid = geneid - elif species == 'rat': - self.cursor.execute( "SELECT mouse FROM GeneIDXRef WHERE rat=%d" % int(geneid) ) - record = self.cursor.fetchone() - if record: - mouse_geneid = record[0] - elif species == 'human': - self.cursor.execute( "SELECT mouse FROM GeneIDXRef WHERE human=%d" % int(geneid) ) - record = self.cursor.fetchone() - if record: - mouse_geneid = record[0] - - 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 - self.cursor.execute(q) - try: - x = self.cursor.fetchone() - if x: return True - else: raise - 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): - - StrainIds = [] - for item in strains: - self.cursor.execute('''SELECT Strain.Id FROM Strain, Species WHERE Strain.Name="%s" and Strain.SpeciesId=Species.Id and Species.name = "%s" ''' % (item, species)) - Id = self.cursor.fetchone()[0] - StrainIds.append('%d' % Id) - - # break it into smaller chunks so we don't overload the MySql server - nnn = len(StrainIds) / 25 - if len(StrainIds) % 25: - nnn += 1 - oridata = [] - - #XZ, 09/24/2008: build one temporary table that only contains the records associated with the input GeneId - tempTable = None - if GeneId and db.type == "ProbeSet": - if method == "3": - tempTable = self.getTempLiteratureTable(species=species, input_species_geneid=GeneId, returnNumber=returnNumber) - - if method == "4" or method == "5": - tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=GeneSymbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=method, returnNumber=returnNumber) - - for step in range(nnn): - temp = [] - StrainIdstep = StrainIds[step*25:min(len(StrainIds), (step+1)*25)] - for item in StrainIdstep: temp.append('T%s.value' % item) - - if db.type == "Publish": - query = "SELECT PublishXRef.Id, " - dataStartPos = 1 - query += string.join(temp,', ') - query += ' FROM (PublishXRef, PublishFreeze)' - #XZ, 03/04/2009: Xiaodong changed Data to PublishData - for item in StrainIdstep: - query += 'left join PublishData as T%s on T%s.Id = PublishXRef.DataId and T%s.StrainId=%s\n' %(item,item,item,item) - query += "WHERE PublishXRef.InbredSetId = PublishFreeze.InbredSetId and PublishFreeze.Name = '%s'" % (db.name, ) - #XZ, 09/20/2008: extract literature correlation value together with gene expression values. - #XZ, 09/20/2008: notice the difference between the code in next block. - elif tempTable: - # we can get a little performance out of selecting our LitCorr here - # but also we need to do this because we are unconcerned with probes that have no geneId associated with them - # as we would not have litCorr data. - - if method == "3": - query = "SELECT %s.Name, %s.value," % (db.type,tempTable) - dataStartPos = 2 - if method == "4" or method == "5": - query = "SELECT %s.Name, %s.Correlation, %s.PValue," % (db.type,tempTable, tempTable) - dataStartPos = 3 - - query += string.join(temp,', ') - query += ' FROM (%s, %sXRef, %sFreeze)' % (db.type, db.type, db.type) - if method == "3": - query += ' LEFT JOIN %s ON %s.GeneId2=ProbeSet.GeneId ' % (tempTable,tempTable) - if method == "4" or method == "5": - query += ' LEFT JOIN %s ON %s.Symbol=ProbeSet.Symbol ' % (tempTable,tempTable) - #XZ, 03/04/2009: Xiaodong changed Data to %sData and changed parameters from %(item,item, db.type,item,item) to %(db.type, item,item, db.type,item,item) - for item in StrainIdstep: - query += 'left join %sData as T%s on T%s.Id = %sXRef.DataId and T%s.StrainId=%s\n' %(db.type, item,item, db.type,item,item) - - if method == "3": - query += "WHERE ProbeSet.GeneId IS NOT NULL AND %s.value IS NOT NULL AND %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s' and %s.Id = %sXRef.%sId order by %s.Id" % (tempTable,db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type) - if method == "4" or method == "5": - query += "WHERE ProbeSet.Symbol IS NOT NULL AND %s.Correlation IS NOT NULL AND %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s' and %s.Id = %sXRef.%sId order by %s.Id" % (tempTable,db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type) - else: - query = "SELECT %s.Name," % db.type - dataStartPos = 1 - query += string.join(temp,', ') - query += ' FROM (%s, %sXRef, %sFreeze)' % (db.type, db.type, db.type) - #XZ, 03/04/2009: Xiaodong changed Data to %sData and changed parameters from %(item,item, db.type,item,item) to %(db.type, item,item, db.type,item,item) - for item in StrainIdstep: - query += 'left join %sData as T%s on T%s.Id = %sXRef.DataId and T%s.StrainId=%s\n' %(db.type, item,item, db.type,item,item) - query += "WHERE %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s' and %s.Id = %sXRef.%sId order by %s.Id" % (db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type) - - self.cursor.execute(query) - results = self.cursor.fetchall() - oridata.append(results) - - datasize = len(oridata[0]) - traits = [] - # put all of the separate data together into a huge list of lists - for j in range(datasize): - traitdata = list(oridata[0][j]) - for i in range(1,nnn): - traitdata += list(oridata[i][j][dataStartPos:]) - - trait = Trait(traitdata[0], traitdata[dataStartPos:]) - - if method == METHOD_LIT: - trait.lit_corr = traitdata[1] - - if method in TISSUE_METHODS: - trait.tissue_corr = traitdata[1] - trait.p_tissue = traitdata[2] - - traits.append(trait) - - if tempTable: - self.cursor.execute( 'DROP TEMPORARY TABLE %s' % tempTable ) - - return traits - - - - - # XZ, 09/20/2008: This function creates TEMPORARY TABLE tmpTableName_2 and return its name. - # XZ, 09/20/2008: It stores top literature correlation values associated with the input geneId. - # XZ, 09/20/2008: Attention: In each row, the input geneId is always in column GeneId1. - #XZ, 12/16/2008: the input geneid can be of mouse, rat or human type - def getTempLiteratureTable(self, species, input_species_geneid, returnNumber): - # according to mysql the TEMPORARY TABLE name should not have to be unique because - # it is only available to the current connection. This program will be invoked via command line, but if it - # were to be invoked over mod_python this could cuase problems. mod_python will keep the connection alive - # in its executing threads ( i think) so there is a potential for the table not being dropped between users. - #XZ, 01/29/2009: To prevent the potential risk, I generate random table names and drop the tables after use them. - - - # the 'input_species_geneid' could be rat or human geneid, need to translate it to mouse geneid - translated_mouse_geneid = self.translateToMouseGeneID (species, input_species_geneid) - - tmpTableName_1 = webqtlUtil.genRandStr(prefix="LITERATURE") - - q1 = 'CREATE TEMPORARY TABLE %s (GeneId1 int(12) unsigned, GeneId2 int(12) unsigned PRIMARY KEY, value double)' % tmpTableName_1 - q2 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId1,GeneId2,value FROM LCorrRamin3 WHERE GeneId1=%s' % (tmpTableName_1, translated_mouse_geneid) - q3 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId2,GeneId1,value FROM LCorrRamin3 WHERE GeneId2=%s AND GeneId1!=%s' % (tmpTableName_1, translated_mouse_geneid,translated_mouse_geneid) - for x in [q1,q2,q3]: self.cursor.execute(x) - - #XZ, 09/23/2008: Just use the top records insteard of using all records - tmpTableName_2 = webqtlUtil.genRandStr(prefix="TOPLITERATURE") - - q1 = 'CREATE TEMPORARY TABLE %s (GeneId1 int(12) unsigned, GeneId2 int(12) unsigned PRIMARY KEY, value double)' % tmpTableName_2 - self.cursor.execute(q1) - q2 = 'SELECT GeneId1, GeneId2, value FROM %s ORDER BY value DESC' % tmpTableName_1 - self.cursor.execute(q2) - result = self.cursor.fetchall() - - counter = 0 #this is to count how many records being inserted into table - for one_row in result: - mouse_geneid1, mouse_geneid2, lit_corr_alue = one_row - - #mouse_geneid1 has been tested before, now should test if mouse_geneid2 has corresponding geneid in other species - translated_species_geneid = 0 - if species == 'mouse': - translated_species_geneid = mouse_geneid2 - elif species == 'rat': - self.cursor.execute( "SELECT rat FROM GeneIDXRef WHERE mouse=%d" % int(mouse_geneid2) ) - record = self.cursor.fetchone() - if record: - translated_species_geneid = record[0] - elif species == 'human': - self.cursor.execute( "SELECT human FROM GeneIDXRef WHERE mouse=%d" % int(mouse_geneid2) ) - record = self.cursor.fetchone() - if record: - translated_species_geneid = record[0] - - if translated_species_geneid: - self.cursor.execute( 'INSERT INTO %s (GeneId1, GeneId2, value) VALUES (%d,%d,%f)' % (tmpTableName_2, int(input_species_geneid),int(translated_species_geneid), float(lit_corr_alue)) ) - counter = counter + 1 - - #pay attention to the number - if (counter > 2*returnNumber): - break - - self.cursor.execute('DROP TEMPORARY TABLE %s' % tmpTableName_1) - - return tmpTableName_2 - - - - #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 getTempTissueCorrTable(self, primaryTraitSymbol="", TissueProbeSetFreezeId=0, method="", returnNumber=0): - - def cmpTissCorrAbsoluteValue(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 - - symbolCorrDict, symbolPvalueDict = self.calculateCorrOfAllTissueTrait(primaryTraitSymbol=primaryTraitSymbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=method) - - symbolCorrList = symbolCorrDict.items() - - symbolCorrList.sort(cmpTissCorrAbsoluteValue) - symbolCorrList = symbolCorrList[0 : 2*returnNumber] - - tmpTableName = webqtlUtil.genRandStr(prefix="TOPTISSUE") - - q1 = 'CREATE TEMPORARY TABLE %s (Symbol varchar(100) PRIMARY KEY, Correlation float, PValue float)' % tmpTableName - self.cursor.execute(q1) - - for one_pair in symbolCorrList: - one_symbol = one_pair[0] - one_corr = one_pair[1] - one_p_value = symbolPvalueDict[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 tmpTableName - - - #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 - pairs it with the TraitID string. Takes as its arguments a formdata instance, and a database instance. - Returns a dictionary of 'TraitID':'LitCorr' for the requested correlation""" - - tempTable = self.getTempLiteratureTable(species=species, input_species_geneid=GeneId, returnNumber=returnNumber) - - query = "SELECT %s.Name, %s.value" % (db.type,tempTable) - query += ' FROM (%s, %sXRef, %sFreeze)' % (db.type, db.type, db.type) - query += ' LEFT JOIN %s ON %s.GeneId2=ProbeSet.GeneId ' % (tempTable,tempTable) - query += "WHERE ProbeSet.GeneId IS NOT NULL AND %s.value IS NOT NULL AND %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s' and %s.Id = %sXRef.%sId order by %s.Id" % (tempTable, db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type) - - self.cursor.execute(query) - results = self.cursor.fetchall() - - litCorrDict = {} - - for entry in results: - traitName,litcorr = entry - litCorrDict[traitName] = litcorr - - self.cursor.execute('DROP TEMPORARY TABLE %s' % tempTable) - - return litCorrDict - - - - #XZ, 01/09/2009: Xiaodong created this function. - def fetchTissueCorrelations(self, db, primaryTraitSymbol="", TissueProbeSetFreezeId=0, method="", returnNumber = 0): - """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""" - - - tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=primaryTraitSymbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=method, returnNumber=returnNumber) - - query = "SELECT ProbeSet.Name, %s.Correlation, %s.PValue" % (tempTable, tempTable) - query += ' FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze)' - query += ' LEFT JOIN %s ON %s.Symbol=ProbeSet.Symbol ' % (tempTable,tempTable) - query += "WHERE ProbeSetFreeze.Name = '%s' and ProbeSetFreeze.Id=ProbeSetXRef.ProbeSetFreezeId and ProbeSet.Id = ProbeSetXRef.ProbeSetId and ProbeSet.Symbol IS NOT NULL AND %s.Correlation IS NOT NULL" % (db.name, tempTable) - - self.cursor.execute(query) - results = self.cursor.fetchall() - - tissueCorrDict = {} - - for entry in results: - traitName, tissueCorr, tissuePValue = entry - tissueCorrDict[traitName] = (tissueCorr, tissuePValue) - - self.cursor.execute('DROP TEMPORARY TABLE %s' % tempTable) - - return tissueCorrDict - - - - #XZ, 01/13/2008 - 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_trait(self, cached, vals): - - if cached: - _log.info("Using the fast method because the file exists") - lit_corrs = {} - tissue_corrs = {} - use_lit = False - if self.method == METHOD_LIT: - lit_corrs = self.fetchLitCorrelations(species=self.species, GeneId=self.gene_id, db=self.db, returnNumber=self.returnNumber) - use_lit = True - - use_tissue_corr = False - if self.method in TISSUE_METHODS: - tissue_corrs = self.fetchTissueCorrelations(db=self.db, primaryTraitSymbol=self.trait_symbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=self.method, returnNumber = self.returnNumber) - use_tissue_corr = True - - DatabaseFileName = self.getFileName( target_db_name=self.target_db_name ) - datasetFile = open(webqtlConfig.TEXTDIR+DatabaseFileName,'r') - - #XZ, 01/08/2009: read the first line - line = datasetFile.readline() - cached_sample_names = webqtlUtil.readLineCSV(line)[1:] - - #XZ, 01/08/2009: This step is critical. It is necessary for this new method. - #XZ: The original function fetchAllDatabaseData uses all strains stored in variable _strains to - #XZ: retrieve the values of each strain from database in real time. - #XZ: The new method uses all strains stored in variable dataset_strains to create a new variable - #XZ: _newvals. _newvals has the same length as dataset_strains. The items in _newvals is in - #XZ: the same order of items in dataset_strains. The value of each item in _newvals is either - #XZ: the value of correspinding strain in _vals or 'None'. - new_vals = [] - for name in cached_sample_names: - if name in self.sample_names: - new_vals.append(float(vals[self.sample_names.index(name)])) - else: - new_vals.append('None') - - nnCorr = len(new_vals) - - #XZ, 01/14/2009: If literature corr or tissue corr is selected, - #XZ: there is no need to use parallel computing. - - traits = [] - data_start = 1 - for line in datasetFile: - raw_trait = webqtlUtil.readLineCSV(line) - trait = Trait.from_csv(raw_trait, data_start) - trait.lit_corr = lit_corrs.get(trait.name) - trait.tissue_corr, trait.p_tissue = tissue_corrs.get(trait.name, (None, None)) - traits.append(trait) - - return traits, new_vals - - else: - _log.info("Using the slow method for correlation") - - _log.info("Fetching from database") - traits = self.fetchAllDatabaseData(species=self.species, GeneId=self.gene_id, GeneSymbol=self.trait_symbol, strains=self.sample_names, db=self.db, method=self.method, returnNumber=self.returnNumber, tissueProbeSetFreezeId= self.tissue_probeset_freeze_id) - _log.info("Done fetching from database") - totalTraits = len(traits) #XZ, 09/18/2008: total trait number - - return traits, vals - - - def do_parallel_correlation(self): - _log.info("Invoking parallel computing") - input_line_list = datasetFile.readlines() - _log.info("Read lines from the file") - all_line_number = len(input_line_list) - - step = 1000 - job_number = math.ceil( float(all_line_number)/step ) - - job_input_lists = [] - - _log.info("Configuring jobs") - - for job_index in range( int(job_number) ): - starti = job_index*step - endi = min((job_index+1)*step, all_line_number) - - one_job_input_list = [] - - for i in range( starti, endi ): - one_job_input_list.append( input_line_list[i] ) - - job_input_lists.append( one_job_input_list ) - - _log.info("Creating pp servers") - - ppservers = () - # Creates jobserver with automatically detected number of workers - job_server = pp.Server(ppservers=ppservers) - - _log.info("Done creating servers") - - jobs = [] - results = [] - - _log.info("Starting parallel computation, submitting jobs") - for one_job_input_list in job_input_lists: #pay attention to modules from outside - jobs.append( job_server.submit(func=compute_corr, args=(nnCorr, _newvals, one_job_input_list, self.method), depfuncs=(), modules=("utility.webqtlUtil",)) ) - _log.info("Done submitting jobs") - - for one_job in jobs: - one_result = one_job() - results.append( one_result ) - - _log.info("Acquiring results") - - for one_result in results: - for one_traitinfo in one_result: - allcorrelations.append( one_traitinfo ) - - _log.info("Appending the results") - - datasetFile.close() - totalTraits = len(allcorrelations) - _log.info("Done correlating using the fast method") - - - def correlate(self, vals): - - correlations = [] - - #XZ: Use the fast method only for probeset dataset, and this dataset must have been created. - #XZ: Otherwise, use original method - _log.info("Entering correlation") - - db_filename = self.getFileName( target_db_name=self.target_db_name ) - - cache_available = db_filename in os.listdir(webqtlConfig.TEXTDIR) - - # If the cache file exists, do a cached correlation for probeset data - if self.db.type == "ProbeSet": -# if self.method in [METHOD_SAMPLE_PEARSON, METHOD_SAMPLE_RANK] and cache_available: -# traits = do_parallel_correlation() -# -# else: - - (traits, vals) = self.get_trait(cache_available, vals) - - for trait in traits: - trait.calculate_correlation(vals, self.method) - - self.record_count = len(traits) #ZS: This isn't a good way to get this value, so I need to change it later - - #XZ, 3/31/2010: Theoretically, we should create one function 'comTissueCorr' - #to compare each trait by their tissue corr p values. - #But because the tissue corr p values are generated by permutation test, - #the top ones always have p value 0. So comparing p values actually does nothing. - #In addition, for the tissue data in our database, the N is always the same. - #So it's safe to compare with tissue corr statistic value. - #That's the same as literature corr. - #if self.method in [METHOD_LIT, METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK] and self.gene_id: - # traits.sort(webqtlUtil.cmpLitCorr) - #else: - #if self.method in TISSUE_METHODS: - # sort(traits, key=lambda A: math.fabs(A.tissue_corr)) - #elif self.method == METHOD_LIT: - # traits.sort(traits, key=lambda A: math.fabs(A.lit_corr)) - #else: - traits = sortTraitCorrelations(traits, self.method) - - # Strip to the top N correlations - traits = traits[:min(self.returnNumber, len(traits))] - - addLiteratureCorr = False - addTissueCorr = False - - trait_list = [] - for trait in traits: - db_trait = webqtlTrait(db=self.db, name=trait.name, cursor=self.cursor) - db_trait.retrieveInfo( QTL='Yes' ) - - db_trait.Name = trait.name - db_trait.corr = trait.correlation - db_trait.nOverlap = trait.overlap - db_trait.corrPValue = trait.p_value - - # NL, 07/19/2010 - # js function changed, add a new parameter rankOrder for js function 'showTissueCorrPlot' - db_trait.RANK_ORDER = self.RANK_ORDERS[self.method] - - #XZ, 26/09/2008: Method is 4 or 5. Have fetched tissue corr, but no literature correlation yet. - if self.method in TISSUE_METHODS: - db_trait.tissueCorr = trait.tissue_corr - db_trait.tissuePValue = trait.p_tissue - addTissueCorr = True - - - #XZ, 26/09/2008: Method is 3, Have fetched literature corr, but no tissue corr yet. - elif self.method == METHOD_LIT: - db_trait.LCorr = trait.lit_corr - db_trait.mouse_geneid = self.translateToMouseGeneID(self.species, db_trait.geneid) - addLiteratureCorr = True - - #XZ, 26/09/2008: Method is 1 or 2. Have NOT fetched literature corr and tissue corr yet. - # Phenotype data will not have geneid, and neither will some probes - # we need to handle this because we will get an attribute error - else: - if self.input_trait_mouse_gene_id and self.db.type=="ProbeSet": - addLiteratureCorr = True - if self.trait_symbol and self.db.type=="ProbeSet": - addTissueCorr = True - - trait_list.append(db_trait) - - if addLiteratureCorr: - trait_list = self.getLiteratureCorrelationByList(self.input_trait_mouse_gene_id, - self.species, trait_list) - if addTissueCorr: - trait_list = self.getTissueCorrelationByList( - primaryTraitSymbol = self.trait_symbol, - traitList = trait_list, - TissueProbeSetFreezeId = TISSUE_MOUSE_DB, - method=self.method) - - return trait_list - - - def calculateCorrOfAllTissueTrait(self, primaryTraitSymbol=None, TissueProbeSetFreezeId=None, method=None): - - symbolCorrDict = {} - symbolPvalueDict = {} - - primaryTraitSymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[primaryTraitSymbol], TissueProbeSetFreezeId=TISSUE_MOUSE_DB) - primaryTraitValue = primaryTraitSymbolValueDict.values()[0] - - SymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[], TissueProbeSetFreezeId=TISSUE_MOUSE_DB) - - if method in ["2","5"]: - symbolCorrDict, symbolPvalueDict = correlationFunction.batchCalTissueCorr(primaryTraitValue,SymbolValueDict,method='spearman') - else: - symbolCorrDict, symbolPvalueDict = correlationFunction.batchCalTissueCorr(primaryTraitValue,SymbolValueDict) - - - return (symbolCorrDict, symbolPvalueDict) - - - - #XZ, 10/13/2010 - def getTissueCorrelationByList(self, primaryTraitSymbol=None, traitList=None, TissueProbeSetFreezeId=None, method=None): - - primaryTraitSymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[primaryTraitSymbol], TissueProbeSetFreezeId=TISSUE_MOUSE_DB) - - if primaryTraitSymbol.lower() in primaryTraitSymbolValueDict: - primaryTraitValue = primaryTraitSymbolValueDict[primaryTraitSymbol.lower()] - - geneSymbolList = [] - - for thisTrait in traitList: - if hasattr(thisTrait, 'symbol'): - geneSymbolList.append(thisTrait.symbol) - - SymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=geneSymbolList, TissueProbeSetFreezeId=TISSUE_MOUSE_DB) - - for thisTrait in traitList: - if hasattr(thisTrait, 'symbol') and thisTrait.symbol and thisTrait.symbol.lower() in SymbolValueDict: - oneTraitValue = SymbolValueDict[thisTrait.symbol.lower()] - if method in ["2","5"]: - result = correlationFunction.calZeroOrderCorrForTiss( primaryTraitValue, oneTraitValue, method='spearman' ) - else: - result = correlationFunction.calZeroOrderCorrForTiss( primaryTraitValue, oneTraitValue) - thisTrait.tissueCorr = result[0] - thisTrait.tissuePValue = result[2] - else: - thisTrait.tissueCorr = None - thisTrait.tissuePValue = None - else: - for thisTrait in traitList: - thisTrait.tissueCorr = None - thisTrait.tissuePValue = None - - return traitList - - - def getTopInfo(self, myTrait=None, method=None, db=None, target_db_name=None, returnNumber=None, methodDict=None, totalTraits=None, identification=None ): - - if myTrait: - if method in ["1","2"]: #genetic correlation - info = HT.Paragraph("Values of Record %s in the " % myTrait.getGivenName(), HT.Href(text=myTrait.db.fullname,url=webqtlConfig.INFOPAGEHREF % myTrait.db.name,target="_blank", Class="fwn"), - " database were compared to all %d records in the " % self.record_count, HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank", Class="fwn"), - ' database. The top %d correlations ranked by the %s are displayed.' % (returnNumber,methodDict[method]), - ' You can resort this list using the small arrowheads in the top row.') - else: - #myTrait.retrieveInfo()#need to know geneid and symbol - if method == "3":#literature correlation - searchDBName = "Literature Correlation" - searchDBLink = "/correlationAnnotation.html#literatureCorr" - else: #tissue correlation - searchDBName = "Tissue Correlation" - searchDBLink = "/correlationAnnotation.html#tissueCorr" - info = HT.Paragraph("Your input record %s in the " % myTrait.getGivenName(), HT.Href(text=myTrait.db.fullname,url=webqtlConfig.INFOPAGEHREF % myTrait.db.name,target="_blank", Class="fwn"), - " database corresponds to ", - HT.Href(text='gene Id %s, and gene symbol %s' % (myTrait.geneid, myTrait.symbol), target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % myTrait.geneid, Class="fs12 fwn"), - '. GN ranked all genes in the ', HT.Href(text=searchDBName,url=searchDBLink,target="_blank", Class="fwn"),' database by the %s.' % methodDict[method], - ' The top %d probes or probesets in the ' % returnNumber, HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank", Class="fwn"), - ' database corresponding to the top genes ranked by the %s are displayed.' %( methodDict[method]), - ' You can resort this list using the small arrowheads in the top row.' ) - - elif identification: - info = HT.Paragraph('Values of %s were compared to all %d traits in ' % (identification, self.record_count), - HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank",Class="fwn"), - ' database. The TOP %d correlations ranked by the %s are displayed.' % (returnNumber,methodDict[method]), - ' You can resort this list using the small arrowheads in the top row.') - - else: - info = HT.Paragraph('Trait values were compared to all values in ', - HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank",Class="fwn"), - ' database. The TOP %d correlations ranked by the %s are displayed.' % (returnNumber,methodDict[method]), - ' You can resort this list using the small arrowheads in the top row.') - - if db.type=="Geno": - info.append(HT.BR(),HT.BR(),'Clicking on the Locus will open the genotypes data for that locus. Click on the correlation to see a scatter plot of the trait data.') - elif db.type=="Publish": - info.append(HT.BR(),HT.BR(),'Clicking on the record ID will open the published phenotype data for that publication. Click on the correlation to see a scatter plot of the trait data. ') - elif db.type=="ProbeSet": - info.append(HT.BR(),'Click the correlation values to generate scatter plots. Select the Record ID to open the Trait Data and Analysis form. Select the symbol to open NCBI Entrez.') - else: - pass - - - return info - - - 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 getTableHeaderForPublish(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", nowrap="on"), sort=0), - THCell(HT.TD('Record',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Record ID", idx=1), - THCell(HT.TD('Phenotype', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Phenotype", idx=2), - THCell(HT.TD('Authors', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Authors", idx=3), - THCell(HT.TD('Year', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Year", idx=4), - THCell(HT.TD('Max',HT.BR(), 'LRS', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS", idx=5), - THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS Location", idx=6), - 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=7), - THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=8), - 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=9)]] - - for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "Pubmed Id", "Max LRS", "Max LRS 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", nowrap="on"), sort=0), - THCell(HT.TD('Record',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Record ID", idx=1), - THCell(HT.TD('Phenotype', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Phenotype", idx=2), - THCell(HT.TD('Authors', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Authors", idx=3), - THCell(HT.TD('Year', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Year", idx=4), - THCell(HT.TD('Max',HT.BR(), 'LRS', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS", idx=5), - THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS Location", idx=6), - 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=7), - THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=8), - 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=9)]] - - for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "Pubmed Id", "Max LRS", "Max LRS 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 getTableBodyForPublish(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None, species=''): - - 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"), nowrap="yes",align="center", Class="fs12 fwn b1 c222"),str(thisTrait.name), thisTrait.name)) - - PhenotypeString = thisTrait.post_publication_description - if thisTrait.confidential: - if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users): - PhenotypeString = thisTrait.pre_publication_description - - tr.append(TDCell(HT.TD(PhenotypeString, Class="fs12 fwn b1 c222"), PhenotypeString, PhenotypeString.upper())) - - tr.append(TDCell(HT.TD(thisTrait.authors, Class="fs12 fwn b1 c222 fsI"),thisTrait.authors, thisTrait.authors.strip().upper())) - - try: - PubMedLinkText = myear = repr = int(thisTrait.year) - except: - PubMedLinkText = repr = "--" - myear = 0 - if thisTrait.pubmed_id: - PubMedLink = HT.Href(text= repr,url= webqtlConfig.PUBMEDLINK_URL % thisTrait.pubmed_id,target='_blank', Class="fs12 fwn") - else: - PubMedLink = repr - - tr.append(TDCell(HT.TD(PubMedLink, Class="fs12 fwn b1 c222", align='center'), repr, myear)) - - #LRS and its location - LRS_score_repr = '--' - LRS_score_value = 0 - LRS_location_repr = '--' - LRS_location_value = 1000000 - LRS_flag = 1 - - #Max LRS and its Locus location - if thisTrait.lrs and thisTrait.locus: - self.cursor.execute(""" - select Geno.Chr, Geno.Mb from Geno, Species - where Species.Name = '%s' and - Geno.Name = '%s' and - Geno.SpeciesId = Species.Id - """ % (species, thisTrait.locus)) - result = self.cursor.fetchone() - - if result: - if result[0] and result[1]: - LRS_Chr = result[0] - LRS_Mb = result[1] - - #XZ: LRS_location_value is used for sorting - try: - LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb) - except: - if LRS_Chr.upper() == 'X': - LRS_location_value = 20*1000 + float(LRS_Mb) - else: - LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb) - - - LRS_score_repr = '%3.1f' % thisTrait.lrs - LRS_score_value = thisTrait.lrs - LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb) ) - LRS_flag = 0 - - #tr.append(TDCell(HT.TD(HT.Href(text=LRS_score_repr,url="javascript:showIntervalMapping('%s', '%s : %s')" % (formName, thisTrait.db.shortname, thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn ffl b1 c222", align='right', nowrap="on"),LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222"), LRS_location_repr, LRS_location_value)) - - if LRS_flag: - tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222"), LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222"), LRS_location_repr, LRS_location_value)) - - repr = '%3.4f' % thisTrait.corr - tr.append(TDCell(HT.TD(HT.Href(text=repr,url="javascript:showCorrPlot('%s', '%s')" % (formName,thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn b1 c222", align='right',nowrap="on"), 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, PhenotypeString, thisTrait.authors, thisTrait.year, thisTrait.pubmed_id, LRS_score_repr, LRS_location_repr, thisTrait.corr, thisTrait.nOverlap, thisTrait.corrPValue]): - worksheet.write([newrow, ncol], item) - newrow += 1 - - return tblobj_body, worksheet, corrScript - - - def getTableHeaderForProbeSet(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",nowrap='ON'), 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('Gene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Gene ID", idx=2), - THCell(HT.TD('Homologene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Homologene ID", idx=3), - THCell(HT.TD('Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Symbol", idx=4), - THCell(HT.TD('Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Description", idx=5), - THCell(HT.TD('Location',HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Location (Chr: Mb)", idx=6), - THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mean Expr", idx=7), - THCell(HT.TD('Max',HT.BR(),'LRS',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS", idx=8), - THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS Location (Chr: Mb)", idx=9), - 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=10), - THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=11), - 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=12), - THCell(HT.TD(HT.Href( - text = HT.Span('Lit',HT.BR(), 'Corr', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#literatureCorr"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Lit Corr", idx=13), - #XZ, 09/22/2008: tissue correlation - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'r', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue r", idx=14), - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'p(r)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_p_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue p(r)", idx=15)]] - - for ncol, item in enumerate(['Record', 'Gene ID', 'Homologene ID', 'Symbol', 'Description', 'Location (Chr: Mb)', 'Mean Expr', 'Max LRS', 'Max LRS Location (Chr: Mb)', 'Sample r', 'N Cases', 'Sample p(r)', 'Lit Corr', 'Tissue r', 'Tissue 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",nowrap='ON'), 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('Gene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Gene ID", idx=2), - THCell(HT.TD('Homologene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Homologene ID", idx=3), - THCell(HT.TD('Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Symbol", idx=4), - THCell(HT.TD('Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Description", idx=5), - THCell(HT.TD('Location',HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Location (Chr: Mb)", idx=6), - THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mean Expr", idx=7), - THCell(HT.TD('Max',HT.BR(),'LRS',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS", idx=8), - THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS Location (Chr: Mb)", idx=9), - 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=10), - THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=11), - 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=12), - THCell(HT.TD(HT.Href( - text = HT.Span('Lit',HT.BR(), 'Corr', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#literatureCorr"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Lit Corr", idx=13), - #XZ, 09/22/2008: tissue correlation - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'rho', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue rho", idx=14), - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'p(rho)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_p_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue p(rho)", idx=15)]] - - for ncol, item in enumerate(['Record ID', 'Gene ID', 'Homologene ID', 'Symbol', 'Description', 'Location (Chr: Mb)', 'Mean Expr', 'Max LRS', 'Max LRS Location (Chr: Mb)', 'Sample rho', 'N Cases', 'Sample p(rho)', 'Lit Corr', 'Tissue rho', 'Tissue p(rho)']): - worksheet.write([newrow, ncol], item, headingStyle) - worksheet.set_column([ncol, ncol], 2*len(item)) - - return tblobj_header, worksheet - - - def getTableBodyForProbeSet(self, traitList=[], primaryTrait=None, formName=None, worksheet=None, newrow=None, corrScript=None, species=''): - - tblobj_body = [] - - for thisTrait in traitList: - - if thisTrait.symbol: - pass - else: - thisTrait.symbol = "--" - - if thisTrait.geneid: - symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % thisTrait.geneid, Class="fs12 fwn") - else: - symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gene&term=%s" % thisTrait.symbol, Class="fs12 fwn") - - tr = [] - - trId = str(thisTrait) - - corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.corr)) - - #XZ, 12/08/2008: checkbox - 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)) - - #XZ, 12/08/2008: probeset name - tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName,thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn b1 c222"), thisTrait.name, thisTrait.name.upper())) - - #XZ, 12/08/2008: gene id - if thisTrait.geneid: - tr.append(TDCell(None, thisTrait.geneid, val=999)) - else: - tr.append(TDCell(None, thisTrait.geneid, val=999)) - - #XZ, 12/08/2008: homologene id - if thisTrait.homologeneid: - tr.append(TDCell("", thisTrait.homologeneid, val=999)) - else: - tr.append(TDCell("", thisTrait.homologeneid, val=999)) - - #XZ, 12/08/2008: gene symbol - tr.append(TDCell(HT.TD(symbolurl, Class="fs12 fwn b1 c222 fsI"),thisTrait.symbol, thisTrait.symbol.upper())) - - #XZ, 12/08/2008: description - #XZ, 06/05/2009: Rob asked to add probe target description - description_string = str(thisTrait.description).strip() - target_string = str(thisTrait.probe_target_description).strip() - - description_display = '' - - if len(description_string) > 1 and description_string != 'None': - description_display = description_string - else: - description_display = thisTrait.symbol - - if len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None': - description_display = description_display + '; ' + target_string.strip() - - tr.append(TDCell(HT.TD(description_display, Class="fs12 fwn b1 c222"), description_display, description_display)) - - #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)) - - """ - #XZ, 12/08/2008: chromosome number - #XZ, 12/10/2008: use Mbvalue to sort chromosome - tr.append(TDCell( HT.TD(thisTrait.chr, Class="fs12 fwn b1 c222", align='right'), thisTrait.chr, Mbvalue) ) - - #XZ, 12/08/2008: Rob wants 6 digit precision, and we have to deal with that the mb could be None - if not thisTrait.mb: - tr.append(TDCell(HT.TD(thisTrait.mb, Class="fs12 fwn b1 c222",align='right'), thisTrait.mb, Mbvalue)) - else: - tr.append(TDCell(HT.TD('%.6f' % thisTrait.mb, Class="fs12 fwn b1 c222", align='right'), thisTrait.mb, Mbvalue)) - """ - - - - #XZ, 01/12/08: This SQL query is much faster. - self.cursor.execute(""" - select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet - where ProbeSetXRef.ProbeSetFreezeId = %d and - ProbeSet.Id = ProbeSetXRef.ProbeSetId and - ProbeSet.Name = '%s' - """ % (thisTrait.db.id, thisTrait.name)) - result = self.cursor.fetchone() - if result: - if result[0]: - mean = result[0] - else: - mean=0 - else: - mean = 0 - - #XZ, 06/05/2009: It is neccessary to turn on nowrap - repr = "%2.3f" % mean - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'),repr, mean)) - - #LRS and its location - LRS_score_repr = '--' - LRS_score_value = 0 - LRS_location_repr = '--' - LRS_location_value = 1000000 - LRS_flag = 1 - - #Max LRS and its Locus location - if thisTrait.lrs and thisTrait.locus: - self.cursor.execute(""" - select Geno.Chr, Geno.Mb from Geno, Species - where Species.Name = '%s' and - Geno.Name = '%s' and - Geno.SpeciesId = Species.Id - """ % (species, thisTrait.locus)) - result = self.cursor.fetchone() - - if result: - if result[0] and result[1]: - LRS_Chr = result[0] - LRS_Mb = result[1] - - #XZ: LRS_location_value is used for sorting - try: - LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb) - except: - if LRS_Chr.upper() == 'X': - LRS_location_value = 20*1000 + float(LRS_Mb) - else: - LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb) - - - LRS_score_repr = '%3.1f' % thisTrait.lrs - LRS_score_value = thisTrait.lrs - LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb) ) - LRS_flag = 0 - - #tr.append(TDCell(HT.TD(HT.Href(text=LRS_score_repr,url="javascript:showIntervalMapping('%s', '%s : %s')" % (formName, thisTrait.db.shortname, thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn ffl b1 c222", align='right', nowrap="on"),LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222", nowrap="on"), LRS_location_repr, LRS_location_value)) - - if LRS_flag: - tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222"), LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222"), LRS_location_repr, LRS_location_value)) - - - #XZ, 12/08/2008: generic correlation - 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", align='right'),repr,abs(thisTrait.corr))) - - #XZ, 12/08/2008: number of overlaped cases - repr = '%d' % thisTrait.nOverlap - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.nOverlap)) - - #XZ, 12/08/2008: p value of genetic correlation - repr = webqtlUtil.SciFloat(thisTrait.corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue)) - - #XZ, 12/08/2008: literature correlation - LCorr = 0.0 - LCorrStr = "--" - if hasattr(thisTrait, 'LCorr') and thisTrait.LCorr: - LCorr = thisTrait.LCorr - LCorrStr = "%2.3f" % thisTrait.LCorr - tr.append(TDCell(HT.TD(LCorrStr, Class="fs12 fwn b1 c222", align='right'), LCorrStr, abs(LCorr))) - - #XZ, 09/22/2008: tissue correlation. - TCorr = 0.0 - TCorrStr = "--" - #XZ, 11/20/2008: need to pass two geneids: input_trait_mouse_geneid and thisTrait.mouse_geneid - if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissueCorr: - TCorr = thisTrait.tissueCorr - TCorrStr = "%2.3f" % thisTrait.tissueCorr - # NL, 07/19/2010: add a new parameter rankOrder for js function 'showTissueCorrPlot' - rankOrder = self.RANK_ORDERS[self.method] - TCorrPlotURL = "javascript:showTissueCorrPlot('%s','%s','%s',%d)" %(formName, primaryTrait.symbol, thisTrait.symbol,rankOrder) - tr.append(TDCell(HT.TD(HT.Href(text=TCorrStr, url=TCorrPlotURL, Class="fs12 fwn ff1"), Class="fs12 fwn ff1 b1 c222", align='right'), TCorrStr, abs(TCorr))) - else: - tr.append(TDCell(HT.TD(TCorrStr, Class="fs12 fwn b1 c222", align='right'), TCorrStr, abs(TCorr))) - - #XZ, 12/08/2008: p value of tissue correlation - TPValue = 1.0 - TPValueStr = "--" - if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissuePValue: #XZ, 09/22/2008: thisTrait.tissuePValue can't be used here because it could be 0 - TPValue = thisTrait.tissuePValue - TPValueStr = "%2.3f" % thisTrait.tissuePValue - tr.append(TDCell(HT.TD(TPValueStr, Class="fs12 fwn b1 c222", align='right'), TPValueStr, TPValue)) - - tblobj_body.append(tr) - - for ncol, item in enumerate([thisTrait.name, thisTrait.geneid, thisTrait.homologeneid, thisTrait.symbol, thisTrait.description, trait_location_repr, mean, LRS_score_repr, LRS_location_repr, thisTrait.corr, thisTrait.nOverlap, thisTrait.corrPValue, LCorr, TCorr, TPValue]): - worksheet.write([newrow, ncol], item) - - newrow += 1 - - return tblobj_body, worksheet, corrScript diff --git a/web/webqtl/correlation/PartialCorrDBPage.py b/web/webqtl/correlation/PartialCorrDBPage.py deleted file mode 100755 index ecd1e623..00000000 --- a/web/webqtl/correlation/PartialCorrDBPage.py +++ /dev/null @@ -1,1359 +0,0 @@ -import string -import cPickle -import os -import pyXLWriter as xl - -from htmlgen import HTMLgen2 as HT - -from base import webqtlConfig -#import webqtlData -from utility.THCell import THCell -from utility.TDCell import TDCell -from base.webqtlTrait import webqtlTrait -from base.webqtlDataset import webqtlDataset -from base.templatePage import templatePage -from utility import webqtlUtil -from CorrelationPage import CorrelationPage -import correlationFunction -from dbFunction import webqtlDatabaseFunction - - -######################################### -# Partial Correlation Dataset Page -######################################### - - -class PartialCorrDBPage(CorrelationPage): - - corrMinInformative = 4 - - def __init__(self, fd): - - - templatePage.__init__(self, fd) - - if not self.openMysql(): - return - - - primaryTraitString = fd.formdata.getvalue('primaryTrait') - primaryTrait = (webqtlTrait(fullname=primaryTraitString, cursor=self.cursor)) - - controlTraitsString = fd.formdata.getvalue('controlTraits') - controlTraitsList = list(string.split(controlTraitsString,',')) - controlTraits = [] - for item in controlTraitsList: - controlTraits.append(webqtlTrait(fullname=item, cursor=self.cursor)) - - #XZ, 3/16/2010: variable RISet must be pass by the form - RISet = fd.RISet - #XZ, 12/12/2008: get species infomation - species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, RISet=RISet) - - #XZ, 09/18/2008: get all information about the user selected database. - self.target_db_name = fd.formdata.getvalue('database2') - - try: - self.db = webqtlDataset(self.target_db_name, self.cursor) - except: - heading = "Partial Correlation Table" - detail = ["The database you just requested has not been established yet."] - self.error(heading=heading,detail=detail) - return - - #XZ, 09/18/2008: check if user has the authority to get access to the database. - if self.db.type == 'ProbeSet': - self.cursor.execute('SELECT Id, Name, FullName, confidentiality, AuthorisedUsers FROM ProbeSetFreeze WHERE Name = "%s"' % self.target_db_name) - indId, indName, indFullName, confidential, AuthorisedUsers = self.cursor.fetchall()[0] - - if confidential == 1: - access_to_confidential_dataset = 0 - - #for the dataset that confidentiality is 1 - #1. 'admin' and 'root' can see all of the dataset - #2. 'user' can see the dataset that AuthorisedUsers contains his id(stored in the Id field of User table) - if webqtlConfig.USERDICT[self.privilege] > webqtlConfig.USERDICT['user']: - access_to_confidential_dataset = 1 - else: - AuthorisedUsersList=AuthorisedUsers.split(',') - if AuthorisedUsersList.__contains__(self.userName): - access_to_confidential_dataset = 1 - - if not access_to_confidential_dataset: - #Error, Confidential Database - heading = "Partial Correlation Table" - detail = ["The %s database you selected is not open to the public at this time, please go back and select another database." % indFullName] - self.error(heading=heading,detail=detail,error="Confidential Database") - return - - - primaryTrait.retrieveData() - _primarystrains, _primaryvals, _primaryvars = primaryTrait.exportInformative() - - controlTraitNames = fd.formdata.getvalue('controlTraits') - _controlstrains,_controlvals,_controlvars,_controlNs = correlationFunction.controlStrains(controlTraitNames,_primarystrains) - - ## If the strains for which each of the control traits and the primary trait have values are not identical, - ## we must remove from the calculation all vlaues for strains that are not present in each. Without doing this, - ## undesirable biases would be introduced. - - common_primary_control_strains = _primarystrains #keep _primarystrains - fixed_primary_vals = _primaryvals #keep _primaryvals - fixed_control_vals = _controlvals - - allsame = True - ##allsame is boolean for whether or not primary and control trait have values for the same strains - for i in _controlstrains: - if _primarystrains != i: - allsame=False - break - - if not allsame: - common_primary_control_strains, fixed_primary_vals, fixed_control_vals, _vars, _controlvars = correlationFunction.fixStrains(_primarystrains,_controlstrains,_primaryvals,_controlvals,_primaryvars,_controlvars) - - N = len(common_primary_control_strains) - if N < self.corrMinInformative: - heading = "Partial Correlation Table" - detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, RISet)] - self.error(heading=heading,detail=detail) - return - - #XZ: We should check the value of control trait and primary trait here. - nameOfIdenticalTraits = correlationFunction.findIdenticalTraits ( fixed_primary_vals, primaryTraitString, fixed_control_vals, controlTraitsList ) - if nameOfIdenticalTraits: - heading = "Partial Correlation Table" - detail = ['%s and %s have same values for the %s strains that will be used to calculate partial correlation (common for all primary and control traits). In such case, partial correlation can NOT be calculated. Please re-select your traits.' % (nameOfIdenticalTraits[0], nameOfIdenticalTraits[1], len(fixed_primary_vals))] - self.error(heading=heading,detail=detail) - return - - - #XZ, 09/28/2008: if user select "1", then display 1, 3 and 4. - #XZ, 09/28/2008: if user select "2", then display 2, 3 and 5. - #XZ, 09/28/2008: if user select "3", then display 1, 3 and 4. - #XZ, 09/28/2008: if user select "4", then display 1, 3 and 4. - #XZ, 09/28/2008: if user select "5", then display 2, 3 and 5. - methodDict = {"1":"Genetic Correlation (Pearson's r)","2":"Genetic Correlation (Spearman's rho)","3":"SGO Literature Correlation","4":"Tissue Correlation (Pearson's r)", "5":"Tissue Correlation (Spearman's rho)"} - self.method = fd.formdata.getvalue('method') - if self.method not in ("1","2","3","4","5"): - self.method = "1" - - self.returnNumber = int(fd.formdata.getvalue('criteria')) - - myTrait = primaryTrait - myTrait.retrieveInfo() - - # We will not get Literature Correlations if there is no GeneId because there is nothing to look against - try: - input_trait_GeneId = myTrait.geneid - except: - input_trait_GeneId = None - - # We will not get Tissue Correlations if there is no gene symbol because there is nothing to look against - try: - input_trait_symbol = myTrait.symbol - except: - input_trait_symbol = None - - - #XZ, 12/12/2008: if the species is rat or human, translate the geneid to mouse geneid - input_trait_mouse_geneid = self.translateToMouseGeneID(species, input_trait_GeneId) - - #XZ: As of Nov/13/2010, this dataset is 'UTHSC Illumina V6.2 RankInv B6 D2 average CNS GI average (May 08)' - TissueProbeSetFreezeId = 1 - - - #XZ, 09/22/2008: If we need search by GeneId, - #XZ, 09/22/2008: we have to check if this GeneId is in the literature or tissue correlation table. - #XZ, 10/15/2008: We also to check if the selected database is probeset type. - if self.method == "3" or self.method == "4" or self.method == "5": - if self.db.type != "ProbeSet": - self.error(heading="Wrong correlation type",detail="It is not possible to compute the %s between your trait and data in this %s database. Please try again after selecting another type of correlation." % (methodDict[self.method],self.db.name),error="Correlation Type Error") - return - - """ - if not input_trait_GeneId: - self.error(heading="No Associated GeneId",detail="This trait has no associated GeneId, so we are not able to show any literature or tissue related information.",error="No GeneId Error") - return - """ - - #XZ: We have checked geneid did exist - - if self.method == "3": - if not input_trait_GeneId or not self.checkForLitInfo(input_trait_mouse_geneid): - self.error(heading="No Literature Info",detail="This gene does not have any associated Literature Information.",error="Literature Correlation Error") - return - - if self.method == "4" or self.method == "5": - if not input_trait_symbol: - self.error(heading="No Tissue Correlation Information",detail="This gene does not have any associated Tissue Correlation Information.",error="Tissue Correlation Error") - return - - if not self.checkSymbolForTissueCorr(TissueProbeSetFreezeId, myTrait.symbol): - self.error(heading="No Tissue Correlation Information",detail="This gene does not have any associated Tissue Correlation Information.",error="Tissue Correlation Error") - return - -####################################################################################################################################### - - nnCorr = len(fixed_primary_vals) - - #XZ: Use the fast method only for probeset dataset, and this dataset must have been created. - #XZ: Otherwise, use original method - - useFastMethod = False - - if self.db.type == "ProbeSet": - DatabaseFileName = self.getFileName( target_db_name=self.target_db_name ) - DirectoryList = os.listdir(webqtlConfig.TEXTDIR) # List of existing text files. Used to check if a text file already exists - if DatabaseFileName in DirectoryList: - useFastMethod = True - - if useFastMethod: - totalTraits, allcorrelations = self.getPartialCorrelationsFast(common_primary_control_strains , fixed_primary_vals, fixed_control_vals, nnCorr, DatabaseFileName, species, input_trait_GeneId, input_trait_symbol, TissueProbeSetFreezeId) - - if totalTraits == 0: - useFastMethod = False - - #XZ, 01/08/2009: use the original method to retrieve from database and compute. - if not useFastMethod: - totalTraits, allcorrelations = self.getPartialCorrelationsNormal(common_primary_control_strains, fixed_primary_vals, fixed_control_vals, nnCorr, species, input_trait_GeneId, input_trait_symbol,TissueProbeSetFreezeId) - -############################################################# - - if self.method == "3" and input_trait_GeneId: - allcorrelations.sort(self.cmpLitCorr) - elif self.method in ["4","5"] and input_trait_GeneId: - allcorrelations.sort(self.cmpLitCorr) - else: - allcorrelations.sort(self.cmpPartialCorrPValue) - - #XZ, 09/20/2008: we only need the top ones. - self.returnNumber = min(self.returnNumber,len(allcorrelations)) - allcorrelations = allcorrelations[:self.returnNumber] - - addLiteratureCorr = False - addTissueCorr = False - - traitList = [] - for item in allcorrelations: - thisTrait = webqtlTrait(db=self.db, name=item[0], cursor=self.cursor) - thisTrait.retrieveInfo() - - thisTrait.Name = item[0] - thisTrait.NOverlap = item[1] - - thisTrait.partial_corr = item[2] - thisTrait.partial_corrPValue = item[3] - - thisTrait.corr = item[4] - thisTrait.corrPValue = item[5] - # NL, 07/19/2010 - # js function changed, add a new parameter rankOrder for js function 'showTissueCorrPlot' - rankOrder = 0; - if self.method in ["2","5"]: - rankOrder = 1; - thisTrait.rankOrder = rankOrder - - #XZ, 26/09/2008: Method is 4 or 5. Have fetched tissue corr, but no literature correlation yet. - if len(item) == 8: - thisTrait.tissueCorr = item[6] - thisTrait.tissuePValue = item[7] - addLiteratureCorr = True - - #XZ, 26/09/2008: Method is 3, Have fetched literature corr, but no tissue corr yet. - elif len(item) == 7: - thisTrait.LCorr = item[6] - thisTrait.mouse_geneid = self.translateToMouseGeneID(species, thisTrait.geneid) - addTissueCorr = True - - #XZ, 26/09/2008: Method is 1 or 2. Have NOT fetched literature corr and tissue corr yet. - # Phenotype data will not have geneid, and neither will some probes - # we need to handle this because we will get an attribute error - else: - if input_trait_mouse_geneid and self.db.type=="ProbeSet": - addLiteratureCorr = True - if input_trait_symbol and self.db.type=="ProbeSet": - addTissueCorr = True - - traitList.append(thisTrait) - - if addLiteratureCorr: - traitList = self.getLiteratureCorrelationByList(input_trait_mouse_geneid, species, traitList) - if addTissueCorr: - traitList = self.getTissueCorrelationByList(primaryTraitSymbol=input_trait_symbol, traitList=traitList,TissueProbeSetFreezeId=TissueProbeSetFreezeId, method=self.method) - -######################################################## - - TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee') - - mainfmName = webqtlUtil.genRandStr("fm_") - form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden')) - hddn = {'FormID':'showDatabase', 'ProbeSetID':'_','database':self.target_db_name, 'CellID':'_', 'RISet':RISet, 'identification':fd.identification} - - if myTrait: - hddn['fullname']=str(myTrait) - - - for key in hddn.keys(): - form.append(HT.Input(name=key, value=hddn[key], type='hidden')) - - #XZ, 11/21/2008: add two parameters to form - form.append(HT.Input(name="X_geneSymbol", value="", type='hidden')) - form.append(HT.Input(name="Y_geneSymbol", value="", type='hidden')) - - #XZ, 3/11/2010: add one parameter to record if the method is rank order. - - form.append(HT.Input(name="rankOrder", value="%s" % rankOrder, type='hidden')) - - form.append(HT.Input(name="TissueProbeSetFreezeId", value="%s" % TissueProbeSetFreezeId, type='hidden')) - - - #################################### - # generate the info on top of page # - #################################### - - info_form = self.getFormForPrimaryAndControlTraits (primaryTrait, controlTraits) - info = self.getTopInfo(myTrait=myTrait, method=self.method, db=self.db, target_db_name=self.target_db_name, returnNumber=self.returnNumber, methodDict=methodDict, totalTraits=totalTraits, identification=fd.identification ) - - ############## - # Excel file # - ############## - filename= webqtlUtil.genRandStr("Corr_") - xlsUrl = HT.Input(type='button', value = 'Download Table', onClick= "location.href='/tmp/%s.xls'" % filename, Class='button') - # Create a new Excel workbook - workbook = xl.Writer('%s.xls' % (webqtlConfig.TMPDIR+filename)) - headingStyle = workbook.add_format(align = 'center', bold = 1, border = 1, size=13, fg_color = 0x1E, color="white") - - #XZ, 3/18/2010: pay attention to the line number of header in this file. As of today, there are 7 lines. - worksheet = self.createExcelFileWithTitleAndFooter(workbook=workbook, identification=fd.identification, db=self.db, returnNumber=self.returnNumber) - - newrow = 7 - - - -##################################################################### - - mintmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'showIntMap');" % mainfmName) - mintmap_img = HT.Image("/images/multiple_interval_mapping1_final.jpg", name='mintmap', alt="Multiple Interval Mapping", title="Multiple Interval Mapping", style="border:none;") - mintmap.append(mintmap_img) - mcorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'compCorr');" % mainfmName) - mcorr_img = HT.Image("/images/compare_correlates2_final.jpg", alt="Compare Correlates", title="Compare Correlates", style="border:none;") - mcorr.append(mcorr_img) - cormatrix = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'corMatrix');" % mainfmName) - cormatrix_img = HT.Image("/images/correlation_matrix1_final.jpg", alt="Correlation Matrix and PCA", title="Correlation Matrix and PCA", style="border:none;") - cormatrix.append(cormatrix_img) - networkGraph = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'networkGraph');" % mainfmName) - networkGraph_img = HT.Image("/images/network_graph1_final.jpg", name='mintmap', alt="Network Graphs", title="Network Graphs", style="border:none;") - networkGraph.append(networkGraph_img) - heatmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'heatmap');" % mainfmName) - heatmap_img = HT.Image("/images/heatmap2_final.jpg", name='mintmap', alt="QTL Heat Map and Clustering", title="QTL Heatmap and Clustering", style="border:none;") - heatmap.append(heatmap_img) - partialCorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'partialCorrInput');" % mainfmName) - partialCorr_img = HT.Image("/images/partial_correlation_final.jpg", name='partialCorr', alt="Partial Correlation", title="Partial Correlation", style="border:none;") - partialCorr.append(partialCorr_img) - addselect = HT.Href(url="#redirect", onClick="addRmvSelection('%s', document.getElementsByName('%s')[0], 'addToSelection');" % (RISet, mainfmName)) - addselect_img = HT.Image("/images/add_collection1_final.jpg", name="addselect", alt="Add To Collection", title="Add To Collection", style="border:none;") - addselect.append(addselect_img) - selectall = HT.Href(url="#redirect", onClick="checkAll(document.getElementsByName('%s')[0]);" % mainfmName) - selectall_img = HT.Image("/images/select_all2_final.jpg", name="selectall", alt="Select All", title="Select All", style="border:none;") - selectall.append(selectall_img) - selectinvert = HT.Href(url="#redirect", onClick = "checkInvert(document.getElementsByName('%s')[0]);" % mainfmName) - selectinvert_img = HT.Image("/images/invert_selection2_final.jpg", name="selectinvert", alt="Invert Selection", title="Invert Selection", style="border:none;") - selectinvert.append(selectinvert_img) - reset = HT.Href(url="#redirect", onClick="checkNone(document.getElementsByName('%s')[0]); return false;" % mainfmName) - reset_img = HT.Image("/images/select_none2_final.jpg", alt="Select None", title="Select None", style="border:none;") - reset.append(reset_img) - selecttraits = HT.Input(type='button' ,name='selecttraits',value='Select Traits', onClick="checkTraits(this.form);",Class="button") - selectgt = HT.Input(type='text' ,name='selectgt',value='-1.0', size=6,maxlength=10,onChange="checkNumeric(this,1.0,'-1.0','gthan','greater than filed')") - selectlt = HT.Input(type='text' ,name='selectlt',value='1.0', size=6,maxlength=10,onChange="checkNumeric(this,-1.0,'1.0','lthan','less than field')") - selectandor = HT.Select(name='selectandor') - selectandor.append(('AND','and')) - selectandor.append(('OR','or')) - selectandor.selected.append('AND') - - chrMenu = HT.Input(type='hidden',name='chromosomes',value='all') - - corrHeading = HT.Paragraph('Partial Correlation Table', Class="title") - - - pageTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="100%", border=0, align="Left") - containerTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="90%",border=0, align="Left") - - optionsTable = HT.TableLite(cellSpacing=2, cellPadding=0,width="320", height="80", border=0, align="Left") - optionsTable.append(HT.TR(HT.TD(selectall), HT.TD(reset), HT.TD(selectinvert), HT.TD(addselect), align="left")) - optionsTable.append(HT.TR(HT.TD(" "*1,"Select"), HT.TD("Deselect"), HT.TD(" "*1,"Invert"), HT.TD(" "*3,"Add"))) - containerTable.append(HT.TR(HT.TD(optionsTable))) - - functionTable = HT.TableLite(cellSpacing=2,cellPadding=0,width="480",height="80", border=0, align="Left") - functionRow = HT.TR(HT.TD(networkGraph, width="16.7%"), HT.TD(cormatrix, width="16.7%"), HT.TD(partialCorr, width="16.7%"), HT.TD(mcorr, width="16.7%"), HT.TD(mintmap, width="16.7%"), HT.TD(heatmap), align="left") - labelRow = HT.TR(HT.TD(" "*1,HT.Text("Graph")), HT.TD(" "*1,HT.Text("Matrix")), HT.TD(" "*1,HT.Text("Partial")), HT.TD(HT.Text("Compare")), HT.TD(HT.Text("QTL Map")), HT.TD(HT.Text(text="Heat Map"))) - functionTable.append(functionRow, labelRow) - containerTable.append(HT.TR(HT.TD(functionTable), HT.BR())) - - moreOptions = HT.Input(type='button',name='options',value='More Options', onClick="",Class="toggle") - fewerOptions = HT.Input(type='button',name='options',value='Fewer Options', onClick="",Class="toggle") - - if (fd.formdata.getvalue('showHideOptions') == 'less'): - containerTable.append(HT.TR(HT.TD(" "), height="10"), HT.TR(HT.TD(HT.Div(fewerOptions, Class="toggleShowHide")))) - containerTable.append(HT.TR(HT.TD(" "))) - else: - containerTable.append(HT.TR(HT.TD(" "), height="10"), HT.TR(HT.TD(HT.Div(moreOptions, Class="toggleShowHide")))) - containerTable.append(HT.TR(HT.TD(" "))) - - containerTable.append(HT.TR(HT.TD(HT.Span(selecttraits,' with partial r > ',selectgt, ' ',selectandor, ' r < ',selectlt,Class="bd1 cbddf fs11")), style="display:none;", Class="extra_options")) - - - tblobj = {} - - - if self.db.type=="Geno": - - containerTable.append(HT.TR(HT.TD(xlsUrl, height=40))) - pageTable.append(HT.TR(HT.TD(containerTable))) - - tblobj['header'], worksheet = self.getTableHeaderForGeno( method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle) - newrow += 1 - - corrScript = HT.Script(language="Javascript") - corrScript.append("var corrArray = new Array();") - - sortby = self.getSortByValue( calculationMethod = self.method ) - - tblobj['body'], worksheet, corrScript = self.getTableBodyForGeno(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript) - - workbook.close() - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - # NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py; - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable") - pageTable.append(HT.TR(HT.TD(div))) - form.append(HT.Input(name='ShowStrains',type='hidden', value =1), - HT.Input(name='ShowLine',type='hidden', value =1), - HT.P(),pageTable) - - TD_LR.append(corrHeading, info_form, HT.P(), info, form, HT.P()) - - self.dict['body'] = str(TD_LR) - # updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js - self.dict['js1'] = '' - self.dict['title'] = 'Partial Correlation Result' - - elif self.db.type=="Publish": - - containerTable.append(HT.TR(HT.TD(xlsUrl, height=40))) - pageTable.append(HT.TR(HT.TD(containerTable))) - - tblobj['header'], worksheet = self.getTableHeaderForPublish(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle) - newrow += 1 - - sortby = self.getSortByValue( calculationMethod = self.method ) - - corrScript = HT.Script(language="Javascript") - corrScript.append("var corrArray = new Array();") - - tblobj['body'], worksheet, corrScript = self.getTableBodyForPublish(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript) - - workbook.close() - - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - # NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py; - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable") - pageTable.append(HT.TR(HT.TD(div))) - - form.append( - HT.Input(name='ShowStrains',type='hidden', value =1), - HT.Input(name='ShowLine',type='hidden', value =1), - HT.P(),pageTable) - - TD_LR.append(corrHeading, info_form, HT.P(), info, form, HT.P()) - - self.dict['body'] = str(TD_LR) - #updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js - self.dict['js1'] = '' - self.dict['title'] = 'Partial Correlation Result' - - elif self.db.type=="ProbeSet": - - tblobj['header'], worksheet = self.getTableHeaderForProbeSet(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle) - newrow += 1 - - sortby = self.getSortByValue( calculationMethod = self.method ) - - corrScript = HT.Script(language="Javascript") - corrScript.append("var corrArray = new Array();") - - tblobj['body'], worksheet, corrScript = self.getTableBodyForProbeSet(traitList=traitList, primaryTrait=myTrait, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript) - - workbook.close() - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - - ''' - #XZ, 07/07/2010: I comment out this block of code. - WebGestaltScript = HT.Script(language="Javascript") - WebGestaltScript.append(""" -setTimeout('openWebGestalt()', 2000); -function openWebGestalt(){ - var thisForm = document['WebGestalt']; - makeWebGestaltTree(thisForm, '%s', %d, 'edag_only.php'); -} - """ % (mainfmName, len(traitList))) - ''' - - #XZ: here is the table of traits - # NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py; - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable") - - self.cursor.execute('SELECT GeneChip.GO_tree_value FROM GeneChip, ProbeFreeze, ProbeSetFreeze WHERE GeneChip.Id = ProbeFreeze.ChipId and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeSetFreeze.Name = "%s"' % self.db.name) - result = self.cursor.fetchone() - - if result: - GO_tree_value = result[0] - - if GO_tree_value: - - hddnWebGestalt = { - 'id_list':'', - 'correlation':'', - 'id_value':'', - 'llid_list':'', - 'id_type':GO_tree_value, - 'idtype':'', - 'species':'', - 'list':'', - 'client':''} - - hddnWebGestalt['ref_type'] = hddnWebGestalt['id_type'] - hddnWebGestalt['cat_type'] = 'GO' - hddnWebGestalt['significancelevel'] = 'Top10' - - if species == 'rat': - hddnWebGestalt['org'] = 'Rattus norvegicus' - elif species == 'human': - hddnWebGestalt['org'] = 'Homo sapiens' - elif species == 'mouse': - hddnWebGestalt['org'] = 'Mus musculus' - else: - hddnWebGestalt['org'] = '' - - for key in hddnWebGestalt.keys(): - form.append(HT.Input(name=key, value=hddnWebGestalt[key], type='hidden')) - - #XZ, 01/12/2009: create database menu for 'Add Correlation' - self.cursor.execute(""" - select - ProbeSetFreeze.FullName, ProbeSetFreeze.Id, Tissue.name - from - ProbeSetFreeze, ProbeFreeze, ProbeSetFreeze as ps2, ProbeFreeze as p2, Tissue - where - ps2.Id = %d - and ps2.ProbeFreezeId = p2.Id - and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id - and (ProbeFreeze.InbredSetId = p2.InbredSetId or (ProbeFreeze.InbredSetId in (1, 3) and p2.InbredSetId in (1, 3))) - and p2.ChipId = ProbeFreeze.ChipId - and ps2.Id != ProbeSetFreeze.Id - and ProbeFreeze.TissueId = Tissue.Id - and ProbeSetFreeze.public > %d - order by - ProbeFreeze.TissueId, ProbeSetFreeze.CreateTime desc - """ % (self.db.id, webqtlConfig.PUBLICTHRESH)) - - results = self.cursor.fetchall() - dbCustomizer = HT.Select(results, name = "customizer") - databaseMenuSub = preTissue = "" - for item in results: - TName, TId, TTissue = item - if TTissue != preTissue: - if databaseMenuSub: - dbCustomizer.append(databaseMenuSub) - databaseMenuSub = HT.Optgroup(label = '%s mRNA ------' % TTissue) - preTissue = TTissue - - databaseMenuSub.append(item[:2]) - if databaseMenuSub: - dbCustomizer.append(databaseMenuSub) - #updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js - #variables: filename, strainIds and vals are required by getquerystring function - strainIds=self.getStrainIds(species=species, strains=_primarystrains) - var1 = HT.Input(name="filename", value=filename, type='hidden') - var2 = HT.Input(name="strainIds", value=strainIds, type='hidden') - var3 = HT.Input(name="vals", value=_primaryvals, type='hidden') - customizerButton = HT.Input(type="button", Class="button", value="Add Correlation", onClick = "xmlhttpPost('%smain.py?FormID=AJAX_table', 'sortable', (getquerystring(this.form)))" % webqtlConfig.CGIDIR) - - containerTable.append(HT.TR(HT.TD(HT.Span(var1,var2,var3,customizerButton, "with", dbCustomizer, Class="bd1 cbddf fs11"), HT.BR(), HT.BR()), style="display:none;", Class="extra_options")) - - #outside analysis part - GCATButton = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GCAT');" % mainfmName) - GCATButton_img = HT.Image("/images/GCAT_logo_final.jpg", name="GCAT", alt="GCAT", title="GCAT", style="border:none") - GCATButton.append(GCATButton_img) - - ODE = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'ODE');" % mainfmName) - ODE_img = HT.Image("/images/ODE_logo_final.jpg", name="ode", alt="ODE", title="ODE", style="border:none") - ODE.append(ODE_img) - - WebGestalt = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GOTree');" % mainfmName) - WebGestalt_img = HT.Image("/images/webgestalt_icon_final.jpg", name="webgestalt", alt="Gene Set Analysis Toolkit", title="Gene Set Analysis Toolkit", style="border:none") - WebGestalt.append(WebGestalt_img) - - LinkOutTable = HT.TableLite(cellSpacing=2,cellPadding=0,width="320",height="80", border=0, align="Left") - if not GO_tree_value: - LinkOutRow = HT.TR(HT.TD(GCATButton, width="50%"), HT.TD(ODE, width="50%"), align="left") - LinkOutLabels = HT.TR(HT.TD(" ", HT.Text("GCAT"), width="50%"), HT.TD(" ",HT.Text("ODE"), width="50%"), align="left") - else: - LinkOutRow = HT.TR(HT.TD(WebGestalt, width="25%"), HT.TD(GCATButton, width="25%"), HT.TD(ODE, width="25%"), align="left") - LinkOutLabels = HT.TR(HT.TD(HT.Text("Gene Set")), HT.TD(" "*2, HT.Text("GCAT")), HT.TD(" "*3, HT.Text("ODE")), style="display:none;", Class="extra_options") - LinkOutTable.append(LinkOutRow,LinkOutLabels) - - containerTable.append(HT.TR(HT.TD(LinkOutTable), Class="extra_options", style="display:none;")) - - containerTable.append(HT.TR(HT.TD(xlsUrl, HT.BR(), HT.BR(), height=40))) - - pageTable.append(HT.TR(HT.TD(containerTable))) - - pageTable.append(HT.TR(HT.TD(div))) - - if species == 'human': - heatmap = "" - - form.append(HT.Input(name='ShowStrains',type='hidden', value =1), - HT.Input(name='ShowLine',type='hidden', value =1), - info, HT.BR(), pageTable, HT.BR()) - - TD_LR.append(corrHeading, info_form, HT.P(), form, HT.P()) - - - self.dict['body'] = str(TD_LR) - self.dict['title'] = 'Partial Correlation Result' - # updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js - self.dict['js1'] = '' - self.dict['js2'] = 'onLoad="pageOffset()"' - self.dict['layer'] = self.generateWarningLayer() - - else: - self.dict['body'] = "" - - - -#################################### -# # -#Partial CorrelationPage Functions # -# # -#################################### - - - def getSortByValue(self, calculationMethod): - - sortby = ("partial_pv", "up") - - if calculationMethod == "3": #XZ: literature correlation - sortby = ("lcorr","down") - elif calculationMethod == "4" or calculationMethod == "5": #XZ: tissue correlation - sortby = ("tissuecorr", "down") - - return sortby - - - #XZ, 3/31/2010: - #A[0] holds trait name. - #A[1] holds partial correlation coefficient number. - #A[2] holds N. - #A[3] holds p value of partial correlation. - def cmpPartialCorrPValue (self, A, B): - try: - if A[3] < B[3]: - return -1 - elif A[3] == B[3]: - return 0 - else: - return 1 - except: - return 0 - - - #XZ, 4/1/2010: - #A[0] holds trait name. - #A[1] holds N. - #A[2] holds partial correlation coefficient number. - #A[3] holds p value of partial correlation. - #A[6] holds literature corr or tissue corr value. - #Sort by literature corr or tissue corr first, then by partial corr p value. - def cmpLitCorr(self, A, B): - try: - if abs(A[6]) < abs(B[6]): - return 1 - elif abs(A[6]) == abs(B[6]): - if A[3] < B[3]: - return -1 - elif A[3] == B[3]: - return 0 - else: - return 1 - else: - return -1 - except: - return 0 - - - def getPartialCorrelationsFast(self, _strains, _vals, _controlvals, nnCorr, DatabaseFileName, species, input_trait_GeneId,gene_symbol,TissueProbeSetFreezeId ): - """Calculates and returns correlation coefficients using data from a csv text file.""" - - try: - allcorrelations = [] - - useLit = False - if self.method == "3": - litCorrs = self.fetchLitCorrelations(species=species, GeneId=input_trait_GeneId, db=self.db, returnNumber=self.returnNumber) - useLit = True - - useTissueCorr = False - if self.method == "4" or self.method == "5": - tissueCorrs = self.fetchTissueCorrelations(db=self.db,primaryTraitSymbol=gene_symbol, TissueProbeSetFreezeId=TissueProbeSetFreezeId, method=self.method, returnNumber=self.returnNumber) - useTissueCorr = True - - datasetFile = open(webqtlConfig.TEXTDIR+DatabaseFileName,'r') - - #XZ, 01/08/2009: read the first line - line = datasetFile.readline() - dataset_strains = webqtlUtil.readLineCSV(line)[1:] - - #XZ, 3/30/2010: This step is critical. - good_dataset_strains_index = [] - - for i in range(len(_strains)): - found_in_dataset_strains = 0 - for j, one_dataset_strain in enumerate(dataset_strains): - if one_dataset_strain == _strains[i]: - found_in_dataset_strains = 1 - good_dataset_strains_index.append(j) - break - - if not found_in_dataset_strains: - good_dataset_strains_index.append(-99999) - - allTargetTraitNames = [] - allTargetTraitValues = [] - - #XZ, 04/01/2009: If literature corr or tissue corr is selected, - #XZ: there is no need to compute partial correlation for all traits. - #XZ: If genetic corr is selected, compute partial correlation for all traits. - for line in datasetFile: - trait_line = webqtlUtil.readLineCSV(line) - trait_name = trait_line[0] - trait_data = trait_line[1:] - - if useLit: - if not litCorrs.has_key( trait_name ): - continue - - if useTissueCorr: - if not tissueCorrs.has_key( trait_name ): - continue - - #XZ, 04/01/2010: If useLit or useTissueCorr, and this trait should not be added, - #it will not go to the next step. - - good_dataset_vals = [] - for i in good_dataset_strains_index: - if i == -99999: - good_dataset_vals.append(None) - else: - good_dataset_vals.append( float(trait_data[i]) ) - - allTargetTraitNames.append(trait_name) - allTargetTraitValues.append(good_dataset_vals) - - datasetFile.close() - - if self.method in ["2", "5"]: #Spearman - allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames, method='s') - else: - allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames) - - totalTraits = len(allcorrelations) - - if useLit or useTissueCorr: - for i, item in enumerate(allcorrelations): - if useLit: - allcorrelations[i].append(litCorrs[ item[0] ]) - if useTissueCorr: - tempCorr, tempPValue = tissueCorrs[ item[0] ] - allcorrelations[i].append(tempCorr) - allcorrelations[i].append(tempPValue) - - return totalTraits, allcorrelations - except: - return 0, 0 - - - def getPartialCorrelationsNormal(self, _strains, _vals, _controlvals, nnCorr, species, input_trait_GeneId, input_trait_symbol,TissueProbeSetFreezeId): - """Calculates and returns correlation coefficients""" - - traitdatabase, dataStartPos = self.fetchAllDatabaseData(species=species, GeneId=input_trait_GeneId, GeneSymbol=input_trait_symbol, strains=_strains, db=self.db, method=self.method, returnNumber=self.returnNumber, tissueProbeSetFreezeId=TissueProbeSetFreezeId) - totalTraits = len(traitdatabase) #XZ, 09/18/2008: total trait number - - allcorrelations = [] - - allTargetTraitNames = [] - allTargetTraitValues = [] - - for traitdata in traitdatabase: - traitdataName = traitdata[0] - traitvals = traitdata[dataStartPos:] - allTargetTraitNames.append (traitdataName) - allTargetTraitValues.append (traitvals) - - if self.method in ["2", "5"]: #Spearman - allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames, method='s') - else: - allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames) - - #XZ, 09/28/2008: if user select '3', then fetchAllDatabaseData would give us LitCorr in the [1] position - #XZ, 09/28/2008: if user select '4' or '5', then fetchAllDatabaseData would give us Tissue Corr in the [1] position - #XZ, 09/28/2008: and Tissue Corr P Value in the [2] position - if input_trait_GeneId and self.db.type == "ProbeSet" and self.method in ["3", "4", "5"]: - for i, item in enumerate(allcorrelations): - if self.method == "3": - item.append( traitdatabase[1] ) - if self.method == "4" or self.method == "5": - item.append( traitdatabase[1] ) - item.append( traitdatabase[2] ) - - - return totalTraits, allcorrelations - - - def getTableHeaderForPublish(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", nowrap="on"), sort=0), - THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="id", idx=1), - THCell(HT.TD('Phenotype', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="pheno", idx=2), - THCell(HT.TD('Authors', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="auth", idx=3), - THCell(HT.TD('Year', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="year", idx=4), - THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="nstr", idx=5), - THCell(HT.TD('Partial r ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="partial_corr", idx=6), - THCell(HT.TD('p(partial r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="partial_pv", idx=7), - THCell(HT.TD('r ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="corr", idx=8), - THCell(HT.TD('p(r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="pv", idx=9), - THCell(HT.TD('delta r', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="delta_corr", idx=10)]] - - for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "PubMedID", "N", "Partial r", "p(partial r)", "r ", "p(r)", "delta 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", nowrap="on"), sort=0), - THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="id", idx=1), - THCell(HT.TD('Phenotype', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="pheno", idx=2), - THCell(HT.TD('Authors', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="auth", idx=3), - THCell(HT.TD('Year', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="year", idx=4), - THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="nstr", idx=5), - THCell(HT.TD('Partial rho ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="partial_corr", idx=6), - THCell(HT.TD('p(partial rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="partial_pv", idx=7), - THCell(HT.TD('rho ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="corr", idx=8), - THCell(HT.TD('p(rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="pv", idx=9), - THCell(HT.TD('delta rho', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="delta_corr", idx=10)]] - - for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "PubMedID", "N", "Partial rho", "p(partial rho)", "rho ", "p(rho)", "delta rho"]): - worksheet.write([newrow, ncol], item, headingStyle) - worksheet.set_column([ncol, ncol], 2*len(item)) - - return tblobj_header, worksheet - - - def getTableBodyForPublish(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None): - - tblobj_body = [] - - for thisTrait in traitList: - tr = [] - - trId = str(thisTrait) - - #partial corr value could be string 'NA' - try: - corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.partial_corr)) - except: - corrScript.append('corrArray["%s"] = {corr:"NA"};' % (trId)) - - 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"), nowrap="yes",align="center", Class="fs12 fwn b1 c222"),str(thisTrait.name), thisTrait.name)) - - PhenotypeString = thisTrait.post_publication_description - if thisTrait.confidential: - if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users): - PhenotypeString = thisTrait.pre_publication_description - tr.append(TDCell(HT.TD(PhenotypeString, Class="fs12 fwn b1 c222"), PhenotypeString, PhenotypeString.upper())) - - tr.append(TDCell(HT.TD(thisTrait.authors, Class="fs12 fwn b1 c222 fsI"),thisTrait.authors, thisTrait.authors.strip().upper())) - - try: - PubMedLinkText = myear = repr = int(thisTrait.year) - except: - PubMedLinkText = repr = "N/A" - myear = 0 - if thisTrait.pubmed_id: - PubMedLink = HT.Href(text= repr,url= webqtlConfig.PUBMEDLINK_URL % thisTrait.pubmed_id,target='_blank', Class="fs12 fwn") - else: - PubMedLink = repr - - tr.append(TDCell(HT.TD(PubMedLink, Class="fs12 fwn b1 c222", align='center'), repr, myear)) - - repr = '%d' % thisTrait.NOverlap - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.NOverlap)) - - try: - repr = '%3.3f' % thisTrait.partial_corr - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), repr, abs(thisTrait.partial_corr))) - except: - repr = 'NA' - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 )) - - repr = webqtlUtil.SciFloat(thisTrait.partial_corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.partial_corrPValue)) - - repr = '%3.3f' % thisTrait.corr - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), repr, abs(thisTrait.corr))) - - repr = webqtlUtil.SciFloat(thisTrait.corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue)) - - #delta - try: - delta = '%3.3f' % ( float(thisTrait.partial_corr) - float(thisTrait.corr) ) - tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='right', nowrap="on"), text=delta, val=abs(float(delta)) )) - except: - delta = 'NA' - tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='left'), text=delta, val=0 )) - - tblobj_body.append(tr) - - for ncol, item in enumerate([thisTrait.name, PhenotypeString, thisTrait.authors, thisTrait.year, thisTrait.pubmed_id, thisTrait.NOverlap, thisTrait.partial_corr, thisTrait.partial_corrPValue, thisTrait.corr, thisTrait.corrPValue, delta]): - worksheet.write([newrow, ncol], str(item) ) - newrow += 1 - - return tblobj_body, worksheet, corrScript - - - 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('Locus', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='locus', idx=1), - THCell(HT.TD('Chr', Class="fs13 fwb ffl b1 cw cbrb"), text='chr', idx=2), - THCell(HT.TD('Megabase', Class="fs13 fwb ffl b1 cw cbrb"), text='Mb', idx=3), - THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='nstr', idx=4), - THCell(HT.TD('Partial r ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_corr', idx=5), - THCell(HT.TD('p(partial r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=6), - THCell(HT.TD('r ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='corr', idx=7), - THCell(HT.TD('p(r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=8), - THCell(HT.TD('delta r', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_corr', idx=9)]] - - for ncol, item in enumerate(['Locus', 'Chr', ' Mb ', ' N ', 'Partial r', 'p(partial r)', 'r ', 'p(r)', 'delta 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('Locus', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='locus', idx=1), - THCell(HT.TD('Chr', Class="fs13 fwb ffl b1 cw cbrb"), text='chr', idx=2), - THCell(HT.TD('Megabase', Class="fs13 fwb ffl b1 cw cbrb"), text='Mb', idx=3), - THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='nstr', idx=4), - THCell(HT.TD('Partial rho', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_corr', idx=5), - THCell(HT.TD('p(partial rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=6), - THCell(HT.TD('rho ', Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text='corr', idx=7), - THCell(HT.TD('p(rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=8), - THCell(HT.TD('delta rho', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_corr', idx=9)]] - - for ncol, item in enumerate(['Locus', 'Chr', ' Mb ', ' N ', 'Partial rho', 'p(partial rho)', 'rho ', 'p(rho)', 'delta 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) - - #partial corr value could be string 'NA' - try: - corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.partial_corr)) - except: - corrScript.append('corrArray["%s"] = {corr:"NA"};' % (trId)) - - 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="center", Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name.upper())) - - #tr.append(TDCell(HT.TD(thisTrait.chr, Class="fs12 fwn ffl b1 c222", align='right'), text=str(thisTrait.chr))) - - try: - Mbvalue = int(thisTrait.chr)*1000 + thisTrait.mb - except: - if not thisTrait.chr or not thisTrait.mb: - Mbvalue = 1000000 - elif thisTrait.chr.upper() == 'X': - Mbvalue = 20*1000 + thisTrait.mb - else: - Mbvalue = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb - - tr.append(TDCell( HT.TD(thisTrait.chr, Class="fs12 fwn b1 c222", align='right'), thisTrait.chr, Mbvalue) ) - tr.append(TDCell(HT.TD(thisTrait.mb, Class="fs12 fwn ffl b1 c222", align='right'), text=str(thisTrait.mb), val=Mbvalue)) - - repr = '%d' % thisTrait.NOverlap - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.NOverlap)) - - try: - repr='%3.3f' % thisTrait.partial_corr - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right',nowrap='ON'),repr,abs(thisTrait.partial_corr))) - except: - repr = 'NA' - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 )) - - repr = webqtlUtil.SciFloat(thisTrait.partial_corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.partial_corrPValue)) - - repr = '%3.3f' % thisTrait.corr - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right',nowrap='ON'), repr, abs(thisTrait.corr))) - - repr = webqtlUtil.SciFloat(thisTrait.corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue)) - - #delta - try: - delta = '%3.3f' % ( float(thisTrait.partial_corr) - float(thisTrait.corr) ) - tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'), text=delta, val=abs(float(delta)) )) - except: - delta = 'NA' - tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='left'), text=delta, val=0 )) - - tblobj_body.append(tr) - - for ncol, item in enumerate([thisTrait.name, thisTrait.chr, thisTrait.mb, thisTrait.NOverlap, thisTrait.partial_corr, thisTrait.partial_corrPValue, thisTrait.corr, thisTrait.corrPValue, delta]): - worksheet.write([newrow, ncol], item) - newrow += 1 - - return tblobj_body, worksheet, corrScript - - - def getTableHeaderForProbeSet(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",nowrap='ON'), sort=0), - THCell(HT.TD('Record',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="id", idx=1), - THCell(HT.TD('','Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="symbol", idx=2), - THCell(HT.TD('','Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="desc", idx=3), - #XZ, 12/09/2008: sort chr - THCell(HT.TD('','Chr',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="chr", idx=4), - THCell(HT.TD('','Mb',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mb", idx=5), - THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="mean", idx=6), - THCell(HT.TD('N',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="nstr", idx=7), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'Partial 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="partial_corr", idx=8), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'p(partial 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="partial_pv", idx=9), - 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="corr", idx=10), - 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="pv", idx=11), - THCell(HT.TD('delta',HT.BR(), 'r', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="delta_corr", idx=12), - THCell(HT.TD(HT.Href( - text = HT.Span('Pubmed',HT.BR(), 'r', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#literatureCorr"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="lcorr", idx=13), - #XZ, 09/22/2008: tissue correlation - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'r', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuecorr", idx=14), - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'p(r)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_p_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuepvalue", idx=15)]] - - for ncol, item in enumerate(['Record', 'Gene ID', 'Symbol', 'Description', 'Chr', 'Megabase', 'Mean Expr', 'N ', 'Sample Partial r', 'Sample p(partial r)', 'Sample r', 'Sample p(r)', 'delta r', 'Lit Corr', 'Tissue r', 'Tissue 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",nowrap='ON'), sort=0), - THCell(HT.TD('Record',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="id", idx=1), - THCell(HT.TD('','Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="symbol", idx=2), - THCell(HT.TD('','Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="desc", idx=3), - THCell(HT.TD('','Chr',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="chr", idx=4), - THCell(HT.TD('','Mb',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mb", idx=5), - THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="mean", idx=6), - THCell(HT.TD('N',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="nstr", idx=7), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'Partial 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="partial_corr", idx=8), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'p(partial 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="partial_pv", idx=9), - 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_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="corr", idx=10), - 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_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="pv", idx=11), - THCell(HT.TD('delta',HT.BR(),'rho', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="delta_corr", idx=12), - THCell(HT.TD(HT.Href( - text = HT.Span('Pubmed',HT.BR(), 'r', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#literatureCorr"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="lcorr", idx=13), - #XZ, 09/22/2008: tissue correlation - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'rho', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_rho"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuecorr", idx=14), - THCell(HT.TD(HT.Href( - text = HT.Span('Tissue',HT.BR(), 'p(rho)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#tissue_p_rho"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuepvalue", idx=15)]] - - for ncol, item in enumerate(['Record', 'Gene ID', 'Symbol', 'Description', 'Chr', 'Megabase', 'Mean Expr', 'N ', 'Sample Partial rho', 'Sample p(partial rho)', 'Sample rho', 'Sample p(rho)', 'delta rho', 'Pubmed r', 'Tissue rho', 'Tissue p(rho)']): - worksheet.write([newrow, ncol], item, headingStyle) - worksheet.set_column([ncol, ncol], 2*len(item)) - - return tblobj_header, worksheet - - - def getTableBodyForProbeSet(self, traitList=[], primaryTrait=None, formName=None, worksheet=None, newrow=None, corrScript=None): - - tblobj_body = [] - - for thisTrait in traitList: - - if thisTrait.symbol: - pass - else: - thisTrait.symbol = "N/A" - - if thisTrait.geneid: - symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % thisTrait.geneid, Class="fs12 fwn") - else: - symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gene&term=%s" % thisTrait.symbol, Class="fs12 fwn") - - tr = [] - - trId = str(thisTrait) - - #partial corr value could be string 'NA' - try: - corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.partial_corr)) - except: - corrScript.append('corrArray["%s"] = {corr:"NA"};' % (trId)) - - #XZ, 12/08/2008: checkbox - 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)) - - #XZ, 12/08/2008: probeset name - tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName,thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn b1 c222"), thisTrait.name, thisTrait.name.upper())) - - #XZ, 12/08/2008: gene symbol - tr.append(TDCell(HT.TD(symbolurl, Class="fs12 fwn b1 c222 fsI"),thisTrait.symbol, thisTrait.symbol.upper())) - - #XZ, 12/08/2008: description - #XZ, 06/05/2009: Rob asked to add probe target description - description_string = str(thisTrait.description).strip() - target_string = str(thisTrait.probe_target_description).strip() - - description_display = '' - - if len(description_string) > 1 and description_string != 'None': - description_display = description_string - else: - description_display = thisTrait.symbol - - if len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None': - description_display = description_display + '; ' + target_string.strip() - - tr.append(TDCell(HT.TD(description_display, Class="fs12 fwn b1 c222"), description_display, description_display)) - - #XZ, 12/08/2008: Mbvalue is used for sorting - try: - Mbvalue = int(thisTrait.chr)*1000 + thisTrait.mb - except: - if not thisTrait.chr or not thisTrait.mb: - Mbvalue = 1000000 - elif thisTrait.chr.upper() == 'X': - Mbvalue = 20*1000 + thisTrait.mb - else: - Mbvalue = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb - - #XZ, 12/08/2008: chromosome number - #XZ, 12/10/2008: use Mbvalue to sort chromosome - tr.append(TDCell( HT.TD(thisTrait.chr, Class="fs12 fwn b1 c222", align='right'), thisTrait.chr, Mbvalue) ) - - #XZ, 12/08/2008: Rob wants 6 digit precision, and we have to deal with that the mb could be None - if not thisTrait.mb: - tr.append(TDCell(HT.TD(thisTrait.mb, Class="fs12 fwn b1 c222",align='right'), thisTrait.mb, Mbvalue)) - else: - tr.append(TDCell(HT.TD('%.6f' % thisTrait.mb, Class="fs12 fwn b1 c222", align='right'), thisTrait.mb, Mbvalue)) - - #XZ, 01/12/08: This SQL query is much faster. - self.cursor.execute(""" - select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet - where ProbeSetXRef.ProbeSetFreezeId = %d and - ProbeSet.Id = ProbeSetXRef.ProbeSetId and - ProbeSet.Name = '%s' - """ % (thisTrait.db.id, thisTrait.name)) - result = self.cursor.fetchone() - if result: - if result[0]: - mean = result[0] - else: - mean=0 - else: - mean = 0 - - #XZ, 06/05/2009: It is neccessary to turn on nowrap - repr = "%2.3f" % mean - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'),repr, mean)) - - #XZ: number of overlaped cases for partial corr - repr = '%d' % thisTrait.NOverlap - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.NOverlap)) - - #XZ: sample partial correlation - try: - repr='%3.3f' % thisTrait.partial_corr - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'),repr,abs(thisTrait.partial_corr))) - except: - repr = 'NA' - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 )) - - #XZ: p value of genetic partial correlation - repr = webqtlUtil.SciFloat(thisTrait.partial_corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.partial_corrPValue)) - - repr = '%3.3f' % thisTrait.corr - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right',nowrap='ON'), repr, abs(thisTrait.corr))) - - repr = webqtlUtil.SciFloat(thisTrait.corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue)) - - #delta - try: - delta = '%3.3f' % ( float(thisTrait.partial_corr) - float(thisTrait.corr) ) - tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'), text=delta, val=abs(float(delta)) )) - except: - delta = 'NA' - tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='left'), text=delta, val=0 )) - - #XZ, 12/08/2008: literature correlation - LCorr = 0.0 - LCorrStr = "N/A" - if hasattr(thisTrait, 'LCorr') and thisTrait.LCorr: - LCorr = thisTrait.LCorr - LCorrStr = "%2.3f" % thisTrait.LCorr - tr.append(TDCell(HT.TD(LCorrStr, Class="fs12 fwn b1 c222", align='right'), LCorrStr, abs(LCorr))) - - #XZ, 09/22/2008: tissue correlation. - TCorr = 0.0 - TCorrStr = "N/A" - #XZ, 11/18/2010: need to pass two gene symbols - if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissueCorr: - TCorr = thisTrait.tissueCorr - TCorrStr = "%2.3f" % thisTrait.tissueCorr - #NL, 07/19/2010: add a new parameter rankOrder for js function 'showTissueCorrPlot' - rankOrder =thisTrait.rankOrder - TCorrPlotURL = "javascript:showTissueCorrPlot('%s','%s','%s',%d)" %(formName, primaryTrait.symbol, thisTrait.symbol,rankOrder) - tr.append(TDCell(HT.TD(HT.Href(text=TCorrStr, url=TCorrPlotURL, Class="fs12 fwn ff1"), Class="fs12 fwn ff1 b1 c222", align='right'), TCorrStr, abs(TCorr) )) - else: - tr.append(TDCell(HT.TD(TCorrStr, Class="fs12 fwn b1 c222", align='right'), TCorrStr, abs(TCorr))) - - #XZ, 12/08/2008: p value of tissue correlation - TPValue = 1.0 - TPValueStr = "N/A" - if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissueCorr: #XZ, 09/22/2008: thisTrait.tissuePValue can't be used here because it could be 0 - TPValue = thisTrait.tissuePValue - TPValueStr = "%2.3f" % thisTrait.tissuePValue - tr.append(TDCell(HT.TD(TPValueStr, Class="fs12 fwn b1 c222", align='right'), TPValueStr, abs(TPValue) )) - - tblobj_body.append(tr) - - for ncol, item in enumerate([thisTrait.name, thisTrait.geneid, thisTrait.symbol, thisTrait.description, thisTrait.chr, thisTrait.mb, mean, thisTrait.NOverlap, thisTrait.partial_corr, thisTrait.partial_corrPValue, thisTrait.corr, thisTrait.corrPValue, delta, LCorrStr, TCorrStr, TPValueStr]): - worksheet.write([newrow, ncol], item) - - newrow += 1 - - return tblobj_body, worksheet, corrScript - - - def getFormForPrimaryAndControlTraits (self, primaryTrait, controlTraits): - - info_form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase', submit=HT.Input(type='hidden')) - - hddn = {'FormID':'showDatabase', 'database':'_', 'ProbeSetID':'_', 'CellID':'_' }#XZ: These four parameters are required by javascript function showDatabase2. - - for key in hddn.keys(): - info_form.append(HT.Input(name=key, value=hddn[key], type='hidden')) - - info_form.append(HT.Paragraph("Primary Trait", Class="subtitle"), '\n') - - primaryTraitTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0) - descriptionString = primaryTrait.genHTML(dispFromDatabase=1) - if primaryTrait.db.type == 'Publish' and primaryTrait.confidential: - descriptionString = primaryTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=primaryTrait.authorized_users) - primaryTraitTable.append(HT.TR(HT.TD(HT.Href(text='%s' % descriptionString, url="javascript:showDatabase2('%s','%s','%s')" % (primaryTrait.db.name,primaryTrait.name,primaryTrait.cellid), Class="fs12 fwn") ))) - - info_form.append(primaryTraitTable) - - info_form.append(HT.Paragraph("Control Traits", Class="subtitle"), '\n') - - controlTraitsTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0) - - seq = 1 - - ## Generate the listing table for control traits - for thisTrait in controlTraits: - descriptionString = thisTrait.genHTML(dispFromDatabase=1) - if thisTrait.db.type == 'Publish' and thisTrait.confidential: - descriptionString = thisTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users) - controlTraitsTable.append(HT.TR(HT.TD("%d."%seq,align="right",width=10), - HT.TD(HT.Href(text='%s' % descriptionString,url="javascript:showDatabase2('%s','%s','%s')" % (thisTrait.db.name,thisTrait.name,thisTrait.cellid), Class="fs12 fwn") ))) - seq += 1 - - info_form.append(controlTraitsTable) - - return info_form diff --git a/web/webqtl/correlation/PartialCorrInputPage.py b/web/webqtl/correlation/PartialCorrInputPage.py deleted file mode 100755 index 7d32da6d..00000000 --- a/web/webqtl/correlation/PartialCorrInputPage.py +++ /dev/null @@ -1,484 +0,0 @@ -# Copyright (C) University of Tennessee Health Science Center, Memphis, TN. -# -# This program is free software: you can redistribute it and/or modify it -# under the terms of the GNU Affero General Public License -# as published by the Free Software Foundation, either version 3 of the -# License, or (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. -# See the GNU Affero General Public License for more details. -# -# 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 -# -# -# -# This module is used by GeneNetwork project (www.genenetwork.org) -# -# Created by GeneNetwork Core Team 2010/08/10 -# -# Last updated by GeneNetwork Core Team 2010/10/20 - -import os -import string -import cPickle - -from htmlgen import HTMLgen2 as HT - -from base import webqtlConfig -from utility.THCell import THCell -from utility.TDCell import TDCell -from base.webqtlTrait import webqtlTrait -from base.templatePage import templatePage -from dbFunction import webqtlDatabaseFunction -from utility import webqtlUtil - - - -class PartialCorrInputPage(templatePage): - - def __init__(self,fd): - - templatePage.__init__(self, fd) - - if not self.openMysql(): - return - - searchResult = fd.formdata.getvalue('searchResult') - - if not searchResult: - heading = 'Partial Correlation' - detail = ['You need to select at least three traits in order to calculate partial correlation.'] - self.error(heading=heading,detail=detail) - return - - - ## Adds the Trait instance for each trait name from the collection - traits = [] - - for item in searchResult: - traits.append(webqtlTrait(fullname=item, cursor=self.cursor)) - - RISet = fd.formdata.getvalue('RISet') - species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, RISet=RISet) - - #XZ: HTML part - TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee') - TD_LR.append("Please select one primary trait, one to three control traits, and at least one target trait.", HT.P() ) - - mainFormName = 'showDatabase' - mainForm = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), name=mainFormName,submit=HT.Input(type='hidden')) - - #XZ: Add hidden form values - hddn = {'FormID':'calPartialCorrTrait', 'database':'', 'ProbeSetID':'', 'CellID':'', #XZ: These four parameters are required by javascript function showDatabase2. - 'controlTraits':'', - 'primaryTrait':'', - 'targetTraits':'', - 'pcMethod':'', - 'RISet':RISet - } - - - for key in hddn.keys(): - mainForm.append(HT.Input(type='hidden', name=key, value=hddn[key])) - - radioNames = [] - - for thisTrait in traits: - oneRadioName = thisTrait.getName() - radioNames.append(oneRadioName) - - radioNamesString = ','.join(radioNames) - - # Creates the image href that runs the javascript setting all traits as target or ignored - setAllTarget = HT.Href(url="#redirect", onClick="setAllAsTarget(document.getElementsByName('showDatabase')[0], '%s');" % radioNamesString) - setAllTargetImg = HT.Image("/images/select_all.gif", alt="Select All", title="Select All", style="border:none;") - setAllTarget.append(setAllTargetImg) - setAllIgnore = HT.Href(url="#redirect", onClick="setAllAsIgnore(document.getElementsByName('showDatabase')[0], '%s');" % radioNamesString) - setAllIgnoreImg = HT.Image("/images/select_all.gif", alt="Select All", title="Select All", style="border:none;") - setAllIgnore.append(setAllIgnoreImg) - - - tblobj = {} - tblobj['header'] = self.getCollectionTableHeader() - - sortby = self.getSortByValue() - - tblobj['body'] = self.getCollectionTableBody(traitList=traits, formName=mainFormName, species=species) - - filename= webqtlUtil.genRandStr("Search_") - - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), Id="sortable") - - mainForm.append(div) - - #XZ: Add button - radioNamesString = ','.join(radioNames) - jsCommand_1 = "validateTrait(this.form, \'" + radioNamesString + "\', 0, 1);" - jsCommand_2 = "validateTrait(this.form, \'" + radioNamesString + "\', 0, 2);" - partialCorrTraitButton_1 = HT.Input(type='button', name='submitPartialCorrTrait_1', value='Pearson\'s r', onClick='%s' % jsCommand_1, Class="button") - partialCorrTraitButton_2 = HT.Input(type='button', name='submitPartialCorrTrait_2', value='Spearman\'s rho', onClick='%s' % jsCommand_2, Class="button") - mainForm.append(HT.BR(), "Compute partial correlation for target selected above:", HT.BR(), partialCorrTraitButton_1, partialCorrTraitButton_2, HT.BR(), HT.BR(), HT.HR(color="gray",size=3) ) - - jsCommand = "validateTrait(this.form, \'" + radioNamesString + "\', 1);" - partialCorrDBButton = HT.Input(type='button', name='submitPartialCorrDB', value='Calculate', onClick='%s' % jsCommand,Class="button") - - methodText = HT.Span("Calculate:", Class="ffl fwb fs12") - - methodMenu = HT.Select(name='method') - methodMenu.append(('Genetic Correlation, Pearson\'s r','1')) - methodMenu.append(('Genetic Correlation, Spearman\'s rho','2')) - methodMenu.append(('SGO Literature Correlation','3')) - methodMenu.append(('Tissue Correlation, Pearson\'s r','4')) - methodMenu.append(('Tissue Correlation, Spearman\'s rho','5')) - - databaseText = HT.Span("Choose Database:", Class="ffl fwb fs12") - databaseMenu = HT.Select(name='database2') - - nmenu = 0 - - self.cursor.execute('SELECT PublishFreeze.FullName,PublishFreeze.Name FROM \ - PublishFreeze,InbredSet WHERE PublishFreeze.InbredSetId = InbredSet.Id \ - and InbredSet.Name = "%s" and PublishFreeze.public > %d' % \ - (RISet,webqtlConfig.PUBLICTHRESH)) - for item in self.cursor.fetchall(): - databaseMenu.append(item) - nmenu += 1 - - self.cursor.execute('SELECT GenoFreeze.FullName,GenoFreeze.Name FROM GenoFreeze,\ - InbredSet WHERE GenoFreeze.InbredSetId = InbredSet.Id and InbredSet.Name = \ - "%s" and GenoFreeze.public > %d' % (RISet,webqtlConfig.PUBLICTHRESH)) - for item in self.cursor.fetchall(): - databaseMenu.append(item) - nmenu += 1 - - #03/09/2009: Xiaodong changed the SQL query to order by Name as requested by Rob. - self.cursor.execute('SELECT Id, Name FROM Tissue order by Name') - for item in self.cursor.fetchall(): - TId, TName = item - databaseMenuSub = HT.Optgroup(label = '%s ------' % TName) - self.cursor.execute('SELECT ProbeSetFreeze.FullName,ProbeSetFreeze.Name FROM ProbeSetFreeze, ProbeFreeze, \ - InbredSet WHERE ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeFreeze.TissueId = %d and \ - ProbeSetFreeze.public > %d and ProbeFreeze.InbredSetId = InbredSet.Id and InbredSet.Name like "%s%%" \ - order by ProbeSetFreeze.CreateTime desc, ProbeSetFreeze.AvgId ' % (TId,webqtlConfig.PUBLICTHRESH, RISet)) - for item2 in self.cursor.fetchall(): - databaseMenuSub.append(item2) - nmenu += 1 - databaseMenu.append(databaseMenuSub) - - if nmenu: - criteriaText = HT.Span("Return:", Class="ffl fwb fs12") - criteriaMenu = HT.Select(name='criteria', selected='500') - criteriaMenu.append(('top 100','100')) - criteriaMenu.append(('top 200','200')) - criteriaMenu.append(('top 500','500')) - criteriaMenu.append(('top 1000','1000')) - criteriaMenu.append(('top 2000','2000')) - criteriaMenu.append(('top 5000','5000')) - criteriaMenu.append(('top 10000','10000')) - criteriaMenu.append(('top 15000','15000')) - criteriaMenu.append(('top 20000','20000')) - - self.MPDCell = HT.TD() - correlationMenus = HT.TableLite( - HT.TR( - HT.TD(databaseText,HT.BR(),databaseMenu, colspan=4) - ), - HT.TR( - HT.TD(methodText,HT.BR(),methodMenu), - self.MPDCell, - HT.TD(criteriaText,HT.BR(),criteriaMenu)), - border=0, cellspacing=4, cellpadding=0) - else: - correlationMenus = "" - - mainForm.append(HT.Font('or',color='red', size=4), HT.BR(), HT.BR(), "Compute partial correlation for each trait in the database selected below:", HT.BR() ) - mainForm.append( partialCorrDBButton, HT.BR(), HT.BR(), correlationMenus) - - TD_LR.append(mainForm) - - self.dict['body'] = str(TD_LR) - self.dict['js1'] ='' - self.dict['title'] = 'Partial Correlation Input' - - - def getCollectionTableHeader(self): - - tblobj_header = [] - - className = "fs13 fwb ffl b1 cw cbrb" - - tblobj_header = [[THCell(HT.TD('Index', Class=className, nowrap="on"), sort=0), - THCell(HT.TD("Primary (X)",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="primary", sort=0), - THCell(HT.TD("Control (Z)",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="control", sort=0), - THCell(HT.TD("Target (Y)",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="target", sort=0), - THCell(HT.TD("Ignored",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="target", sort=0), - THCell(HT.TD('Dataset', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="dataset", idx=1), - THCell(HT.TD('Trait', HT.BR(), 'ID', HT.BR(), valign="top", Class=className, nowrap="on"), text="name", idx=2), - THCell(HT.TD('Description', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="desc", idx=3), - THCell(HT.TD('Location', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="location", idx=4), - THCell(HT.TD('Mean', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="mean", idx=5), - THCell(HT.TD('N', HT.BR(), 'Cases', HT.BR(), valign="top", Class=className, nowrap="on"), text="samples", idx=6), - THCell(HT.TD('Max LRS', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="lrs", idx=7), - THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb', HT.BR(), valign="top", Class=className, nowrap="on"), text="lrs_location", idx=8)]] - - return tblobj_header - - - - def getCollectionTableBody(self, traitList=None, formName=None, species=''): - - tblobj_body = [] - - className = "fs12 fwn b1 c222" - - for thisTrait in traitList: - tr = [] - - if not thisTrait.haveinfo: - thisTrait.retrieveInfo(QTL=1) - - trId = str(thisTrait) - - oneRadioName = thisTrait.getName() - - tr.append(TDCell( HT.TD(' ',align="center",valign="center",Class=className) )) - tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="primary"),align="center",valign="center",Class=className) )) - tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="control"),align="center",valign="center",Class=className) )) - tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="target", checked="true"),align="center",valign="center",Class=className) )) - tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="ignored"),align="center",valign="center",Class=className) )) - - tr.append(TDCell(HT.TD(thisTrait.db.name, Class="fs12 fwn b1 c222"), thisTrait.db.name, thisTrait.db.name.upper())) - - tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showDatabase3('%s','%s','%s','')" % (formName, thisTrait.db.name, thisTrait.name), Class="fs12 fwn"), nowrap="yes",align="left", Class=className -),str(thisTrait.name), thisTrait.name)) - - #description column - if (thisTrait.db.type == "Publish"): - PhenotypeString = thisTrait.post_publication_description - if thisTrait.confidential: - if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users): - PhenotypeString = thisTrait.pre_publication_description - tr.append(TDCell(HT.TD(PhenotypeString, Class=className), PhenotypeString, PhenotypeString.upper())) - elif (thisTrait.db.type == "ProbeSet" or thisTrait.db.type == "Temp"): - description_string = str(thisTrait.description).strip() - if (thisTrait.db.type == "ProbeSet"): - target_string = str(thisTrait.probe_target_description).strip() - - description_display = '' - - if len(description_string) > 1 and description_string != 'None': - description_display = description_string - else: - description_display = thisTrait.symbol - - if len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None': - description_display = description_display + '; ' + target_string.strip() - - description_string = description_display - - tr.append(TDCell(HT.TD(description_string, Class=className), description_string, description_string)) - else: - tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz")) - - #location column - if (thisTrait.db.type == "Publish"): - tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz")) - else: - #ZS: trait_location_value is used for sorting - trait_location_repr = "N/A" - trait_location_value = 1000000 - - if hasattr(thisTrait, 'chr') and hasattr(thisTrait, 'mb') and 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, nowrap="yes", Class=className), trait_location_repr, trait_location_value) ) - - if (thisTrait.db.type == "ProbeSet"): - self.cursor.execute(""" - select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet - where ProbeSetXRef.ProbeSetFreezeId = %d and - ProbeSet.Id = ProbeSetXRef.ProbeSetId and - ProbeSet.Name = '%s' - """ % (thisTrait.db.id, thisTrait.name)) - result = self.cursor.fetchone() - if result: - if result[0]: - mean = result[0] - else: - mean=0 - else: - mean = 0 - - #XZ, 06/05/2009: It is neccessary to turn on nowrap - repr = "%2.3f" % mean - tr.append(TDCell(HT.TD(repr, Class=className, align='right', nowrap='ON'),repr, mean)) - - elif (thisTrait.db.type == "Publish"): - self.cursor.execute(""" - select count(PublishData.value), sum(PublishData.value) from PublishData, PublishXRef, PublishFreeze - where PublishData.Id = PublishXRef.DataId and - PublishXRef.Id = %s and - PublishXRef.InbredSetId = PublishFreeze.InbredSetId and - PublishFreeze.Id = %d - """ % (thisTrait.name, thisTrait.db.id)) - result = self.cursor.fetchone() - - if result: - if result[0] and result[1]: - mean = result[1]/result[0] - else: - mean = 0 - else: - mean = 0 - - repr = "%2.3f" % mean - tr.append(TDCell(HT.TD(repr, Class=className, align='right', nowrap='ON'),repr, mean)) - else: - tr.append(TDCell(HT.TD("--", Class=className, align='left', nowrap='ON'),"--", 0)) - - #Number of cases - n_cases_value = 0 - n_cases_repr = "--" - if (thisTrait.db.type == "Publish"): - self.cursor.execute(""" - select count(PublishData.value) from PublishData, PublishXRef, PublishFreeze - where PublishData.Id = PublishXRef.DataId and - PublishXRef.Id = %s and - PublishXRef.InbredSetId = PublishFreeze.InbredSetId and - PublishFreeze.Id = %d - """ % (thisTrait.name, thisTrait.db.id)) - result = self.cursor.fetchone() - - if result: - if result[0]: - n_cases_value = result[0] - n_cases_repr = result[0] - - if (n_cases_value == "--"): - tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value)) - else: - tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value)) - - elif (thisTrait.db.type == "ProbeSet"): - self.cursor.execute(""" - select count(ProbeSetData.value) from ProbeSet, ProbeSetXRef, ProbeSetData, ProbeSetFreeze - where ProbeSet.Name='%s' and - ProbeSetXRef.ProbeSetId = ProbeSet.Id and - ProbeSetXRef.DataId = ProbeSetData.Id and - ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id and - ProbeSetFreeze.Name = '%s' - """ % (thisTrait.name, thisTrait.db.name)) - result = self.cursor.fetchone() - - if result: - if result[0]: - n_cases_value = result[0] - n_cases_repr = result[0] - if (n_cases_value == "--"): - tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value)) - else: - tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value)) - - elif (thisTrait.db.type == "Geno"): - self.cursor.execute(""" - select count(GenoData.value) from GenoData, GenoXRef, GenoFreeze, Geno, Strain - where Geno.SpeciesId = %s and Geno.Name='%s' and - GenoXRef.GenoId = Geno.Id and - GenoXRef.DataId = GenoData.Id and - GenoXRef.GenoFreezeId = GenoFreeze.Id and - GenoData.StrainId = Strain.Id and - GenoFreeze.Name = '%s' - """ % (webqtlDatabaseFunction.retrieveSpeciesId(self.cursor, thisTrait.db.riset), thisTrait.name, thisTrait.db.name)) - result = self.cursor.fetchone() - - if result: - if result[0]: - n_cases_value = result[0] - n_cases_repr = result[0] - if (n_cases_value == "--"): - tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value)) - else: - tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value)) - - else: - tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value)) - - - if (thisTrait.db.type != "Geno"): - #LRS and its location - LRS_score_repr = '--' - LRS_score_value = 0 - LRS_location_repr = '--' - LRS_location_value = 1000000 - LRS_flag = 1 - - #Max LRS and its Locus location - if hasattr(thisTrait, 'lrs') and hasattr(thisTrait, 'locus') and thisTrait.lrs and thisTrait.locus: - self.cursor.execute(""" - select Geno.Chr, Geno.Mb from Geno, Species - where Species.Name = '%s' and - Geno.Name = '%s' and - Geno.SpeciesId = Species.Id - """ % (species, thisTrait.locus)) - result = self.cursor.fetchone() - - if result: - if result[0] and result[1]: - LRS_Chr = result[0] - LRS_Mb = result[1] - - #XZ: LRS_location_value is used for sorting - try: - LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb) - except: - if LRS_Chr.upper() == 'X': - LRS_location_value = 20*1000 + float(LRS_Mb) - else: - LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb) - - - LRS_score_repr = '%3.1f' % thisTrait.lrs - LRS_score_value = thisTrait.lrs - LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb) ) - LRS_flag = 0 - - tr.append(TDCell(HT.TD(LRS_score_repr, Class=className, align='right', nowrap="on"), LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_location_repr, Class=className), LRS_location_repr, LRS_location_value)) - - if LRS_flag: - tr.append(TDCell(HT.TD(LRS_score_repr, Class=className), LRS_score_repr, LRS_score_value)) - tr.append(TDCell(HT.TD(LRS_location_repr, Class=className), LRS_location_repr, LRS_location_value)) - else: - tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", 0)) - tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", 1000000)) - - tblobj_body.append(tr) - - return tblobj_body - - - - def getSortByValue(self): - - sortby = ("pv", "up") - - return sortby - diff --git a/web/webqtl/correlation/PartialCorrTraitPage.py b/web/webqtl/correlation/PartialCorrTraitPage.py deleted file mode 100755 index 1c79e250..00000000 --- a/web/webqtl/correlation/PartialCorrTraitPage.py +++ /dev/null @@ -1,310 +0,0 @@ -# Copyright (C) University of Tennessee Health Science Center, Memphis, TN. -# -# This program is free software: you can redistribute it and/or modify it -# under the terms of the GNU Affero General Public License -# as published by the Free Software Foundation, either version 3 of the -# License, or (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. -# See the GNU Affero General Public License for more details. -# -# 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 -# -# -# -# This module is used by GeneNetwork project (www.genenetwork.org) -# -# Created by GeneNetwork Core Team 2010/08/10 -# -# Last updated by GeneNetwork Core Team 2010/10/20 - -import string -import cPickle -import os - -from htmlgen import HTMLgen2 as HT - -from base import webqtlConfig -from utility.THCell import THCell -from utility.TDCell import TDCell -from base.webqtlTrait import webqtlTrait -from base.templatePage import templatePage -from utility import webqtlUtil -from CorrelationPage import CorrelationPage -import correlationFunction - - - -class PartialCorrTraitPage(CorrelationPage): - - corrMinInformative = 4 - - - def __init__(self,fd): - - templatePage.__init__(self, fd) - - if not self.openMysql(): - return - - TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee') - - TD_LR.append(HT.Paragraph("Partial Correlation Table", Class="title"), '\n') - - pc_method = fd.formdata.getvalue('pcMethod') - - primaryTraitString = fd.formdata.getvalue('primaryTrait') - primaryTrait = (webqtlTrait(fullname=primaryTraitString, cursor=self.cursor)) - - controlTraitsString = fd.formdata.getvalue('controlTraits') - controlTraitsList = list(string.split(controlTraitsString,',')) - controlTraits = [] - for item in controlTraitsList: - controlTraits.append(webqtlTrait(fullname=item, cursor=self.cursor)) - - targetTraitsString = fd.formdata.getvalue('targetTraits') - targetTraitsList = list(string.split(targetTraitsString,',')) - targetTraits = [] - _targetnames = [] - for item in targetTraitsList: - oneTargetTrait = webqtlTrait(fullname=item, cursor=self.cursor) - oneTargetTrait.retrieveInfo() - targetTraits.append( oneTargetTrait ) - _targetnames.append( oneTargetTrait.name ) - - #XZ: filter out the strains that have no value. - primaryTrait.retrieveData() - _strains, _vals, _vars = primaryTrait.exportInformative() - - #XZ: _controlstrains, _controlvals and _controlvars are list of list [ [], [], ...]. _controlNs is number - _controlstrains,_controlvals,_controlvars,_controlNs = correlationFunction.controlStrains(controlTraitsString,_strains) - - N = len(_strains) - - allsame = True - ##allsame is boolean for whether or not primary and control trait have values for the same strains - for i in _controlstrains: - if _strains != i: - allsame=False - break - - ## If the strains for which each of the control traits and the primary trait have values are not identical, - ## we must remove from the calculation all vlaues for strains that are not present in each. Without doing this, - ## undesirable biases would be introduced. - # XZ, 01/11/2010: After execution of function fixStrains, variables _vals,_controlvals,_vars,_controlvars have the same number and same order of strains as strains in variable _strains. The _controlstrains remains intact. - if not allsame: - _strains,_vals,_controlvals,_vars,_controlvars = correlationFunction.fixStrains(_strains,_controlstrains,_vals,_controlvals,_vars,_controlvars) - N = len(_strains) - - #XZ: We should check the value of control trait and primary trait here. - nameOfIdenticalTraits = correlationFunction.findIdenticalTraits ( _vals, primaryTraitString, _controlvals, controlTraitsList ) - if nameOfIdenticalTraits: - heading = "Partial Correlation Table" - detail = ['%s and %s have same values for the %s strains that will be used to calculate partial correlation (common for all primary and control traits). In such case, partial correlation can NOT be calculated. Please re-select your traits.' % (nameOfIdenticalTraits[0], nameOfIdenticalTraits[1], len(_vals))] - self.error(heading=heading,detail=detail) - return - - - if N < self.corrMinInformative: - heading = "Partial Correlation Table" - detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, fd.RISet)] - self.error(heading=heading,detail=detail) - return - - #XZ, 01/11/2010: Pay attention to the target trait strain number and order! - #XZ 03/29/2010: need to input target trait values to this function. - - _targetvals = [] - for oneTargetTrait in targetTraits: - oneTargetTrait.retrieveData() - oneTraitVals = oneTargetTrait.exportData( _strains ) - _targetvals.append(oneTraitVals) - - - if pc_method == 'spearman': - allcorrelations = correlationFunction.determinePartialsByR(primaryVal = _vals, controlVals = _controlvals, targetVals = _targetvals, targetNames = _targetnames, method='s') - else: - allcorrelations = correlationFunction.determinePartialsByR(primaryVal = _vals, controlVals = _controlvals, targetVals = _targetvals, targetNames = _targetnames) - - totalTraits = len(allcorrelations) - - - info_form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase', submit=HT.Input(type='hidden')) - - hddn = {'FormID':'showDatabase', 'database':'_', 'ProbeSetID':'_', 'CellID':'_' }#XZ: These four parameters are required by javascript function showDatabase2. - - for key in hddn.keys(): - info_form.append(HT.Input(name=key, value=hddn[key], type='hidden')) - - info_form.append(HT.Paragraph("Primary Trait", Class="subtitle"), '\n') - - primaryTraitTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0) - - descriptionString = primaryTrait.genHTML(dispFromDatabase=1) - if primaryTrait.db.type == 'Publish' and primaryTrait.confidential: - descriptionString = primaryTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=primaryTrait.authorized_users) - primaryTraitTable.append(HT.TR(HT.TD(HT.Href(text='%s' % descriptionString, url="javascript:showDatabase2('%s','%s','%s')" % (primaryTrait.db.name,primaryTrait.name,primaryTrait.cellid), Class="fs12 fwn") ))) - - info_form.append(primaryTraitTable) - - info_form.append(HT.Paragraph("Control Traits", Class="subtitle"), '\n') - - controlTraitsTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0) - - seq = 1 - - ## Generate the listing table for control traits - for thisTrait in controlTraits: - descriptionString = thisTrait.genHTML(dispFromDatabase=1) - if thisTrait.db.type == 'Publish' and thisTrait.confidential: - descriptionString = thisTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users) - controlTraitsTable.append(HT.TR(HT.TD("%d."%seq,align="left", width=10), - HT.TD(HT.Href(text='%s' % descriptionString,url="javascript:showDatabase2('%s','%s','%s')" % (thisTrait.db.name,thisTrait.name,thisTrait.cellid), Class="fs12 fwn") ))) - seq += 1 - - info_form.append(controlTraitsTable) - - - TD_LR.append(info_form) - - - mainfmName = webqtlUtil.genRandStr("fm_") - form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden')) - - hddn = {'FormID':'showDatabase', 'database':'_', 'ProbeSetID':'_', 'CellID':'_' }#XZ: These four parameters are required by javascript function showDatabase2. - - for key in hddn.keys(): - form.append(HT.Input(name=key, value=hddn[key], type='hidden')) - - - filename= webqtlUtil.genRandStr("Corr_") - - tblobj = {} - - if pc_method == 'spearman': - tblobj['header'] = \ - [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb"), sort=0), - THCell(HT.TD('Database', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='db', idx=1), - THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='id', idx=2), - THCell(HT.TD('Symbol', Class="fs13 fwb ffl b1 cw cbrb"), text='symbol', idx=3), - THCell(HT.TD('Description', Class="fs13 fwb ffl b1 cw cbrb", align='center'), text='desc', idx=4), - THCell(HT.TD('N ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='nstr', idx=5), - THCell(HT.TD('Partial rho ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='partial_corr', idx=6), - THCell(HT.TD('p(partial rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=7), - THCell(HT.TD('rho ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='corr', idx=8), - THCell(HT.TD('p(rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=9), - THCell(HT.TD('delta rho', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_rho', idx=10)]] - else: - tblobj['header'] = \ - [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb"), sort=0), - THCell(HT.TD('Database', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='db', idx=1), - THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='id', idx=2), - THCell(HT.TD('Symbol', Class="fs13 fwb ffl b1 cw cbrb"), text='symbol', idx=3), - THCell(HT.TD('Description', Class="fs13 fwb ffl b1 cw cbrb", align='center'), text='desc', idx=4), - THCell(HT.TD('N ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='nstr', idx=5), - THCell(HT.TD('Partial r ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='partial_corr', idx=6), - THCell(HT.TD('p(partial r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=7), - THCell(HT.TD('r ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='corr', idx=8), - THCell(HT.TD('p(r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=9), - THCell(HT.TD('delta r', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_r', idx=10)]] - - sortby = ("partial_pv", "up") - - tblobj['body'] = [] - for i, thisTrait in enumerate(targetTraits): - tr = [] - - trId = str(thisTrait) - 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.db.fullname,url=webqtlConfig.INFOPAGEHREF % thisTrait.db.name,target="_blank", Class="fs12 fwn"), Class="fs12 fwn ffl b1 c222"), text=thisTrait.db.name, val=thisTrait -.db.name.upper())) - tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showDatabase3('%s', '%s', '%s', '%s')" % (mainfmName,thisTrait.db.name,thisTrait.name,thisTrait.cellid), Class="fs12 fwn"), Class="fs12 fwn b1 c222"), text=thisTrait.name, val=thisTrait.name)) - - #XZ: Symbol column - if thisTrait.db.type =="ProbeSet": - if thisTrait.symbol: - tr.append(TDCell(HT.TD(thisTrait.symbol, Class="fs12 fwn ffl b1 c222"), text=thisTrait.symbol, val=thisTrait.symbol.upper())) - else: - tr.append(TDCell(HT.TD('NA', Class="fs12 fwn ffl b1 c222"), text='NA', val='NA')) - elif thisTrait.db.type =="Publish": - AbbreviationString = "--" - if (thisTrait.post_publication_abbreviation != None): - AbbreviationString = thisTrait.post_publication_abbreviation - - if thisTrait.confidential: - if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users): - if thisTrait.pre_publication_abbreviation: - AbbreviationString = thisTrait.pre_publication_abbreviation - else: - AbbreviationString = "--" - - if AbbreviationString == "--": - tr.append(TDCell(HT.TD('NA', Class="fs12 fwn ffl b1 c222"), text='NA', val='NA')) - else: - tr.append(TDCell(HT.TD(AbbreviationString, Class="fs12 fwn ffl b1 c222"), text=AbbreviationString, val=AbbreviationString.upper())) - else: - tr.append(TDCell(HT.TD(thisTrait.name, Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name)) - - #XZ: Description column - if thisTrait.db.type =="ProbeSet" or thisTrait.db.type == "Temp": - tr.append(TDCell(HT.TD(thisTrait.description, Class="fs12 fwn ffl b1 c222"), text=thisTrait.description, val=thisTrait.description.upper())) - elif thisTrait.db.type =="Publish": - PhenotypeString = thisTrait.post_publication_description - if thisTrait.confidential: - if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users): - PhenotypeString = thisTrait.pre_publication_description - tr.append(TDCell(HT.TD(PhenotypeString, Class="fs12 fwn ffl b1 c222"), text=PhenotypeString, val=PhenotypeString.upper())) - else: - tr.append(TDCell(HT.TD(thisTrait.name, Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name)) - - tr.append(TDCell(HT.TD(allcorrelations[i][1], Class="fs12 fwn ffl b1 c222", align='right'), text=allcorrelations[i][1], val=allcorrelations[i][1])) - - #partial correlation result - try: - repr = '%3.3f' % float(allcorrelations[i][2]) - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'), text=repr, val=abs(allcorrelations[i][2]))) - except: - repr = 'NA' - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 )) - - repr = webqtlUtil.SciFloat(allcorrelations[i][3]) - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", nowrap='ON', align='right'), text=repr, val=allcorrelations[i][3])) - - #zero order correlation result - repr = '%3.3f' % float(allcorrelations[i][4]) - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'), text=repr, val=abs(allcorrelations[i][4]))) - - repr = webqtlUtil.SciFloat(allcorrelations[i][5]) - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", nowrap='ON', align='right'), text=repr, val=allcorrelations[i][5])) - - #delta - try: - repr = '%3.3f' % ( float(allcorrelations[i][2]) - float(allcorrelations[i][4]) ) - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'), text=repr, val=repr )) - except: - repr = 'NA' - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 )) - - tblobj['body'].append(tr) - - objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb') - cPickle.dump(tblobj, objfile) - objfile.close() - # NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py; - div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), Id="sortable") - form.append(div) - - - TD_LR.append(HT.Center(form),HT.P()) - - self.dict['body'] = str(TD_LR) - # updated by NL, moved js function xmlhttpPost() and updatepage() to dhtml.js - self.dict['js1'] = '' - self.dict['title'] = 'Partial Correlation Result' - diff --git a/web/webqtl/correlation/PlotCorrelationPage.py b/web/webqtl/correlation/PlotCorrelationPage.py deleted file mode 100755 index 23d2ccde..00000000 --- a/web/webqtl/correlation/PlotCorrelationPage.py +++ /dev/null @@ -1,683 +0,0 @@ -# Copyright (C) University of Tennessee Health Science Center, Memphis, TN. -# -# This program is free software: you can redistribute it and/or modify it -# under the terms of the GNU Affero General Public License -# as published by the Free Software Foundation, either version 3 of the -# License, or (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. -# See the GNU Affero General Public License for more details. -# -# 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 -# -# -# -# This module is used by GeneNetwork project (www.genenetwork.org) -# -# Created by GeneNetwork Core Team 2010/08/10 -# -# Last updated by Ning Liu 2011/01/11 - -import string -import piddle as pid -import os - -from htmlgen import HTMLgen2 as HT - -from utility import svg #Code using this module currently commented out -from utility import Plot -from base.webqtlTrait import webqtlTrait -from base.templatePage import templatePage -from utility import webqtlUtil -from base import webqtlConfig -from dbFunction import webqtlDatabaseFunction -from correlation import correlationFunction - -######################################### -# PlotCorrelationPage -######################################### -class PlotCorrelationPage(templatePage): - corrMinInformative = 4 - - def __init__(self, fd): - - templatePage.__init__(self, fd) - - self.initializeDisplayParameters(fd) - - if not fd.genotype: - fd.readGenotype() - - if fd.allstrainlist: - mdpchoice = fd.formdata.getvalue('MDPChoice') - if mdpchoice == "1": - strainlist = fd.f1list + fd.strainlist - elif mdpchoice == "2": - strainlist = [] - strainlist2 = fd.f1list + fd.strainlist - for strain in fd.allstrainlist: - if strain not in strainlist2: - strainlist.append(strain) - #So called MDP Panel - if strainlist: - strainlist = fd.f1list+fd.parlist+strainlist - else: - strainlist = fd.allstrainlist - fd.readData(fd.allstrainlist) - else: - mdpchoice = None - strainlist = fd.strainlist - fd.readData() - - #if fd.allstrainlist: - # fd.readData(fd.allstrainlist) - # strainlist = fd.allstrainlist - #else: - # fd.readData() - # strainlist = fd.strainlist - - - if not self.openMysql(): - return - - isSampleCorr = 0 #XZ: initial value is false - isTissueCorr = 0 #XZ: initial value is false - - #Javascript functions (showCorrelationPlot2, showTissueCorrPlot) have made sure the correlation type is either sample correlation or tissue correlation. - if (self.database and (self.ProbeSetID != 'none')): - isSampleCorr = 1 - elif (self.X_geneSymbol and self.Y_geneSymbol): - isTissueCorr = 1 - else: - heading = "Correlation Type Error" - detail = ["For the input parameters, GN can not recognize the correlation type is sample correlation or tissue correlation."] - self.error(heading=heading,detail=detail) - return - - - TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee', align="left", wrap="off") - - - dataX=[] - dataY=[] - dataZ=[] # shortname - fullTissueName=[] - xlabel = '' - ylabel = '' - - if isTissueCorr: - dataX, dataY, xlabel, ylabel, dataZ, fullTissueName = self.getTissueLabelsValues(X_geneSymbol=self.X_geneSymbol, Y_geneSymbol=self.Y_geneSymbol, TissueProbeSetFreezeId=self.TissueProbeSetFreezeId) - plotHeading = HT.Paragraph('Tissue Correlation Scatterplot') - plotHeading.__setattr__("class","title") - - if isSampleCorr: - plotHeading = HT.Paragraph('Sample Correlation Scatterplot') - plotHeading.__setattr__("class","title") - - #XZ: retrieve trait 1 info, Y axis - trait1_data = [] #trait 1 data - trait1Url = '' - - try: - Trait1 = webqtlTrait(db=self.database, name=self.ProbeSetID, cellid=self.CellID, cursor=self.cursor) - Trait1.retrieveInfo() - Trait1.retrieveData() - except: - heading = "Retrieve Data" - detail = ["The database you just requested has not been established yet."] - self.error(heading=heading,detail=detail) - return - - trait1_data = Trait1.exportData(strainlist) - if Trait1.db.type == 'Publish' and Trait1.confidential: - trait1Url = Trait1.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=Trait1.authorized_users) - else: - trait1Url = Trait1.genHTML(dispFromDatabase=1) - ylabel = '%s : %s' % (Trait1.db.shortname, Trait1.name) - if Trait1.cellid: - ylabel += ' : ' + Trait1.cellid - - - #XZ, retrieve trait 2 info, X axis - traitdata2 = [] #trait 2 data - _vals = [] #trait 2 data - trait2Url = '' - - if ( self.database2 and (self.ProbeSetID2 != 'none') ): - try: - Trait2 = webqtlTrait(db=self.database2, name=self.ProbeSetID2, cellid=self.CellID2, cursor=self.cursor) - Trait2.retrieveInfo() - Trait2.retrieveData() - except: - heading = "Retrieve Data" - detail = ["The database you just requested has not been established yet."] - self.error(heading=heading,detail=detail) - return - - if Trait2.db.type == 'Publish' and Trait2.confidential: - trait2Url = Trait2.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=Trait2.authorized_users) - else: - trait2Url = Trait2.genHTML(dispFromDatabase=1) - traitdata2 = Trait2.exportData(strainlist) - _vals = traitdata2[:] - xlabel = '%s : %s' % (Trait2.db.shortname, Trait2.name) - if Trait2.cellid: - xlabel += ' : ' + Trait2.cellid - else: - for item in strainlist: - if fd.allTraitData.has_key(item): - _vals.append(fd.allTraitData[item].val) - else: - _vals.append(None) - - if fd.identification: - xlabel = fd.identification - else: - xlabel = "User Input Data" - - try: - Trait2 = webqtlTrait(fullname=fd.formdata.getvalue('fullname'), cursor=self.cursor) - trait2Url = Trait2.genHTML(dispFromDatabase=1) - except: - trait2Url = xlabel - - if (_vals and trait1_data): - if len(_vals) != len(trait1_data): - errors = HT.Blockquote(HT.Font('Error: ',color='red'),HT.Font('The number of traits are inconsistent, Program quit',color='black')) - errors.__setattr__("class","subtitle") - TD_LR.append(errors) - self.dict['body'] = str(TD_LR) - return - - for i in range(len(_vals)): - if _vals[i]!= None and trait1_data[i]!= None: - dataX.append(_vals[i]) - dataY.append(trait1_data[i]) - strainName = strainlist[i] - if self.showstrains: - dataZ.append(webqtlUtil.genShortStrainName(RISet=fd.RISet, input_strainName=strainName)) - else: - heading = "Correlation Plot" - detail = ['Empty Dataset for sample correlation, please check your data.'] - self.error(heading=heading,detail=detail) - return - - - #XZ: We have gotten all data for both traits. - if len(dataX) >= self.corrMinInformative: - - if self.rankOrder == 0: - rankPrimary = 0 - rankSecondary = 1 - else: - rankPrimary = 1 - rankSecondary = 0 - - lineColor = self.setLineColor(); - symbolColor = self.setSymbolColor(); - idColor = self.setIdColor(); - - c = pid.PILCanvas(size=(self.plotSize, self.plotSize*0.90)) - data_coordinate = Plot.plotXY(canvas=c, dataX=dataX, dataY=dataY, rank=rankPrimary, dataLabel = dataZ, labelColor=pid.black, lineSize=self.lineSize, lineColor=lineColor, idColor=idColor, idFont=self.idFont, idSize=self.idSize, symbolColor=symbolColor, symbolType=self.symbol, filled=self.filled, symbolSize=self.symbolSize, XLabel=xlabel, connectdot=0, YLabel=ylabel, title='', fitcurve=self.showline, displayR =1, offset= (90, self.plotSize/20, self.plotSize/10, 90), showLabel = self.showIdentifiers) - - if rankPrimary == 1: - dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX)) - else: - dataXlabel, dataYlabel = dataX, dataY - - gifmap1 = HT.Map(name='CorrelationPlotImageMap1') - - for i, item in enumerate(data_coordinate): - one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 5, item[1] - 5, item[0] + 5, item[1] + 5) - if isTissueCorr: - one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i]) - else: - one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i]) - gifmap1.areas.append(HT.Area(shape='rect',coords=one_rect_coordinate, title=one_rect_title) ) - - filename= webqtlUtil.genRandStr("XY_") - c.save(webqtlConfig.IMGDIR+filename, format='gif') - img1=HT.Image('/image/'+filename+'.gif',border=0, usemap='#CorrelationPlotImageMap1') - - mainForm_1 = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase', submit=HT.Input(type='hidden')) - hddn = {'FormID':'showDatabase','ProbeSetID':'_','database':'_','CellID':'_','RISet':fd.RISet, 'ProbeSetID2':'_', 'database2':'_', 'CellID2':'_', 'allstrainlist':string.join(fd.strainlist, " "), 'traitList': fd.formdata.getvalue("traitList")} - if fd.incparentsf1: - hddn['incparentsf1'] = 'ON' - for key in hddn.keys(): - mainForm_1.append(HT.Input(name=key, value=hddn[key], type='hidden')) - - if isSampleCorr: - mainForm_1.append(HT.P(), HT.Blockquote(HT.Strong('X axis:'),HT.Blockquote(trait2Url),HT.Strong('Y axis:'),HT.Blockquote(trait1Url), style='width: %spx;' % self.plotSize, wrap="hard")) - - graphForm = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), name='MDP_Form',submit=HT.Input(type='hidden')) - graph_hddn = self.setHiddenParameters(fd, rankPrimary) - webqtlUtil.exportData(graph_hddn, fd.allTraitData) #XZ: This is necessary to replot with different groups of strains - - for key in graph_hddn.keys(): - graphForm.append(HT.Input(name=key, value=graph_hddn[key], type='hidden')) - - options = self.createOptionsMenu(fd, mdpchoice) - - if (self.showOptions == '0'): - showOptionsButton = HT.Input(type='button' ,name='optionsButton',value='Hide Options', onClick="showHideOptions();", Class="button") - else: - showOptionsButton = HT.Input(type='button' ,name='optionsButton',value='Show Options', onClick="showHideOptions();", Class="button") - - # updated by NL: 12-07-2011 add variables for tissue abbreviation page - if isTissueCorr: - graphForm.append(HT.Input(name='shortTissueName', value='', type='hidden')) - graphForm.append(HT.Input(name='fullTissueName', value='', type='hidden')) - shortTissueNameStr=string.join(dataZ, ",") - fullTissueNameStr=string.join(fullTissueName, ",") - - tissueAbbrButton=HT.Input(type='button' ,name='tissueAbbrButton',value='Show Abbreviations', onClick="showTissueAbbr('MDP_Form','%s','%s')" % (shortTissueNameStr,fullTissueNameStr), Class="button") - graphForm.append(showOptionsButton,' ',tissueAbbrButton, HT.BR(), HT.BR()) - else: - graphForm.append(showOptionsButton, HT.BR(), HT.BR()) - - graphForm.append(options, HT.BR()) - graphForm.append(HT.HR(), HT.BR(), HT.P()) - - TD_LR.append(plotHeading, HT.BR(),graphForm, HT.BR(), gifmap1, HT.P(), img1, HT.P(), mainForm_1) - TD_LR.append(HT.BR(), HT.HR(color="grey", size=5, width="100%")) - - - - c = pid.PILCanvas(size=(self.plotSize, self.plotSize*0.90)) - data_coordinate = Plot.plotXY(canvas=c, dataX=dataX, dataY=dataY, rank=rankSecondary, dataLabel = dataZ, labelColor=pid.black,lineColor=lineColor, lineSize=self.lineSize, idColor=idColor, idFont=self.idFont, idSize=self.idSize, symbolColor=symbolColor, symbolType=self.symbol, filled=self.filled, symbolSize=self.symbolSize, XLabel=xlabel, connectdot=0, YLabel=ylabel,title='', fitcurve=self.showline, displayR =1, offset= (90, self.plotSize/20, self.plotSize/10, 90), showLabel = self.showIdentifiers) - - if rankSecondary == 1: - dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX)) - else: - dataXlabel, dataYlabel = dataX, dataY - - gifmap2 = HT.Map(name='CorrelationPlotImageMap2') - - for i, item in enumerate(data_coordinate): - one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 6, item[1] - 6, item[0] + 6, item[1] + 6) - if isTissueCorr: - one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i]) - else: - one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i]) - - gifmap2.areas.append(HT.Area(shape='rect',coords=one_rect_coordinate, title=one_rect_title) ) - - filename= webqtlUtil.genRandStr("XY_") - c.save(webqtlConfig.IMGDIR+filename, format='gif') - img2=HT.Image('/image/'+filename+'.gif',border=0, usemap='#CorrelationPlotImageMap2') - - mainForm_2 = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase2', submit=HT.Input(type='hidden')) - hddn = {'FormID':'showDatabase2','ProbeSetID':'_','database':'_','CellID':'_','RISet':fd.RISet, 'ProbeSetID2':'_', 'database2':'_', 'CellID2':'_', 'allstrainlist':string.join(fd.strainlist, " "), 'traitList': fd.formdata.getvalue("traitList")} - if fd.incparentsf1: - hddn['incparentsf1'] = 'ON' - for key in hddn.keys(): - mainForm_2.append(HT.Input(name=key, value=hddn[key], type='hidden')) - - if isSampleCorr: - mainForm_2.append(HT.P(), HT.Blockquote(HT.Strong('X axis:'),HT.Blockquote(trait2Url),HT.Strong('Y axis:'),HT.Blockquote(trait1Url), style='width:%spx;' % self.plotSize)) - - - TD_LR.append(HT.BR(), HT.P()) - TD_LR.append('\n', gifmap2, HT.P(), HT.P(), img2, HT.P(), mainForm_2) - - self.dict['body'] = str(TD_LR) - else: - heading = "Correlation Plot" - detail = ['Fewer than %d strain data were entered for %s data set. No statitical analysis has been attempted.' % (self.corrMinInformative, fd.RISet)] - self.error(heading=heading,detail=detail) - return - - - - def initializeDisplayParameters(self, fd): - """ - Initializes all of the PlotCorrelationPage class parameters, - acquiring most values from the formdata (fd) - """ - - rankOrderString = fd.formdata.getvalue('rankOrder') - if rankOrderString == "1": - self.rankOrder = 1 - else: - self.rankOrder = 0 - - self.dict['title'] = 'Correlation X-Y Scatterplot' - focusScript = "onLoad=\"document.getElementsByName('plotSize')[0].focus();\";" - self.dict['js2'] = focusScript - - self.showstrains = fd.formdata.getvalue('ShowStrains') - self.showline = fd.formdata.getvalue('ShowLine') - self.X_geneSymbol = fd.formdata.getvalue('X_geneSymbol','') - self.Y_geneSymbol = fd.formdata.getvalue('Y_geneSymbol','') - self.TissueProbeSetFreezeId = fd.formdata.getvalue('TissueProbeSetFreezeId', '1') - - self.symbolColor = fd.formdata.getvalue('symbolColor', 'black') - self.symbol = fd.formdata.getvalue('symbol', 'circle') - self.filled = fd.formdata.getvalue('filled', 'yes') - self.symbolSize = fd.formdata.getvalue('symbolSize', 'tiny') - self.idColor = fd.formdata.getvalue('idColor', 'blue') - self.idFont = fd.formdata.getvalue('idFont', 'arial') - self.idSize = fd.formdata.getvalue('idSize', '14') - self.lineColor = fd.formdata.getvalue('lineColor', 'grey') - self.lineSize = fd.formdata.getvalue('lineSize', 'medium') - self.showOptions = fd.formdata.getvalue('showOptions', '0') - - try: - self.plotSize = int(fd.formdata.getvalue('plotSize', 900)) - except: - self.plotSize = 900 - try: - self.showIdentifiers = int(fd.formdata.getvalue('showIdentifiers', 1)) - except: - self.showIdentifiers = 1 - - self.database = fd.formdata.getvalue('database') - self.ProbeSetID = fd.formdata.getvalue('ProbeSetID', 'none') - self.CellID = fd.formdata.getvalue('CellID') - - self.database2 = fd.formdata.getvalue('database2') - self.ProbeSetID2 = fd.formdata.getvalue('ProbeSetID2', 'none') - self.CellID2 = fd.formdata.getvalue('CellID2') - - def createOptionsMenu(self, fd, mdpchoice): - """ - Create all the HTML for the options menu; the first if/else statements - determine whether the Div container holding all the other html is visible - or not. - """ - - if (self.showOptions == '0'): - options = HT.Div(name="options", id="options", style="display: none") - self.showOptions = '1' - else: - options = HT.Div(name="options", id="options", style="display: ''") - self.showOptions = '0' - - if self.showIdentifiers: - containerTable = HT.TableLite(cellspacing=1, width=730, height=150, border=1) - else: - containerTable = HT.TableLite(cellspacing=1, width=730, height=130, border=1) - - if self.showIdentifiers: - containerTable = HT.TableLite(cellspacing=1, width=730, height=150, border=1) - else: - containerTable = HT.TableLite(cellspacing=1, width=730, height=130, border=1) - - containerRow = HT.TR() - containerCell = HT.TD(valign="middle", align="center") - - optionsTable = HT.TableLite(Class="collap", cellspacing=2, width=700) - - sizeOptions = HT.TR(align="right") - tagOptions = HT.TR(align="right") - markerOptions = HT.TR(align="right") - lineOptions = HT.TR(align="right") - replot_mdpOptions = HT.TR(align="right") - - sizeOptions.append(HT.TD(HT.Bold("Size: "), " "*1, HT.Input(type='text' ,name='plotSize', value=self.plotSize, style="background-color: #FFFFFF; width: 50px;", onChange="checkWidth();"), align="left")) - - idColorSel = HT.Select(name="idColorSel", onChange="changeIdColor(); submit();", selected=self.idColor) - idColorSel.append(("blue", "blue")) - idColorSel.append(("green", "green")) - idColorSel.append(("red", "red")) - idColorSel.append(("yellow", "yellow")) - idColorSel.append(("white", "white")) - idColorSel.append(("purple", "purple")) - idColorSel.append(("brown", "brown")) - idColorSel.append(("grey", "grey")) - idColorSel.append(("black","black")) - - idFontSel = HT.Select(name="idFontSel", onChange="changeIdFont(); submit();", selected=self.idFont) - idFontSel.append(("Arial", "arial")) - idFontSel.append(("Trebuchet", "trebuc")) - idFontSel.append(("Verdana", "verdana")) - idFontSel.append(("Georgia", "Georgia")) - idFontSel.append(("Courier", "cour")) - - idSizeSel = HT.Select(name="idSizeSel", onChange="changeIdSize(); submit();", selected=self.idSize) - idSizeSel.append(("10", "10")) - idSizeSel.append(("12", "12")) - idSizeSel.append(("14", "14")) - idSizeSel.append(("16", "16")) - idSizeSel.append(("18", "18")) - - if self.showIdentifiers: - tagButton = HT.TD(HT.Input(type='button' ,name='',value=' Hide Tags ',onClick="this.form.showIdentifiers.value=0;submit();", Class="button"), align="right") - else: - tagButton = HT.TD(HT.Input(type='button' ,name='',value=' Show Tags ',onClick="this.form.showIdentifiers.value=1;submit();", Class="button"), align="right") - - tagOptions.append(HT.TD(HT.Text(HT.Bold("Tag Settings: ")), align="left")) - tagOptions.append(HT.TD(HT.Text(text="Font: "), idFontSel)) - tagOptions.append(HT.TD(HT.Text(text="Color: "), idColorSel)) - tagOptions.append(HT.TD(HT.Text(text="Point: "), idSizeSel)) - tagOptions.append(tagButton) - optionsTable.append(sizeOptions, tagOptions) - - if fd.allstrainlist and mdpchoice: - allStrainList = HT.Input(name='allstrainlist', value=string.join(fd.allstrainlist, " "), type='hidden') - mdpChoice = HT.Input(name='MDPChoice', value=mdpchoice, type='hidden') - btn0 = HT.Input(type='button' ,name='',value='All Cases',onClick="this.form.MDPChoice.value=0;submit();", Class="button") - btn1 = HT.Input(type='button' ,name='',value='%s Only' % fd.RISet,onClick="this.form.MDPChoice.value=1;submit();", Class="button") - btn2 = HT.Input(type='button' ,name='',value='MDP Only', onClick="this.form.MDPChoice.value=2;submit();", Class="button") - - - colorSel = HT.Select(name="colorSel", onChange="changeSymbolColor(); submit();", selected=self.symbolColor) - colorSel.append(("red", "red")) - colorSel.append(("green", "green")) - colorSel.append(("blue", "blue")) - colorSel.append(("yellow", "yellow")) - colorSel.append(("purple", "purple")) - colorSel.append(("brown", "brown")) - colorSel.append(("grey", "grey")) - colorSel.append(("black","black")) - - symbolSel = HT.Select(name="symbolSel", onChange="changeSymbol(); submit();", selected=self.symbol) - symbolSel.append(("4-star","4-star")) - symbolSel.append(("3-star","3-star")) - symbolSel.append(("cross", "cross")) - symbolSel.append(("circle","circle")) - symbolSel.append(("diamond", "diamond")) - symbolSel.append(("square", "square")) - symbolSel.append(("vert rect", "vertRect")) - symbolSel.append(("hori rect", "horiRect")) - - sizeSel = HT.Select(name="sizeSel", onChange="changeSize(); submit();", selected=self.symbolSize) - sizeSel.append(("tiny","tiny")) - sizeSel.append(("small","small")) - sizeSel.append(("medium","medium")) - sizeSel.append(("large","large")) - - fillSel = HT.Select(name="fillSel", onChange="changeFilled(); submit();", selected=self.filled) - fillSel.append(("no","no")) - fillSel.append(("yes","yes")) - - lineColorSel = HT.Select(name="lineColorSel", onChange="changeLineColor(); submit();", selected=self.lineColor) - lineColorSel.append(("red", "red")) - lineColorSel.append(("green", "green")) - lineColorSel.append(("blue", "blue")) - lineColorSel.append(("yellow", "yellow")) - lineColorSel.append(("purple", "purple")) - lineColorSel.append(("brown", "brown")) - lineColorSel.append(("grey", "grey")) - lineColorSel.append(("black","black")) - - lineSizeSel = HT.Select(name="lineSizeSel", onChange="changeLineSize(); submit();", selected=self.lineSize) - lineSizeSel.append(("thin", "thin")) - lineSizeSel.append(("medium", "medium")) - lineSizeSel.append(("thick", "thick")) - - - markerOptions.append(HT.TD(HT.Text(HT.Bold("Marker Settings: ")), align="left")) - markerOptions.append(HT.TD(HT.Text(text="Marker: "), symbolSel)) - markerOptions.append(HT.TD(HT.Text(text="Color: "), colorSel)) - markerOptions.append(HT.TD(HT.Text(text="Fill: "), fillSel)) - markerOptions.append(HT.TD(HT.Text(text="Size: "), sizeSel)) - - lineOptions.append(HT.TD(HT.Text(HT.Bold("Line Settings: ")), align="left")) - lineOptions.append(HT.TD(HT.Text(text="Width: "), lineSizeSel)) - lineOptions.append(HT.TD(HT.Text(text="Color: "), lineColorSel)) - - replotButton = HT.Input(type='button', name='', value=' Replot ',onClick="checkWidth(); submit();", Class="button") - - if fd.allstrainlist and mdpchoice: - replot_mdpOptions.append(HT.TD(replotButton, align="left"), HT.TD(allStrainList, mdpChoice, btn0, btn1, btn2, align="center", colspan=3)) - optionsTable.append(markerOptions, lineOptions, HT.TR(HT.TD(HT.BR())), replot_mdpOptions ) - else: - replot_mdpOptions.append(HT.TD(replotButton, align="left")) - optionsTable.append(markerOptions, lineOptions, HT.TR(HT.TD(HT.BR())), replot_mdpOptions) - - containerCell.append(optionsTable) - containerRow.append(containerCell) - containerTable.append(containerRow) - - options.append(containerTable) - - return options - - def setHiddenParameters(self, fd, rankPrimary): - """ - Create the dictionary of hidden form parameters from PlotCorrelationPage's class parameters - """ - - graph_hddn = {'FormID':'showCorrelationPlot','RISet':fd.RISet, 'identification':fd.identification, "incparentsf1":1, "showIdentifiers":self.showIdentifiers} - - if self.database: graph_hddn['database']=self.database - if self.ProbeSetID: graph_hddn['ProbeSetID']=self.ProbeSetID - if self.CellID: graph_hddn['CellID']=self.CellID - if self.database2: graph_hddn['database2']=self.database2 - if self.ProbeSetID2: graph_hddn['ProbeSetID2']=self.ProbeSetID2 - if self.CellID2: graph_hddn['CellID2']=self.CellID2 - if self.showstrains: graph_hddn['ShowStrains']=self.showstrains - if self.showline: graph_hddn['ShowLine']=self.showline - if self.X_geneSymbol: graph_hddn['X_geneSymbol']=self.X_geneSymbol - if self.Y_geneSymbol: graph_hddn['Y_geneSymbol']=self.Y_geneSymbol - if self.TissueProbeSetFreezeId: graph_hddn['TissueProbeSetFreezeId']=self.TissueProbeSetFreezeId - if self.rankOrder: graph_hddn['rankOrder'] = rankPrimary - if fd.formdata.getvalue('fullname'): graph_hddn['fullname']=fd.formdata.getvalue('fullname') - if self.lineColor: graph_hddn['lineColor'] = self.lineColor - if self.lineSize: graph_hddn['lineSize'] = self.lineSize - if self.idColor: graph_hddn['idColor'] = self.idColor - if self.idFont: graph_hddn['idFont'] = self.idFont - if self.idSize: graph_hddn['idSize'] = self.idSize - if self.symbolColor: graph_hddn['symbolColor'] = self.symbolColor - if self.symbol: graph_hddn['symbol'] = self.symbol - if self.filled: graph_hddn['filled'] = self.filled - if self.symbolSize: graph_hddn['symbolSize'] = self.symbolSize - if self.showOptions: graph_hddn['showOptions'] = self.showOptions - - return graph_hddn - - def setIdColor(self): - """ - Set the plot tag/ID color based upon the value of the idColor class parameter - """ - - if self.idColor == 'black': - idColor = pid.black - elif self.idColor == 'white': - idColor = pid.white - elif self.idColor == 'yellow': - idColor = pid.yellow - elif self.idColor == 'grey': - idColor = pid.grey - elif self.idColor == 'blue': - idColor = pid.blue - elif self.idColor == 'purple': - idColor = pid.purple - elif self.idColor == 'brown': - idColor = pid.brown - elif self.idColor == 'green': - idColor = pid.green - else: - idColor = pid.red - - return idColor - - def setSymbolColor(self): - """ - Set the plot symbol color based upon the value of the symbolColor class parameter - """ - - if self.symbolColor == 'black': - symbolColor = pid.black - elif self.symbolColor == 'grey': - symbolColor = pid.grey - elif self.symbolColor == 'yellow': - symbolColor = pid.yellow - elif self.symbolColor == 'blue': - symbolColor = pid.blue - elif self.symbolColor == 'purple': - symbolColor = pid.purple - elif self.symbolColor == 'brown': - symbolColor = pid.brown - elif self.symbolColor== 'green': - symbolColor = pid.green - else: - symbolColor = pid.red - - return symbolColor - - def setLineColor(self): - """ - Set the plot line color based upon the lineColor class parameter - """ - - if self.lineColor == 'black': - lineColor = pid.black - elif self.lineColor == 'grey': - lineColor = pid.grey - elif self.lineColor == 'yellow': - lineColor = pid.yellow - elif self.lineColor == 'blue': - lineColor = pid.blue - elif self.lineColor == 'purple': - lineColor = pid.purple - elif self.lineColor == 'brown': - lineColor = pid.brown - elif self.lineColor== 'green': - lineColor = pid.green - else: - lineColor = pid.red - - return lineColor - - - def getTissueLabelsValues(self, X_geneSymbol=None, Y_geneSymbol=None, TissueProbeSetFreezeId=None ): - - dataX = [] - dataY = [] - data_fullLabel = [] - data_shortLabel = [] - # updated by NL, 2011-01-11 using new function getTissueProbeSetXRefInfo to get dataId value - X_symbolList,X_geneIdDict,X_dataIdDict,X_ChrDict,X_MbDict,X_descDict,X_pTargetDescDict = correlationFunction.getTissueProbeSetXRefInfo(cursor=self.cursor,GeneNameLst=[X_geneSymbol],TissueProbeSetFreezeId=TissueProbeSetFreezeId) - Y_symbolList,Y_geneIdDict,Y_dataIdDict,Y_ChrDict,Y_MbDict,Y_descDict,Y_pTargetDescDict = correlationFunction.getTissueProbeSetXRefInfo(cursor=self.cursor,GeneNameLst=[Y_geneSymbol],TissueProbeSetFreezeId=TissueProbeSetFreezeId) - # in dataIdDict, key is the lower cased geneSymbol - X_DataId = X_dataIdDict[X_geneSymbol.lower()] - Y_DataId = Y_dataIdDict[Y_geneSymbol.lower()] - - self.cursor.execute("SELECT TissueID,value FROM TissueProbeSetData WHERE Id = %d ORDER BY TissueID" % int(X_DataId) ) - results = self.cursor.fetchall() - for item in results: - TissueID, Value = item - dataX.append(Value) - self.cursor.execute("SELECT Tissue.Name, Tissue.Short_Name FROM Tissue WHERE Id = %d" % int(TissueID) ) - temp = self.cursor.fetchone() - data_fullLabel.append( temp[0] ) - data_shortLabel.append( temp[1] ) - - self.cursor.execute("SELECT TissueID,value FROM TissueProbeSetData WHERE Id = %d ORDER BY TissueID" % int(Y_DataId) ) - results = self.cursor.fetchall() - for item in results: - TissueID, Value = item - dataY.append(Value) - - X_label = "%s" % X_geneSymbol - Y_label = "%s" % Y_geneSymbol - - return dataX, dataY, X_label, Y_label, data_shortLabel, data_fullLabel diff --git a/web/webqtl/correlation/__init__.py b/web/webqtl/correlation/__init__.py deleted file mode 100755 index e69de29b..00000000 --- a/web/webqtl/correlation/__init__.py +++ /dev/null diff --git a/web/webqtl/correlation/correlationFunction.py b/web/webqtl/correlation/correlationFunction.py deleted file mode 100755 index cc19f54e..00000000 --- a/web/webqtl/correlation/correlationFunction.py +++ /dev/null @@ -1,923 +0,0 @@ -# Copyright (C) University of Tennessee Health Science Center, Memphis, TN. -# -# This program is free software: you can redistribute it and/or modify it -# under the terms of the GNU Affero General Public License -# as published by the Free Software Foundation, either version 3 of the -# License, or (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. -# See the GNU Affero General Public License for more details. -# -# 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 -# -# -# -# This module is used by GeneNetwork project (www.genenetwork.org) -# -# Created by GeneNetwork Core Team 2010/08/10 -# -# Last updated by NL 2011/03/23 - - -import math -import rpy2.robjects -import pp -import string - -from utility import webqtlUtil -from base.webqtlTrait import webqtlTrait -from dbFunction import webqtlDatabaseFunction - - - -#XZ: The input 'controls' is String. It contains the full name of control traits. -#XZ: The input variable 'strainlst' is List. It contains the strain names of primary trait. -#XZ: The returned tcstrains is the list of list [[],[]...]. So are tcvals and tcvars. The last returned parameter is list of numbers. -#XZ, 03/29/2010: For each returned control trait, there is no None value in it. -def controlStrains(controls, strainlst): - - controls = controls.split(',') - - cvals = {} - for oneTraitName in controls: - oneTrait = webqtlTrait(fullname=oneTraitName, cursor=webqtlDatabaseFunction.getCursor() ) - oneTrait.retrieveData() - cvals[oneTraitName] = oneTrait.data - - tcstrains = [] - tcvals = [] - tcvars = [] - - for oneTraitName in controls: - strains = [] - vals = [] - vars = [] - - for _strain in strainlst: - if cvals[oneTraitName].has_key(_strain): - _val = cvals[oneTraitName][_strain].val - if _val != None: - strains.append(_strain) - vals.append(_val) - vars.append(None) - - tcstrains.append(strains) - tcvals.append(vals) - tcvars.append(vars) - - return tcstrains, tcvals, tcvars, [len(x) for x in tcstrains] - - - -#XZ, 03/29/2010: After execution of functon "controlStrains" and "fixStrains", primary trait and control traits have the same strains and in the same order. There is no 'None' value in them. -def fixStrains(_strains,_controlstrains,_vals,_controlvals,_vars,_controlvars): - """Corrects strains, vals, and vars so that all contrain only those strains common - to the reference trait and all control traits.""" - - def dictify(strains,vals,vars): - subdict = {} - for i in xrange(len(strains)): - subdict[strains[i]] = (vals[i],vars[i]) - return subdict - - #XZ: The 'dicts' is a list of dictionary. The first element is the dictionary of reference trait. The rest elements are for control traits. - dicts = [] - dicts.append(dictify(_strains,_vals,_vars)) - - nCstrains = len(_controlstrains) - for i in xrange(nCstrains): - dicts.append(dictify(_controlstrains[i],_controlvals[i],_controlvars[i])) - - _newstrains = [] - _vals = [] - _vars = [] - _controlvals = [[] for x in xrange(nCstrains)] - _controlvars = [[] for x in xrange(nCstrains)] - - for strain in _strains: - inall = True - for d in dicts: - if strain not in d: - inall = False - break - if inall: - _newstrains.append(strain) - _vals.append(dicts[0][strain][0]) - _vars.append(dicts[0][strain][1]) - for i in xrange(nCstrains): - _controlvals[i].append(dicts[i+1][strain][0]) - _controlvars[i].append(dicts[i+1][strain][1]) - - return _newstrains, _vals, _controlvals, _vars, _controlvars - - -#XZ, 6/15/2010: If there is no identical control traits, the returned list is empty. -#else, the returned list has two elements of control trait name. -def findIdenticalControlTraits ( controlVals, controlNames ): - nameOfIdenticalTraits = [] - - controlTraitNumber = len(controlVals) - - if controlTraitNumber > 1: - - #XZ: reset the precision of values and convert to string type - for oneTraitVal in controlVals: - for oneStrainVal in oneTraitVal: - oneStrainVal = '%.3f' % oneStrainVal - - for i, oneTraitVal in enumerate( controlVals ): - for j in range(i+1, controlTraitNumber): - if oneTraitVal == controlVals[j]: - nameOfIdenticalTraits.append(controlNames[i]) - nameOfIdenticalTraits.append(controlNames[j]) - - return nameOfIdenticalTraits - -#XZ, 6/15/2010: If there is no identical control traits, the returned list is empty. -#else, the returned list has two elements of control trait name. -#primaryVal is of list type. It contains value of primary trait. -#primaryName is of string type. -#controlVals is of list type. Each element is list too. Each element contain value of one control trait. -#controlNames is of list type. -def findIdenticalTraits (primaryVal, primaryName, controlVals, controlNames ): - nameOfIdenticalTraits = [] - - #XZ: reset the precision of values and convert to string type - for oneStrainVal in primaryVal: - oneStrainVal = '%.3f' % oneStrainVal - - for oneTraitVal in controlVals: - for oneStrainVal in oneTraitVal: - oneStrainVal = '%.3f' % oneStrainVal - - controlTraitNumber = len(controlVals) - - if controlTraitNumber > 1: - for i, oneTraitVal in enumerate( controlVals ): - for j in range(i+1, controlTraitNumber): - if oneTraitVal == controlVals[j]: - nameOfIdenticalTraits.append(controlNames[i]) - nameOfIdenticalTraits.append(controlNames[j]) - break - - if len(nameOfIdenticalTraits) == 0: - for i, oneTraitVal in enumerate( controlVals ): - if primaryVal == oneTraitVal: - nameOfIdenticalTraits.append(primaryName) - nameOfIdenticalTraits.append(controlNames[i]) - break - - return nameOfIdenticalTraits - - - -#XZ, 03/29/2010: The strains in primaryVal, controlVals, targetVals must be of the same number and in same order. -#XZ: No value in primaryVal and controlVals could be None. - -def determinePartialsByR (primaryVal, controlVals, targetVals, targetNames, method='p'): - - def compute_partial ( primaryVal, controlVals, targetVals, targetNames, method ): - - rpy2.robjects.r(""" -pcor.test <- function(x,y,z,use="mat",method="p",na.rm=T){ - # The partial correlation coefficient between x and y given z - # - # pcor.test is free and comes with ABSOLUTELY NO WARRANTY. - # - # x and y should be vectors - # - # z can be either a vector or a matrix - # - # use: There are two methods to calculate the partial correlation coefficient. - # One is by using variance-covariance matrix ("mat") and the other is by using recursive formula ("rec"). - # Default is "mat". - # - # method: There are three ways to calculate the correlation coefficient, - # which are Pearson's ("p"), Spearman's ("s"), and Kendall's ("k") methods. - # The last two methods which are Spearman's and Kendall's coefficient are based on the non-parametric analysis. - # Default is "p". - # - # na.rm: If na.rm is T, then all the missing samples are deleted from the whole dataset, which is (x,y,z). - # If not, the missing samples will be removed just when the correlation coefficient is calculated. - # However, the number of samples for the p-value is the number of samples after removing - # all the missing samples from the whole dataset. - # Default is "T". - - x <- c(x) - y <- c(y) - z <- as.data.frame(z) - - if(use == "mat"){ - p.use <- "Var-Cov matrix" - pcor = pcor.mat(x,y,z,method=method,na.rm=na.rm) - }else if(use == "rec"){ - p.use <- "Recursive formula" - pcor = pcor.rec(x,y,z,method=method,na.rm=na.rm) - }else{ - stop("use should be either rec or mat!\n") - } - - # print the method - if(gregexpr("p",method)[[1]][1] == 1){ - p.method <- "Pearson" - }else if(gregexpr("s",method)[[1]][1] == 1){ - p.method <- "Spearman" - }else if(gregexpr("k",method)[[1]][1] == 1){ - p.method <- "Kendall" - }else{ - stop("method should be pearson or spearman or kendall!\n") - } - - # sample number - n <- dim(na.omit(data.frame(x,y,z)))[1] - - # given variables' number - gn <- dim(z)[2] - - # p-value - if(p.method == "Kendall"){ - statistic <- pcor/sqrt(2*(2*(n-gn)+5)/(9*(n-gn)*(n-1-gn))) - p.value <- 2*pnorm(-abs(statistic)) - - }else{ - statistic <- pcor*sqrt((n-2-gn)/(1-pcor^2)) - p.value <- 2*pnorm(-abs(statistic)) - } - - data.frame(estimate=pcor,p.value=p.value,statistic=statistic,n=n,gn=gn,Method=p.method,Use=p.use) -} - -# By using var-cov matrix -pcor.mat <- function(x,y,z,method="p",na.rm=T){ - - x <- c(x) - y <- c(y) - z <- as.data.frame(z) - - if(dim(z)[2] == 0){ - stop("There should be given data\n") - } - - data <- data.frame(x,y,z) - - if(na.rm == T){ - data = na.omit(data) - } - - xdata <- na.omit(data.frame(data[,c(1,2)])) - Sxx <- cov(xdata,xdata,m=method) - - xzdata <- na.omit(data) - xdata <- data.frame(xzdata[,c(1,2)]) - zdata <- data.frame(xzdata[,-c(1,2)]) - Sxz <- cov(xdata,zdata,m=method) - - zdata <- na.omit(data.frame(data[,-c(1,2)])) - Szz <- cov(zdata,zdata,m=method) - - # is Szz positive definite? - zz.ev <- eigen(Szz)$values - if(min(zz.ev)[1]<0){ - stop("\'Szz\' is not positive definite!\n") - } - - # partial correlation - Sxx.z <- Sxx - Sxz %*% solve(Szz) %*% t(Sxz) - - rxx.z <- cov2cor(Sxx.z)[1,2] - - rxx.z -} - -# By using recursive formula -pcor.rec <- function(x,y,z,method="p",na.rm=T){ - # - - x <- c(x) - y <- c(y) - z <- as.data.frame(z) - - if(dim(z)[2] == 0){ - stop("There should be given data\n") - } - - data <- data.frame(x,y,z) - - if(na.rm == T){ - data = na.omit(data) - } - - # recursive formula - if(dim(z)[2] == 1){ - tdata <- na.omit(data.frame(data[,1],data[,2])) - rxy <- cor(tdata[,1],tdata[,2],m=method) - - tdata <- na.omit(data.frame(data[,1],data[,-c(1,2)])) - rxz <- cor(tdata[,1],tdata[,2],m=method) - - tdata <- na.omit(data.frame(data[,2],data[,-c(1,2)])) - ryz <- cor(tdata[,1],tdata[,2],m=method) - - rxy.z <- (rxy - rxz*ryz)/( sqrt(1-rxz^2)*sqrt(1-ryz^2) ) - - return(rxy.z) - }else{ - x <- c(data[,1]) - y <- c(data[,2]) - z0 <- c(data[,3]) - zc <- as.data.frame(data[,-c(1,2,3)]) - - rxy.zc <- pcor.rec(x,y,zc,method=method,na.rm=na.rm) - rxz0.zc <- pcor.rec(x,z0,zc,method=method,na.rm=na.rm) - ryz0.zc <- pcor.rec(y,z0,zc,method=method,na.rm=na.rm) - - rxy.z <- (rxy.zc - rxz0.zc*ryz0.zc)/( sqrt(1-rxz0.zc^2)*sqrt(1-ryz0.zc^2) ) - return(rxy.z) - } -} -""") - - R_pcorr_function = rpy2.robjects.r['pcor.test'] - R_corr_test = rpy2.robjects.r['cor.test'] - - primary = rpy2.robjects.FloatVector(range(len(primaryVal))) - for i in range(len(primaryVal)): - primary[i] = primaryVal[i] - - control = rpy2.robjects.r.matrix(rpy2.robjects.FloatVector( range(len(controlVals)*len(controlVals[0])) ), ncol=len(controlVals)) - for i in range(len(controlVals)): - for j in range(len(controlVals[0])): - control[i*len(controlVals[0]) + j] = controlVals[i][j] - - allcorrelations = [] - - for targetIndex, oneTargetVals in enumerate(targetVals): - - this_primary = None - this_control = None - this_target = None - - if None in oneTargetVals: - - goodIndex = [] - for i in range(len(oneTargetVals)): - if oneTargetVals[i] != None: - goodIndex.append(i) - - this_primary = rpy2.robjects.FloatVector(range(len(goodIndex))) - for i in range(len(goodIndex)): - this_primary[i] = primaryVal[goodIndex[i]] - - this_control = rpy2.robjects.r.matrix(rpy2.robjects.FloatVector( range(len(controlVals)*len(goodIndex)) ), ncol=len(controlVals)) - for i in range(len(controlVals)): - for j in range(len(goodIndex)): - this_control[i*len(goodIndex) + j] = controlVals[i][goodIndex[j]] - - this_target = rpy2.robjects.FloatVector(range(len(goodIndex))) - for i in range(len(goodIndex)): - this_target[i] = oneTargetVals[goodIndex[i]] - - else: - this_primary = primary - this_control = control - this_target = rpy2.robjects.FloatVector(range(len(oneTargetVals))) - for i in range(len(oneTargetVals)): - this_target[i] = oneTargetVals[i] - - one_name = targetNames[targetIndex] - one_N = len(this_primary) - - #calculate partial correlation - one_pc_coefficient = 'NA' - one_pc_p = 1 - - try: - if method == 's': - result = R_pcorr_function(this_primary, this_target, this_control, method='s') - else: - result = R_pcorr_function(this_primary, this_target, this_control) - - #XZ: In very few cases, the returned coefficient is nan. - #XZ: One way to detect nan is to compare the number to itself. NaN is always != NaN - if result[0][0] == result[0][0]: - one_pc_coefficient = result[0][0] - #XZ: when the coefficient value is 1 (primary trait and target trait are the same), - #XZ: occationally, the returned p value is nan instead of 0. - if result[1][0] == result[1][0]: - one_pc_p = result[1][0] - elif abs(one_pc_coefficient - 1) < 0.0000001: - one_pc_p = 0 - except: - pass - - #calculate zero order correlation - one_corr_coefficient = 0 - one_corr_p = 1 - - try: - if method == 's': - R_result = R_corr_test(this_primary, this_target, method='spearman') - else: - R_result = R_corr_test(this_primary, this_target) - - one_corr_coefficient = R_result[3][0] - one_corr_p = R_result[2][0] - except: - pass - - traitinfo = [ one_name, one_N, one_pc_coefficient, one_pc_p, one_corr_coefficient, one_corr_p ] - - allcorrelations.append(traitinfo) - - return allcorrelations - #End of function compute_partial - - - allcorrelations = [] - - target_trait_number = len(targetVals) - - if target_trait_number < 1000: - allcorrelations = compute_partial ( primaryVal, controlVals, targetVals, targetNames, method ) - else: - step = 1000 - job_number = math.ceil( float(target_trait_number)/step ) - - job_targetVals_lists = [] - job_targetNames_lists = [] - - for job_index in range( int(job_number) ): - starti = job_index*step - endi = min((job_index+1)*step, target_trait_number) - - one_job_targetVals_list = [] - one_job_targetNames_list = [] - - for i in range( starti, endi ): - one_job_targetVals_list.append( targetVals[i] ) - one_job_targetNames_list.append( targetNames[i] ) - - job_targetVals_lists.append( one_job_targetVals_list ) - job_targetNames_lists.append( one_job_targetNames_list ) - - ppservers = () - # Creates jobserver with automatically detected number of workers - job_server = pp.Server(ppservers=ppservers) - - jobs = [] - results = [] - - for i, one_job_targetVals_list in enumerate( job_targetVals_lists ): - one_job_targetNames_list = job_targetNames_lists[i] - #pay attention to modules from outside - jobs.append( job_server.submit(func=compute_partial, args=( primaryVal, controlVals, one_job_targetVals_list, one_job_targetNames_list, method), depfuncs=(), modules=("rpy2.robjects",)) ) - - for one_job in jobs: - one_result = one_job() - results.append( one_result ) - - for one_result in results: - for one_traitinfo in one_result: - allcorrelations.append( one_traitinfo ) - - return allcorrelations - - - -#XZ, April 30, 2010: The input primaryTrait and targetTrait are instance of webqtlTrait -#XZ: The primaryTrait and targetTrait should have executed retrieveData function -def calZeroOrderCorr (primaryTrait, targetTrait, method='pearson'): - - #primaryTrait.retrieveData() - - #there is no None value in primary_val - primary_strain, primary_val, primary_var = primaryTrait.exportInformative() - - #targetTrait.retrieveData() - - #there might be None value in target_val - target_val = targetTrait.exportData(primary_strain, type="val") - - R_primary = rpy2.robjects.FloatVector(range(len(primary_val))) - for i in range(len(primary_val)): - R_primary[i] = primary_val[i] - - N = len(target_val) - - if None in target_val: - goodIndex = [] - for i in range(len(target_val)): - if target_val[i] != None: - goodIndex.append(i) - - N = len(goodIndex) - - R_primary = rpy2.robjects.FloatVector(range(len(goodIndex))) - for i in range(len(goodIndex)): - R_primary[i] = primary_val[goodIndex[i]] - - R_target = rpy2.robjects.FloatVector(range(len(goodIndex))) - for i in range(len(goodIndex)): - R_target[i] = target_val[goodIndex[i]] - - else: - R_target = rpy2.robjects.FloatVector(range(len(target_val))) - for i in range(len(target_val)): - R_target[i] = target_val[i] - - R_corr_test = rpy2.robjects.r['cor.test'] - - if method == 'spearman': - R_result = R_corr_test(R_primary, R_target, method='spearman') - else: - R_result = R_corr_test(R_primary, R_target) - - corr_result = [] - corr_result.append( R_result[3][0] ) - corr_result.append( N ) - corr_result.append( R_result[2][0] ) - - return corr_result - -##################################################################################### -#Input: primaryValue(list): one list of expression values of one probeSet, -# targetValue(list): one list of expression values of one probeSet, -# method(string): indicate correlation method ('pearson' or 'spearman') -#Output: corr_result(list): first item is Correlation Value, second item is tissue number, -# third item is PValue -#Function: get correlation value,Tissue quantity ,p value result by using R; -#Note : This function is special case since both primaryValue and targetValue are from -#the same dataset. So the length of these two parameters is the same. They are pairs. -#Also, in the datatable TissueProbeSetData, all Tissue values are loaded based on -#the same tissue order -##################################################################################### - -def calZeroOrderCorrForTiss (primaryValue=[], targetValue=[], method='pearson'): - - R_primary = rpy2.robjects.FloatVector(range(len(primaryValue))) - N = len(primaryValue) - for i in range(len(primaryValue)): - R_primary[i] = primaryValue[i] - - R_target = rpy2.robjects.FloatVector(range(len(targetValue))) - for i in range(len(targetValue)): - R_target[i]=targetValue[i] - - R_corr_test = rpy2.robjects.r['cor.test'] - if method =='spearman': - R_result = R_corr_test(R_primary, R_target, method='spearman') - else: - R_result = R_corr_test(R_primary, R_target) - - corr_result =[] - corr_result.append( R_result[3][0]) - corr_result.append( N ) - corr_result.append( R_result[2][0]) - - return corr_result - - - - -def batchCalTissueCorr(primaryTraitValue=[], SymbolValueDict={}, method='pearson'): - - def cal_tissue_corr(primaryTraitValue, oneSymbolValueDict, method ): - - oneSymbolCorrDict = {} - oneSymbolPvalueDict = {} - - R_corr_test = rpy2.robjects.r['cor.test'] - - R_primary = rpy2.robjects.FloatVector(range(len(primaryTraitValue))) - - for i in range(len(primaryTraitValue)): - R_primary[i] = primaryTraitValue[i] - - for (oneTraitSymbol, oneTraitValue) in oneSymbolValueDict.iteritems(): - R_target = rpy2.robjects.FloatVector(range(len(oneTraitValue))) - for i in range(len(oneTraitValue)): - R_target[i] = oneTraitValue[i] - - if method =='spearman': - R_result = R_corr_test(R_primary, R_target, method='spearman') - else: - R_result = R_corr_test(R_primary, R_target) - - oneSymbolCorrDict[oneTraitSymbol] = R_result[3][0] - oneSymbolPvalueDict[oneTraitSymbol] = R_result[2][0] - - return(oneSymbolCorrDict, oneSymbolPvalueDict) - - - - symbolCorrDict = {} - symbolPvalueDict = {} - - items_number = len(SymbolValueDict) - - if items_number <= 1000: - symbolCorrDict, symbolPvalueDict = cal_tissue_corr(primaryTraitValue, SymbolValueDict, method) - else: - items_list = SymbolValueDict.items() - - step = 1000 - job_number = math.ceil( float(items_number)/step ) - - job_oneSymbolValueDict_list = [] - - for job_index in range( int(job_number) ): - starti = job_index*step - endi = min((job_index+1)*step, items_number) - - oneSymbolValueDict = {} - - for i in range( starti, endi ): - one_item = items_list[i] - one_symbol = one_item[0] - one_value = one_item[1] - oneSymbolValueDict[one_symbol] = one_value - - job_oneSymbolValueDict_list.append( oneSymbolValueDict ) - - - ppservers = () - # Creates jobserver with automatically detected number of workers - job_server = pp.Server(ppservers=ppservers) - - jobs = [] - results = [] - - for i, oneSymbolValueDict in enumerate( job_oneSymbolValueDict_list ): - - #pay attention to modules from outside - jobs.append( job_server.submit(func=cal_tissue_corr, args=(primaryTraitValue, oneSymbolValueDict, method), depfuncs=(), modules=("rpy2.robjects",)) ) - - for one_job in jobs: - one_result = one_job() - results.append( one_result ) - - for one_result in results: - oneSymbolCorrDict, oneSymbolPvalueDict = one_result - symbolCorrDict.update( oneSymbolCorrDict ) - symbolPvalueDict.update( oneSymbolPvalueDict ) - - return (symbolCorrDict, symbolPvalueDict) - -########################################################################### -#Input: cursor, GeneNameLst (list), TissueProbeSetFreezeId -#output: geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict (Dict) -#function: get multi dicts for short and long label functions, and for getSymbolValuePairDict and -# getGeneSymbolTissueValueDict to build dict to get CorrPvArray -#Note: If there are multiple probesets for one gene, select the one with highest mean. -########################################################################### -def getTissueProbeSetXRefInfo(cursor=None,GeneNameLst=[],TissueProbeSetFreezeId=0): - Symbols ="" - symbolList =[] - geneIdDict ={} - dataIdDict = {} - ChrDict = {} - MbDict = {} - descDict = {} - pTargetDescDict = {} - - count = len(GeneNameLst) - - # Added by NL 01/06/2011 - # Note that:inner join is necessary in this query to get distinct record in one symbol group with highest mean value - # Duo to the limit size of TissueProbeSetFreezeId table in DB, performance of inner join is acceptable. - if count==0: - query=''' - select t.Symbol,t.GeneId, t.DataId,t.Chr, t.Mb,t.description,t.Probe_Target_Description - from ( - select Symbol, max(Mean) as maxmean - from TissueProbeSetXRef - where TissueProbeSetFreezeId=%s and Symbol!='' and Symbol Is Not Null group by Symbol) - as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol and t.Mean = x.maxmean; - '''%TissueProbeSetFreezeId - - else: - for i, item in enumerate(GeneNameLst): - - if i == count-1: - Symbols += "'%s'" %item - else: - Symbols += "'%s'," %item - - Symbols = "("+ Symbols+")" - query=''' - select t.Symbol,t.GeneId, t.DataId,t.Chr, t.Mb,t.description,t.Probe_Target_Description - from ( - select Symbol, max(Mean) as maxmean - from TissueProbeSetXRef - where TissueProbeSetFreezeId=%s and Symbol in %s group by Symbol) - as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol and t.Mean = x.maxmean; - '''% (TissueProbeSetFreezeId,Symbols) - - try: - - cursor.execute(query) - results =cursor.fetchall() - resultCount = len(results) - # Key in all dicts is the lower-cased symbol - for i, item in enumerate(results): - symbol = item[0] - symbolList.append(symbol) - - key =symbol.lower() - geneIdDict[key]=item[1] - dataIdDict[key]=item[2] - ChrDict[key]=item[3] - MbDict[key]=item[4] - descDict[key]=item[5] - pTargetDescDict[key]=item[6] - - except: - symbolList = None - geneIdDict=None - dataIdDict=None - ChrDict=None - MbDict=None - descDict=None - pTargetDescDict=None - - return symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict - -########################################################################### -#Input: cursor, symbolList (list), dataIdDict(Dict) -#output: symbolValuepairDict (dictionary):one dictionary of Symbol and Value Pair, -# key is symbol, value is one list of expression values of one probeSet; -#function: get one dictionary whose key is gene symbol and value is tissue expression data (list type). -#Attention! All keys are lower case! -########################################################################### -def getSymbolValuePairDict(cursor=None,symbolList=None,dataIdDict={}): - symbolList = map(string.lower, symbolList) - symbolValuepairDict={} - valueList=[] - - for key in symbolList: - if dataIdDict.has_key(key): - DataId = dataIdDict[key] - - valueQuery = "select value from TissueProbeSetData where Id=%s" % DataId - try : - cursor.execute(valueQuery) - valueResults = cursor.fetchall() - for item in valueResults: - item =item[0] - valueList.append(item) - symbolValuepairDict[key] = valueList - valueList=[] - except: - symbolValuepairDict[key] = None - - return symbolValuepairDict - - -######################################################################################################## -#input: cursor, symbolList (list), dataIdDict(Dict): key is symbol -#output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair. -# key is symbol, value is one list of expression values of one probeSet. -#function: wrapper function for getSymbolValuePairDict function -# build gene symbol list if necessary, cut it into small lists if necessary, -# then call getSymbolValuePairDict function and merge the results. -######################################################################################################## - -def getGeneSymbolTissueValueDict(cursor=None,symbolList=None,dataIdDict={}): - limitNum=1000 - count = len(symbolList) - - SymbolValuePairDict = {} - - if count !=0 and count <=limitNum: - SymbolValuePairDict = getSymbolValuePairDict(cursor=cursor,symbolList=symbolList,dataIdDict=dataIdDict) - - elif count >limitNum: - SymbolValuePairDict={} - n = count/limitNum - start =0 - stop =0 - - for i in range(n): - stop =limitNum*(i+1) - gList1 = symbolList[start:stop] - PairDict1 = getSymbolValuePairDict(cursor=cursor,symbolList=gList1,dataIdDict=dataIdDict) - start =limitNum*(i+1) - - SymbolValuePairDict.update(PairDict1) - - if stop < count: - stop = count - gList2 = symbolList[start:stop] - PairDict2 = getSymbolValuePairDict(cursor=cursor,symbolList=gList2,dataIdDict=dataIdDict) - SymbolValuePairDict.update(PairDict2) - - return SymbolValuePairDict - -######################################################################################################## -#input: cursor, GeneNameLst (list), TissueProbeSetFreezeId(int) -#output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair. -# key is symbol, value is one list of expression values of one probeSet. -#function: wrapper function of getGeneSymbolTissueValueDict function -# for CorrelationPage.py -######################################################################################################## - -def getGeneSymbolTissueValueDictForTrait(cursor=None,GeneNameLst=[],TissueProbeSetFreezeId=0): - SymbolValuePairDict={} - symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict = getTissueProbeSetXRefInfo(cursor=cursor,GeneNameLst=GeneNameLst,TissueProbeSetFreezeId=TissueProbeSetFreezeId) - if symbolList: - SymbolValuePairDict = getGeneSymbolTissueValueDict(cursor=cursor,symbolList=symbolList,dataIdDict=dataIdDict) - return SymbolValuePairDict - -######################################################################################################## -#Input: cursor(cursor): MySQL connnection cursor; -# priGeneSymbolList(list): one list of gene symbol; -# symbolValuepairDict(dictionary): one dictionary of Symbol and Value Pair, -# key is symbol, value is one list of expression values of one probeSet; -#Output: corrArray(array): array of Correlation Value, -# pvArray(array): array of PValue; -#Function: build corrArray, pvArray for display by calling calculation function:calZeroOrderCorrForTiss -######################################################################################################## - -def getCorrPvArray(cursor=None,priGeneSymbolList=[],symbolValuepairDict={}): - # setting initial value for corrArray, pvArray equal to 0 - Num = len(priGeneSymbolList) - - corrArray = [([0] * (Num))[:] for i in range(Num)] - pvArray = [([0] * (Num))[:] for i in range(Num)] - i = 0 - for pkey in priGeneSymbolList: - j = 0 - pkey = pkey.strip().lower()# key in symbolValuepairDict is low case - if symbolValuepairDict.has_key(pkey): - priValue = symbolValuepairDict[pkey] - for tkey in priGeneSymbolList: - tkey = tkey.strip().lower()# key in symbolValuepairDict is low case - if priValue and symbolValuepairDict.has_key(tkey): - tarValue = symbolValuepairDict[tkey] - - if tarValue: - if i>j: - # corrArray stores Pearson Correlation values - # pvArray stores Pearson P-Values - pcorr_result =calZeroOrderCorrForTiss(primaryValue=priValue,targetValue=tarValue) - corrArray[i][j] =pcorr_result[0] - pvArray[i][j] =pcorr_result[2] - elif i<j: - # corrArray stores Spearman Correlation values - # pvArray stores Spearman P-Values - scorr_result =calZeroOrderCorrForTiss(primaryValue=priValue,targetValue=tarValue,method='spearman') - corrArray[i][j] =scorr_result[0] - pvArray[i][j] =scorr_result[2] - else: - # on the diagonal line, correlation value is 1, P-Values is 0 - corrArray[i][j] =1 - pvArray[i][j] =0 - j+=1 - else: - corrArray[i][j] = None - pvArray[i][j] = None - j+=1 - else: - corrArray[i][j] = None - pvArray[i][j] = None - j+=1 - else: - corrArray[i][j] = None - pvArray[i][j] = None - - i+=1 - - return corrArray, pvArray - -######################################################################################################## -#Input: cursor(cursor): MySQL connnection cursor; -# primaryTraitSymbol(string): one gene symbol; -# TissueProbeSetFreezeId (int): Id of related TissueProbeSetFreeze -# method: '0' default value, Pearson Correlation; '1', Spearman Correlation -#Output: symbolCorrDict(Dict): Dict of Correlation Value, key is symbol -# symbolPvalueDict(Dict): Dict of PValue,key is symbol ; -#Function: build symbolCorrDict, symbolPvalueDict for display by calling calculation function:calZeroOrderCorrForTiss -######################################################################################################## -def calculateCorrOfAllTissueTrait(cursor=None, primaryTraitSymbol=None, TissueProbeSetFreezeId=None,method='0'): - - symbolCorrDict = {} - symbolPvalueDict = {} - - primaryTraitSymbolValueDict = getGeneSymbolTissueValueDictForTrait(cursor=cursor, GeneNameLst=[primaryTraitSymbol], TissueProbeSetFreezeId=TissueProbeSetFreezeId) - primaryTraitValue = primaryTraitSymbolValueDict.values()[0] - - SymbolValueDict = getGeneSymbolTissueValueDictForTrait(cursor=cursor, GeneNameLst=[], TissueProbeSetFreezeId=TissueProbeSetFreezeId) - - if method =='1': - symbolCorrDict, symbolPvalueDict = batchCalTissueCorr(primaryTraitValue,SymbolValueDict,method='spearman') - else: - symbolCorrDict, symbolPvalueDict = batchCalTissueCorr(primaryTraitValue,SymbolValueDict) - - - return (symbolCorrDict, symbolPvalueDict) |