Add all the source codes into the github.

7 files changed, 5717 insertions, 0 deletions

diff --git a/web/webqtl/correlation/CorrelationPage.py b/web/webqtl/correlation/CorrelationPage.py
new file mode 100755
index 00000000..8ce669cb
--- /dev/null
+++ b/web/webqtl/correlation/CorrelationPage.py
@@ -0,0 +1,1958 @@
+# 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
+
+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
+
+
+class CorrelationPage(templatePage):
+
+    corrMinInformative = 4
+
+    def __init__(self, fd):
+
+        #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
+
+
+        templatePage.__init__(self, fd)
+
+        if not self.openMysql():
+            return
+		
+        if not fd.genotype:
+            fd.readGenotype()
+
+        #XZ, 09/18/2008: get the information such as value, variance of the input strain names from the form.		
+        if fd.allstrainlist:
+            mdpchoice = fd.formdata.getvalue('MDPChoice')
+            #XZ, in HTML source code, it is "BXD Only" or "BXH only", and so on
+            if mdpchoice == "1":
+                strainlist = fd.f1list + fd.strainlist
+            #XZ, in HTML source code, it is "MDP Only"
+            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
+            #XZ, in HTML source code, it is "All Cases"
+            else:
+                strainlist = fd.allstrainlist
+            #XZ, 09/18/2008: put the trait data into dictionary fd.allTraitData
+            fd.readData(fd.allstrainlist)
+        else:	
+            mdpchoice = None	
+            strainlist = fd.strainlist
+            #XZ, 09/18/2008: put the trait data into dictionary fd.allTraitData
+            fd.readData()
+
+        #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('database')
+
+        try:
+            self.db = webqtlDataset(self.target_db_name, self.cursor)
+        except:
+            heading = "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 = "Correlation Table"
+                    detail = ["The %s database you selected is not open to the public at this time, please go back and select other database." % indFullName]
+                    self.error(heading=heading,detail=detail,error="Confidential Database")
+                    return
+
+        #XZ, 09/18/2008: filter out the strains that have no value.
+        _strains, _vals, _vars, N = fd.informativeStrains(strainlist)
+
+        N = len(_strains)
+
+        if N < self.corrMinInformative:
+            heading = "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, 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 = fd.formdata.getvalue('fullname')
+        if myTrait:
+            myTrait = webqtlTrait(fullname=myTrait, cursor=self.cursor)
+            myTrait.retrieveInfo()
+
+        # We will not get Literature Correlations if there is no GeneId because there is nothing to look against
+        try:
+            input_trait_GeneId = int(fd.formdata.getvalue('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
+
+############################################################################################################################################
+
+        allcorrelations = []
+        nnCorr = len(_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:
+            if 1:
+            #try:
+                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=input_trait_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, 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'. 
+                _newvals = []
+                for item in dataset_strains:
+                    if item in _strains:
+                        _newvals.append(_vals[_strains.index(item)])
+                    else:
+                        _newvals.append('None')
+
+                nnCorr = len(_newvals)
+
+                #XZ, 01/14/2009: If literature corr or tissue corr is selected,
+                #XZ: there is no need to use parallel computing.
+                if useLit or useTissueCorr:
+                    for line in datasetFile:
+                        traitdata=webqtlUtil.readLineCSV(line)
+                        traitdataName = traitdata[0]
+                        traitvals = traitdata[1:]
+
+                        if useLit:
+                            if not litCorrs.has_key( traitdataName ):
+                                continue
+
+                        if useTissueCorr:
+                            if not tissueCorrs.has_key( traitdataName ):
+                                continue
+
+                        if self.method == "3" or self.method == "4":
+                            corr,nOverlap = webqtlUtil.calCorrelationText(traitvals,_newvals,nnCorr)
+                        else:
+                            corr,nOverlap = webqtlUtil.calCorrelationRankText(traitvals,_newvals,nnCorr)
+
+                        traitinfo = [traitdataName,corr,nOverlap]
+
+                        if useLit:
+                            traitinfo.append(litCorrs[traitdataName])
+
+                        if useTissueCorr:
+                            tempCorr, tempPValue = tissueCorrs[traitdataName]
+                            traitinfo.append(tempCorr)
+                            traitinfo.append(tempPValue)
+
+                        allcorrelations.append(traitinfo)
+                #XZ, 01/14/2009: If genetic corr is selected, use parallel computing
+                else:
+                    input_line_list = datasetFile.readlines()
+                    all_line_number = len(input_line_list)
+
+                    step = 1000
+                    job_number = math.ceil( float(all_line_number)/step )
+
+                    job_input_lists = []
+
+                    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 )
+
+                    ppservers = ()
+                    # Creates jobserver with automatically detected number of workers
+                    job_server = pp.Server(ppservers=ppservers)
+
+                    jobs = []
+                    results = []
+
+                    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",)) )
+
+                    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 )
+
+                datasetFile.close()
+                totalTraits = len(allcorrelations)
+            #except:
+            #    useFastMethod = False
+            #    self.error(heading="No computation method",detail="Something is wrong within the try except block in CorrelationPage python module.",error="Computation Error")
+            #    return
+
+        #XZ, 01/08/2009: use the original method to retrieve from database and compute.            
+        if not useFastMethod:
+
+            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
+
+            for traitdata in traitdatabase:
+                traitdataName = traitdata[0]
+                traitvals = traitdata[dataStartPos:]
+                if self.method == "1" or self.method == "3" or self.method == "4":
+                    corr,nOverlap = webqtlUtil.calCorrelation(traitvals,_vals,nnCorr)
+                else:
+                    corr,nOverlap = webqtlUtil.calCorrelationRank(traitvals,_vals,nnCorr)
+
+                traitinfo = [traitdataName,corr,nOverlap]
+
+                #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":
+                    if self.method == "3":
+                        traitinfo.append( traitdata[1] )
+                    if self.method == "4" or self.method == "5":
+                        traitinfo.append( traitdata[1] )
+                        traitinfo.append( traitdata[2] )
+
+                allcorrelations.append(traitinfo)
+
+
+#############################################################
+
+        if self.method == "3" and input_trait_GeneId:
+            allcorrelations.sort(webqtlUtil.cmpLitCorr)
+        #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. 
+        elif self.method in ["4","5"] and input_trait_GeneId:
+            allcorrelations.sort(webqtlUtil.cmpLitCorr)
+        else:
+            allcorrelations.sort(webqtlUtil.cmpCorr)
+
+
+        #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( QTL='Yes' )
+
+            nOverlap = item[2]
+            corr = item[1]
+
+            #XZ: calculate corrPValue
+            if nOverlap < 3:
+                corrPValue = 1.0
+            else:
+                if abs(corr) >= 1.0:
+                    corrPValue = 0.0
+                else:
+                    ZValue = 0.5*log((1.0+corr)/(1.0-corr))
+                    ZValue = ZValue*sqrt(nOverlap-3)
+                    corrPValue = 2.0*(1.0 - reaper.normp(abs(ZValue)))
+
+            thisTrait.Name = item[0]
+            thisTrait.corr = corr
+            thisTrait.nOverlap = nOverlap
+            thisTrait.corrPValue = corrPValue
+            # 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) == 5:
+                thisTrait.tissueCorr = item[3]
+                thisTrait.tissuePValue = item[4]
+                addLiteratureCorr = True
+
+            #XZ, 26/09/2008: Method is 3,  Have fetched literature corr, but no tissue corr yet.
+            elif len(item) == 4:
+                thisTrait.LCorr = item[3]
+                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, 'databaseFull':self.db.fullname, 'CellID':'_', 'RISet':RISet, 'identification':fd.identification}
+
+        if myTrait:
+            hddn['fullname']=fd.formdata.getvalue('fullname')
+        if mdpchoice:
+            hddn['MDPChoice']=mdpchoice
+
+
+        #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" % TissueProbeSetFreezeId, 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=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')
+        
+	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("&nbsp;"*1,"Select"), HT.TD("Deselect"), HT.TD("&nbsp;"*1,"Invert"), HT.TD("&nbsp;"*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("&nbsp;"*1,HT.Text("Graph")), HT.TD("&nbsp;"*1,HT.Text("Matrix")), HT.TD("&nbsp;"*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("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(fewerOptions, Class="toggleShowHide"))))
+			containerTable.append(HT.TR(HT.TD("&nbsp;")))
+        else:	
+			containerTable.append(HT.TR(HT.TD("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(moreOptions, Class="toggleShowHide"))))	
+			containerTable.append(HT.TR(HT.TD("&nbsp;")))
+
+        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=40)))
+
+	    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=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=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=species, strains=_strains)
+            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"))
+
+            #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)
+
+            '''
+            #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 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'))
+
+
+	    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("&nbsp;", HT.Text("GCAT"), width="50%"), HT.TD("&nbsp;",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("&nbsp;"*2, HT.Text("GCAT")), HT.TD("&nbsp;"*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())))
+
+	    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, 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=tissueProbeSetFreezeId, 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])
+        traitdatabase = []
+        # put all of the seperate 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:])
+            traitdatabase.append(traitdata)
+
+        if tempTable:
+            self.cursor.execute( 'DROP TEMPORARY TABLE %s' % tempTable )
+
+        return traitdatabase, dataStartPos
+
+
+    # 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=TissueProbeSetFreezeId, 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=TissueProbeSetFreezeId, 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 calculateCorrOfAllTissueTrait(self, primaryTraitSymbol=None, TissueProbeSetFreezeId=None, method=None):
+
+        symbolCorrDict = {}
+        symbolPvalueDict = {}
+
+        primaryTraitSymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[primaryTraitSymbol], TissueProbeSetFreezeId=TissueProbeSetFreezeId)
+        primaryTraitValue = primaryTraitSymbolValueDict.values()[0]
+
+        SymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[], TissueProbeSetFreezeId=TissueProbeSetFreezeId)
+
+        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=TissueProbeSetFreezeId)
+
+        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=TissueProbeSetFreezeId)
+
+            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 " % totalTraits, 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, totalTraits),
+                                 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 = 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 = "--"
+            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
new file mode 100755
index 00000000..ecd1e623
--- /dev/null
+++ b/web/webqtl/correlation/PartialCorrDBPage.py
@@ -0,0 +1,1359 @@
+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("&nbsp;"*1,"Select"), HT.TD("Deselect"), HT.TD("&nbsp;"*1,"Invert"), HT.TD("&nbsp;"*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("&nbsp;"*1,HT.Text("Graph")), HT.TD("&nbsp;"*1,HT.Text("Matrix")), HT.TD("&nbsp;"*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("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(fewerOptions, Class="toggleShowHide"))))
+			containerTable.append(HT.TR(HT.TD("&nbsp;")))
+        else:	
+			containerTable.append(HT.TR(HT.TD("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(moreOptions, Class="toggleShowHide"))))	
+			containerTable.append(HT.TR(HT.TD("&nbsp;")))
+
+        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("&nbsp;", HT.Text("GCAT"), width="50%"), HT.TD("&nbsp;",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("&nbsp;"*2, HT.Text("GCAT")), HT.TD("&nbsp;"*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
new file mode 100755
index 00000000..7d32da6d
--- /dev/null
+++ b/web/webqtl/correlation/PartialCorrInputPage.py
@@ -0,0 +1,484 @@
+# 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
new file mode 100755
index 00000000..1c79e250
--- /dev/null
+++ b/web/webqtl/correlation/PartialCorrTraitPage.py
@@ -0,0 +1,310 @@
+# 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
new file mode 100755
index 00000000..23d2ccde
--- /dev/null
+++ b/web/webqtl/correlation/PlotCorrelationPage.py
@@ -0,0 +1,683 @@
+# 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,'&nbsp;&nbsp;&nbsp;&nbsp;',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: "), "&nbsp;"*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
new file mode 100755
index 00000000..e69de29b
--- /dev/null
+++ b/web/webqtl/correlation/__init__.py
diff --git a/web/webqtl/correlation/correlationFunction.py b/web/webqtl/correlation/correlationFunction.py
new file mode 100755
index 00000000..cc19f54e
--- /dev/null
+++ b/web/webqtl/correlation/correlationFunction.py
@@ -0,0 +1,923 @@
+# 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)