Add all the source codes into the github.

5 files changed, 1479 insertions, 0 deletions

diff --git a/web/webqtl/correlationMatrix/CorrelationMatrixPage.py b/web/webqtl/correlationMatrix/CorrelationMatrixPage.py
new file mode 100755
index 00000000..a01111f5
--- /dev/null
+++ b/web/webqtl/correlationMatrix/CorrelationMatrixPage.py
@@ -0,0 +1,595 @@
+# 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 2011/02/14
+
+import os
+import string
+from htmlgen import HTMLgen2 as HT
+import sys
+import time
+import numarray
+import numarray.linear_algebra as la
+import piddle as pid
+import math
+
+from base.templatePage import templatePage
+from base import webqtlConfig
+from base.webqtlTrait import webqtlTrait
+from utility import webqtlUtil
+from utility import Plot
+
+
+
+# XZ, 09/09/2008: After adding several traits to collection, click "Correlation Matrix" button,
+# XZ, 09/09/2008: This class will generate what you see.
+#########################################
+#      Correlation Matrix Page
+#########################################
+
+class CorrelationMatrixPage(templatePage):
+
+    def __init__(self,fd,InputData=None):
+
+        templatePage.__init__(self, fd)
+
+        self.dict['title'] = 'Correlation Matrix'
+
+        if not self.openMysql():
+            return
+            
+        if not fd.genotype:
+            fd.readGenotype()
+            fd.strainlist = fd.f1list + fd.strainlist
+            
+        #self.searchResult = fd.formdata.getvalue('searchResult')
+        self.oldSearchResult = fd.formdata.getvalue('oldSearchResult')
+        
+        if self.oldSearchResult:
+            try:
+                self.searchResult = fd.formdata.getvalue('oldSearchResult')
+            except:
+            	self.searchResult = fd.formdata.getvalue('searchResult')
+        
+        else:
+        	self.searchResult = fd.formdata.getvalue('searchResult')
+        
+        if not self.searchResult:
+            heading = 'Correlation Matrix'
+            detail = ['You need to select at least two traits in order to generate correlation matrix.']
+            self.error(heading=heading,detail=detail)
+            return
+        if type("1") == type(self.searchResult):
+            self.searchResult = [self.searchResult]
+        
+        if self.searchResult:
+            #testvals,names,dbInfos = self.getAllSearchResult(fd,self.searchResult)
+            if len(self.searchResult) > webqtlConfig.MAXCORR:
+                heading = 'Correlation Matrix'
+                detail = ['In order to display Correlation Matrix properly, Do not select more than %d traits for Correlation Matrix.' % webqtlConfig.MAXCORR]
+                self.error(heading=heading,detail=detail)
+                return
+
+            #XZ, 7/22/2009: this block is not necessary
+            #elif len(self.searchResult) > 40:
+            #    noPCA = 1
+            #else:
+            #    noPCA = 0
+    
+            traitList = []
+            traitDataList = []
+            for item in self.searchResult:
+                thisTrait = webqtlTrait(fullname=item, cursor=self.cursor)
+                thisTrait.retrieveInfo()
+                thisTrait.retrieveData(fd.strainlist)
+                traitList.append(thisTrait)
+                traitDataList.append(thisTrait.exportData(fd.strainlist))
+                
+        else:
+            heading = 'Correlation Matrix'
+            detail = [HT.Font('Error : ',color='red'),HT.Font('Error occurs while retrieving data FROM database.',color='black')]
+            self.error(heading=heading,detail=detail)
+            return
+
+        NNN = len(traitList)
+        
+        if NNN == 0:
+            heading = "Correlation Matrix"
+            detail = ['No trait was selected for %s data set. No matrix generated.' % self.data.RISet]
+            self.error(heading=heading,detail=detail)
+            return
+        elif NNN < 2:
+            heading = 'Correlation Matrix'
+            detail = ['You need to select at least two traits in order to generate correlation matrix.']
+            self.error(heading=heading,detail=detail)
+            return
+        else:
+        	
+        	
+        	
+            corArray = [([0] * (NNN+1))[:] for i in range(NNN+1)]
+            pearsonArray = [([0] * (NNN))[:] for i in range(NNN)]
+            spearmanArray = [([0] * (NNN))[:] for i in range(NNN)]
+            corArray[0][0] = 'Correlation'
+            TD_LR = HT.TD(colspan=2,width="100%",bgColor='#eeeeee')
+            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', 'ProbeSetID':'_','database':'_',
+            'CellID':'_','ProbeSetID2':'_','database2':'_','CellID2':'_',
+            'newNames':fd.formdata.getvalue("newNames", "_"), 
+            'RISet':fd.RISet,'ShowStrains':'ON','ShowLine':'ON', 'rankOrder':'_', 
+            "allstrainlist":string.join(fd.strainlist, " "), 'traitList':string.join(self.searchResult, "\t")}
+            if fd.incparentsf1:
+                hddn['incparentsf1']='ON'
+            	    
+            for key in hddn.keys():
+                form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
+           
+            for item in self.searchResult:
+            	form.append(HT.Input(name='oldSearchResult', value=str(item), type='hidden'))
+            
+            traiturls = []
+            traiturls2 = []
+            shortNames = []
+            verboseNames = []
+            verboseNames2 = []
+            verboseNames3 = []
+            abbreviation = ''
+            
+            #dbInfo.ProbeSetID = ProbeSetID
+            #dbInfo.CellID = CellID
+            for i, thisTrait in enumerate(traitList):
+                _url = "javascript:showDatabase2('%s','%s','%s');" % (thisTrait.db.name, thisTrait.name, thisTrait.cellid)
+                #_text = 'Trait%d: ' % (i+1)+str(thisTrait)
+                _text = 'Trait %d: ' % (i+1)+thisTrait.displayName()
+                                
+                if thisTrait.db.type == 'Geno':
+                    _shortName = 'Genotype'
+                    abbreviation = 'Genotype'
+                    _verboseName = 'Locus %s' % (thisTrait.name)
+                    _verboseName2 = 'Chr %s @ %s Mb' % (thisTrait.chr, '%2.3f' % thisTrait.mb)
+                    _verboseName3 = ''
+                elif thisTrait.db.type == 'Publish':
+                    if thisTrait.post_publication_abbreviation:
+                        AbbreviationString = thisTrait.post_publication_abbreviation
+                    else:
+                        AbbreviationString = ''
+                    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 = ''
+                    _shortName = 'Phenotype: %s' % (AbbreviationString)  
+                    _verboseName2 = ''
+                    _verboseName3 = ''
+                    if thisTrait.pubmed_id:
+                        _verboseName = 'PubMed %d: ' % thisTrait.pubmed_id
+                    else: 
+                        _verboseName = 'Unpublished '
+                    _verboseName += 'RecordID/%s' % (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
+                    _verboseName2 = 'Phenotype: %s' % (PhenotypeString)
+                    if thisTrait.authors:
+                        a1 = string.split(thisTrait.authors,',')[0]
+                        while a1[0] == '"' or a1[0] == "'" :
+                            a1 = a1[1:]
+                            _verboseName += ' by '
+                            _verboseName += HT.Italic('%s, and colleagues' % (a1))
+                elif thisTrait.db.type == 'Temp':
+                    abbreviation = ''
+                    _shortName = thisTrait.name
+                    if thisTrait.description:
+                        _verboseName = thisTrait.description
+                    else:
+                        _verboseName = 'Temp'
+                    _verboseName2 = ''
+                    _verboseName3 = ''
+                else:
+                    abbreviation = thisTrait.symbol
+                    _shortName = 'Symbol: %s ' % thisTrait.symbol
+                    _verboseName = thisTrait.symbol
+                    _verboseName2 = ''
+                    _verboseName3 = ''
+                    if thisTrait.chr and thisTrait.mb:
+                        _verboseName += ' on Chr %s @ %s Mb' % (thisTrait.chr,thisTrait.mb)
+                    if thisTrait.description:
+                        _verboseName2 = '%s' % (thisTrait.description)
+                    if thisTrait.probe_target_description:
+                        _verboseName3 = '%s' % (thisTrait.probe_target_description)         
+                                
+                cururl = HT.Href(text=_text, url=_url,Class='fs12') 
+                cururl2 = HT.Href(text='Trait%d' % (i+1),url=_url,Class='fs12')
+                traiturls.append(cururl)
+                traiturls2.append(cururl2)
+                shortName = HT.Div(id="shortName_" + str(i), style="display:none")
+                shortName.append(_shortName)
+                shortNames.append(shortName)
+                verboseName = HT.Div(id="verboseName_" + str(i), style="display:none")
+                verboseName.append(_verboseName)
+                verboseNames.append(verboseName)
+                verboseName2 = HT.Div(id="verboseName2_" + str(i), style="display:none")
+                verboseName2.append(_verboseName2)
+                verboseNames2.append(verboseName2)
+                verboseName3 = HT.Div(id="verboseName3_" + str(i), style="display:none")
+                verboseName3.append(_verboseName3)
+                verboseNames3.append(verboseName3)
+                    
+
+                                
+                corArray[i+1][0] = 'Trait%d: ' % (i+1)+str(thisTrait) + '/' + str(thisTrait) + ': ' + abbreviation + '/' + str(thisTrait) + ': ' + str(_verboseName) + ' : ' + str(_verboseName2) + ' : ' + str(_verboseName3)
+                corArray[0][i+1] = 'Trait%d: ' % (i+1)+str(thisTrait)
+                
+            corMatrixHeading = HT.Paragraph('Correlation Matrix', Class="title")
+            
+            tbl = HT.TableLite(Class="collap", border=0, cellspacing=1, 
+                cellpadding=5, width='100%')
+            row1 = HT.TR(HT.TD(Class="fs14 fwb ffl b1 cw cbrb"),
+                HT.TD('Spearman Rank Correlation (rho)', Class="fs14 fwb ffl b1 cw cbrb", colspan= NNN+1,align="center")
+                )
+            row2 = HT.TR(
+                HT.TD("P e a r s o n &nbsp;&nbsp;&nbsp; r", rowspan= NNN+1,Class="fs14 fwb ffl b1 cw cbrb", width=10,align="center"),
+                HT.TD(Class="b1", width=300))
+            for i in range(NNN):
+                row2.append(HT.TD(traiturls2[i], Class="b1", align="center"))
+            tbl.append(row1,row2)
+
+            nOverlapTrait =9999            
+            nnCorr = len(fd.strainlist)
+            for i, thisTrait in enumerate(traitList):
+                newrow = HT.TR()
+                newrow.append(HT.TD(traiturls[i], shortNames[i], verboseNames[i], verboseNames2[i], 
+                                    verboseNames3[i], Class="b1"))
+                names1 = [thisTrait.db.name, thisTrait.name, thisTrait.cellid]
+                for j, thisTrait2 in enumerate(traitList):
+                    names2 = [thisTrait2.db.name, thisTrait2.name, thisTrait2.cellid]
+                    if j < i:
+                        corr,nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)
+                        
+                        rank = fd.formdata.getvalue("rankOrder", "0")
+                        
+                        if nOverlap < nOverlapTrait:
+                            nOverlapTrait = nOverlap
+                        if corr > 0.7:
+                            fontcolor="red"
+                        elif corr > 0.5:
+                            fontcolor="#FF6600"
+                        elif corr < -0.7:
+                            fontcolor="blue"
+                        elif corr < -0.5:
+                            fontcolor="#009900"
+                        else:
+                            fontcolor ="#000000"
+
+                        pearsonArray[i][j] = corr
+                        pearsonArray[j][i] = corr
+                        if corr!= 0.0:
+                            corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
+                            thisurl = HT.Href(text=HT.Font('%2.3f'% corr,HT.BR(),'%d' % nOverlap ,color=fontcolor, Class="fs11 fwn"),url = "javascript:showCorrelationPlot2(db='%s',ProbeSetID='%s',CellID='%s',db2='%s',ProbeSetID2='%s',CellID2='%s',rank='%s')" % (names1[0], names1[1], names1[2], names2[0], names2[1], names2[2], rank))
+                        else:
+                            corArray[i+1][j+1] = '---/%d' % nOverlap
+                            thisurl = HT.Font('---',HT.BR(), '%d' % nOverlap)
+                        
+                        newrow.append(HT.TD(thisurl,Class="b1",NOWRAP="ON",align="middle"))
+                    elif j == i:
+                        corr,nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)
+                        pearsonArray[i][j] = 1.0
+                        spearmanArray[i][j] = 1.0
+                        corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
+                        nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)[1]
+                        newrow.append(HT.TD(HT.Href(text=HT.Font(HT.Italic("n"),HT.BR(),str(nOverlap),Class="fs11 fwn b1",align="center", color="000000"), url="javascript:showDatabase2('%s','%s','%s')" % (thisTrait.db.name, thisTrait.name, thisTrait.cellid)), bgColor='#cccccc', align="center", Class="b1", NOWRAP="ON"))
+                    else:       
+                        corr,nOverlap = webqtlUtil.calCorrelationRank(traitDataList[i],traitDataList[j],nnCorr)
+                        
+                        rank = fd.formdata.getvalue("rankOrder", "1")
+                        
+                        if corr > 0.7:
+                            fontcolor="red"
+                        elif corr > 0.5:
+                            fontcolor="#FF6600"
+                        elif corr < -0.7:
+                            fontcolor="blue"
+                        elif corr < -0.5:
+                            fontcolor="#009900"
+                        else:
+                            fontcolor ="#000000"
+                        spearmanArray[i][j] = corr
+                        spearmanArray[j][i] = corr
+                        if corr!= 0.0:
+                            corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
+                            thisurl = HT.Href(text=HT.Font('%2.3f'% corr,HT.BR(),'%d' % nOverlap ,color=fontcolor, Class="fs11 fwn"),url = "javascript:showCorrelationPlot2(db='%s',ProbeSetID='%s',CellID='%s',db2='%s',ProbeSetID2='%s',CellID2='%s',rank='%s')" % (names1[0], names1[1], names1[2], names2[0], names2[1], names2[2], rank))
+                        else:
+                            corArray[i+1][j+1] = '---/%d' % nOverlap
+                            thisurl = HT.Span('---',HT.BR(), '%d' % nOverlap, Class="fs11 fwn")
+                        newrow.append(HT.TD(thisurl,Class="b1", NOWRAP="ON",align="middle"))
+                tbl.append(newrow)
+                
+            info = HT.Blockquote('Lower left cells list Pearson product-moment correlations; upper right cells list Spearman rank order correlations. Each cell also contains the n of cases. Values higher than 0.7 are displayed in ',HT.Font('red', color='red'),'; those between 0.5 and 0.7 in  ',HT.Font('orange', color='#FF6600'),'; Values lower than -0.7 are in ',HT.Font('blue', color='blue'),'; between -0.5 and -0.7 in ',HT.Font('green', color='#009900'),'. Select any cell to generate a scatter plot. Select trait labels for more information.', Class="fs13 fwn")
+            
+            exportbutton = HT.Input(type='button',  name='export', value='Export', onClick="exportText(allCorrelations);",Class="button")
+            shortButton = HT.Input(type='button' ,name='dispShort',value=' Short Labels ', onClick="displayShortName();",Class="button")
+            verboseButton = HT.Input(type='button' ,name='dispVerbose',value=' Long Labels ', onClick="displayVerboseName();", Class="button")
+            form.append(HT.Blockquote(tbl,HT.P(),shortButton,verboseButton,exportbutton))
+            TD_LR.append(corMatrixHeading,info,form,HT.P())
+
+            #if noPCA:
+            #    TD_LR.append(HT.Blockquote('No PCA is computed if more than 32 traits are selected.'))
+
+            #print corArray
+            exportScript = """
+                <SCRIPT language=JavaScript>
+                var allCorrelations = %s;
+                </SCRIPT>
+
+            """
+            exportScript = exportScript % str(corArray)
+            self.dict['js1'] = exportScript+'<SCRIPT SRC="/javascript/correlationMatrix.js"></SCRIPT><BR>'
+            self.dict['body'] = str(TD_LR)
+            
+            #don't calculate PCA while number exceed 32
+            #if noPCA:
+            #    return
+
+            #XZ, 7/22/2009: deal with PCA stuff
+            #Only for Array Data
+            
+            if NNN > 2:
+    
+                traitname = map(lambda X:str(X.name), traitList)
+                
+                #generate eigenvalues
+                
+                # import sys
+                sys.argv=[" "]
+                # import numarray
+                # import numarray.linear_algebra as la
+                #spearmanEigen = eigenvectors(array(spearmanArray))
+                pearsonEigen = la.eigenvectors(numarray.array(pearsonArray))
+                #spearmanEigenValue,spearmanEigenVectors = self.sortEigenVectors(spearmanEigen)
+                pearsonEigenValue,pearsonEigenVectors = self.sortEigenVectors(pearsonEigen)
+		
+				
+		"""
+		for i in range(len(pearsonEigenValue)):
+			if type(pearsonEigenValue[i]).__name__ == 'complex':
+				pearsonEigenValue[i] = pearsonEigenValue[i].real
+		for i in range(len(pearsonEigenVectors)):
+			for j in range(len(pearsonEigenVectors[i])):
+				if type(pearsonEigenVectors[i][j]).__name__ == 'complex':
+					pearsonEigenVectors[i][j] = pearsonEigenVectors[i][j].real
+				if type(pearsonEigenVectors[i][j]).__name__ == 'complex':
+					pearsonEigenVectors[i][j] = pearsonEigenVectors[i][j].real		      
+		"""
+        
+		if type(pearsonEigenValue[0]).__name__ == 'complex':
+		   pass
+		else:	
+            	   traitHeading = HT.Paragraph('PCA Traits',align='left', Class="title")  
+                   
+        	   tbl2 = self.calcPCATraits(traitDataList=traitDataList, nnCorr=nnCorr, NNN=NNN, pearsonEigenValue=pearsonEigenValue, 
+                                         pearsonEigenVectors=pearsonEigenVectors, form=form, fd=fd)
+                   #Buttons on search page
+                   #mintmap = HT.Input(type='button' ,name='mintmap',value='Multiple Mapping', onClick="databaseFunc(this.form,'showIntMap');",Class="button")
+                   addselect = HT.Input(type='button' ,name='addselect',value='Add to Collection', onClick="addRmvSelection('%s', this.form, 'addToSelection');"  % fd.RISet,Class="button")
+                   selectall = HT.Input(type='button' ,name='selectall',value='Select All', onClick="checkAll(this.form);",Class="button")
+                   reset = HT.Input(type='reset',name='',value='Select None',Class="button")
+                   updateNames = HT.Input(type='button', name='updateNames',value='Update Trait Names', onClick="editPCAName(this.form);", Class="button")
+                   chrMenu = HT.Input(type='hidden',name='chromosomes',value='all')
+                   
+                   """
+                   #need to be refined
+                   if fd.genotype.Mbmap:
+                       scaleMenu = HT.Select(name='scale')
+                       scaleMenu.append(tuple(["Genetic Map",'morgan']))
+                       scaleMenu.append(tuple(["Physical Map",'physic']))
+                   else:
+                       scaleMenu = ""
+                   """    
+                   
+                   tbl2.append(HT.TR(HT.TD(HT.P(),chrMenu,updateNames,selectall,reset,addselect,colspan=3)))
+        	   form.append(HT.P(),traitHeading,HT.Blockquote(tbl2))
+                
+                   plotHeading1 = HT.Paragraph('Scree Plot', Class="title") 
+                   TD_LR.append(plotHeading1)
+                   img1 = self.screePlot(NNN=NNN, pearsonEigenValue=pearsonEigenValue)
+    
+                   TD_LR.append(HT.Blockquote(img1))
+                   
+                   plotHeading2 = HT.Paragraph('Factor Loadings Plot', Class="title")
+                   TD_LR.append(plotHeading2)
+                   img2 = self.factorLoadingsPlot(pearsonEigenVectors=pearsonEigenVectors, traitList=traitList)
+                   
+                   TD_LR.append(HT.Blockquote(img2))                      
+
+        self.dict['body'] = str(TD_LR)
+    
+    def screePlot(self, NNN=0, pearsonEigenValue=None):
+
+        c1 = pid.PILCanvas(size=(700,500))
+        Plot.plotXY(canvas=c1, dataX=range(1,NNN+1), dataY=pearsonEigenValue, rank=0, labelColor=pid.blue,plotColor=pid.red, symbolColor=pid.blue, XLabel='Factor Number', connectdot=1,YLabel='Percent of Total Variance %', title='Pearson\'s R Scree Plot')
+        filename= webqtlUtil.genRandStr("Scree_")
+        c1.save(webqtlConfig.IMGDIR+filename, format='gif')
+        img=HT.Image('/image/'+filename+'.gif',border=0)
+        
+        return img
+    
+    def factorLoadingsPlot(self, pearsonEigenVectors=None, traitList=None):
+        
+        traitname = map(lambda X:str(X.name), traitList)
+        c2 = pid.PILCanvas(size=(700,500))
+        Plot.plotXY(c2, pearsonEigenVectors[0],pearsonEigenVectors[1], 0, dataLabel = traitname, labelColor=pid.blue, plotColor=pid.red, symbolColor=pid.blue,XLabel='Factor (1)', connectdot=1, YLabel='Factor (2)', title='Factor Loadings Plot (Pearson)', loadingPlot=1)
+        filename= webqtlUtil.genRandStr("FacL_")
+        c2.save(webqtlConfig.IMGDIR+filename, format='gif')
+        img = HT.Image('/image/'+filename+'.gif',border=0)
+     
+        return img
+    
+    def calcPCATraits(self, traitDataList=None, nnCorr=0, NNN=0, pearsonEigenValue=None, pearsonEigenVectors=None, form=None, fd=None):  
+       """
+       This function currently returns the html to be displayed instead of the traits themselves. Need to fix later.
+       """
+    
+       detailInfo = string.split(self.searchResult[0],':')
+    
+       self.sameProbeSet = 'yes'
+       for item in self.searchResult[1:]:
+           detailInfo2 = string.split(item,':')
+           if detailInfo[0] != detailInfo2[0] or detailInfo[1] != detailInfo2[1]:
+               self.sameProbeSet = None
+               break
+                
+       for item in traitDataList:
+           if len(item) != nnCorr:
+               return
+       infoStrains = []
+       infoStrainsPos = []
+       dataArray  = [[] for i in range(NNN)]
+    
+       for i in range(len(traitDataList[0])):
+           currentStrain = 1
+           for j in range(NNN):
+               if not traitDataList[j][i]:
+                   currentStrain = 0
+                   break
+           if currentStrain == 1:
+               infoStrains.append(fd.strainlist[i])
+               infoStrainsPos.append(i)
+               for j in range(NNN): 
+                   dataArray[j].append(traitDataList[j][i])
+
+    
+       self.cursor.execute('delete Temp, TempData FROM Temp, TempData WHERE Temp.DataId = TempData.Id and UNIX_TIMESTAMP()-UNIX_TIMESTAMP(CreateTime)>%d;' % webqtlConfig.MAXLIFE)
+
+       StrainIds = []
+       for item in infoStrains:
+           self.cursor.execute('SELECT Strain.Id FROM Strain,StrainXRef, InbredSet WHERE Strain.Name="%s" and Strain.Id = StrainXRef.StrainId and StrainXRef.InbredSetId = InbredSet.Id and InbredSet.Name = "%s"' % (item, fd.RISet))
+           StrainIds.append('%d' % self.cursor.fetchone()[0])
+    
+       """
+       #minimal 12 overlapping strains      
+       if len(dataArray[0]) < 12:
+           form.append(HT.P(),traitHeading,HT.Blockquote(HT.Paragraph('The number of overlapping strains is less than 12, no PCA scores computed.',align='left')))
+           self.dict['body'] = str(TD_LR)
+           return
+       """
+       dataArray = self.zScore(dataArray)
+       dataArray = numarray.array(dataArray)
+       dataArray2 = numarray.dot(pearsonEigenVectors,dataArray)
+    
+       tbl2 = HT.TableLite(cellSpacing=2,cellPadding=0,border=0, width="100%")
+    
+       ct0 = time.localtime(time.time())
+       ct = time.strftime("%B/%d %H:%M:%S",ct0)
+       if self.sameProbeSet:
+           newDescription = 'PCA Traits generated at %s from %s' % (ct,detailInfo[1])
+       else:
+           newDescription = 'PCA Traits generated at %s from traits selected' % ct
+    
+
+       j = 1
+       self.cursor.execute('SELECT Id  FROM InbredSet WHERE Name = "%s"' % fd.RISet)
+       InbredSetId = self.cursor.fetchall()[0][0]
+       user_ip = fd.remote_ip
+       if fd.formdata.getvalue("newNames"):
+           newNames = fd.formdata.getvalue("newNames").split(",")
+       else:
+       	   newNames = 0
+       
+       for item in dataArray2:
+           if pearsonEigenValue[j-1] < 100.0/NNN:
+               break
+           
+           if (newNames == 0):
+               description  = '%s : PC%02d' % (newDescription, j)              
+           else:    
+               description = '%s : %s' % (newDescription, newNames[j-1])           	   
+                  
+           self.cursor.execute('SELECT max(id) FROM TempData')
+           try:
+               DataId = self.cursor.fetchall()[0][0] + 1
+           except:
+               DataId = 1
+           newProbeSetID = webqtlUtil.genRandStr("PCA_Tmp_")
+           self.cursor.execute('insert into Temp(Name,description, createtime,DataId,InbredSetId,IP) values(%s,%s,Now(),%s,%s,%s)' ,(newProbeSetID, description, DataId,InbredSetId,user_ip))
+        
+           k = 0    
+           for StrainId in StrainIds:
+               self.cursor.execute('insert into TempData(Id, StrainId, value) values(%s,%s,%s)' % (DataId, StrainId, item[k]*(-1.0)))
+               k += 1
+           setDescription = HT.Div(id="pcaTrait%s" % j)
+           descriptionLink = HT.Href(text=description, url="javascript:showDatabase2('Temp','%s','')" % newProbeSetID, Class="fwn")
+           descriptionEdit = HT.Input(type='text', value='', name='editName%s' % j)
+           
+           #onBlur='editPDAName(this.form, %s);' % j
+           
+           setDescription.append(descriptionLink)
+           setDescription.append(descriptionEdit)
+           
+           traitName = "%s:%s" % ('Temp',newProbeSetID)
+           tbl2.append(HT.TR(HT.TD("%d."%j,align="right",valign="top"),HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=traitName),valign="top",width=50),HT.TD(setDescription)))
+           j += 1
+    
+       return tbl2
+    
+    def zScore(self,dataArray):
+        NN = len(dataArray[0])
+        if NN < 10:
+            return dataArray
+        else:
+            i = 0
+            for data in dataArray:
+                N = len(data)
+                S = reduce(lambda x,y: x+y, data, 0.)
+                SS = reduce(lambda x,y: x+y*y, data, 0.)
+                mean = S/N
+                var = SS - S*S/N
+                stdev = math.sqrt(var/(N-1))
+                data2 = map(lambda x:(x-mean)/stdev,data)
+                dataArray[i] = data2
+                i += 1
+            return dataArray
+    
+    def sortEigenVectors(self,vector):
+        try:
+            eigenValues = vector[0].tolist()
+            eigenVectors = vector[1].tolist()
+            combines = []
+            i = 0
+            for item in eigenValues:
+                combines.append([eigenValues[i],eigenVectors[i]])
+                i += 1
+            combines.sort(webqtlUtil.cmpEigenValue)
+            A = []
+            B = []
+            for item in combines:
+                A.append(item[0])
+                B.append(item[1])
+            sum = reduce(lambda x,y: x+y, A, 0.0)
+            A = map(lambda x:x*100.0/sum, A) 
+            return [A,B]
+        except:
+            return []
+
diff --git a/web/webqtl/correlationMatrix/TissueAbbreviationPage.py b/web/webqtl/correlationMatrix/TissueAbbreviationPage.py
new file mode 100755
index 00000000..ad8f0ac7
--- /dev/null
+++ b/web/webqtl/correlationMatrix/TissueAbbreviationPage.py
@@ -0,0 +1,79 @@
+# 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 2011/12/7
+#
+# Last updated by GeneNetwork Core Team 2011/12/7
+
+
+from base.templatePage import templatePage
+from htmlgen import HTMLgen2 as HT
+
+import string
+import os
+
+
+class TissueAbbreviationPage (templatePage):
+
+    def __init__(self,fd):
+		templatePage.__init__(self, fd)
+		
+		shortName=fd.formdata.getfirst("shortTissueName", ',')
+		fullName=fd.formdata.getfirst("fullTissueName", ',')
+		shortNameList=[]
+		fullNameList=[]
+		
+		if shortName:
+			shortNameList=shortName.split(',')
+			
+		if fullName:
+			fullNameList=fullName.split(',')
+			
+		tissueAbbrDict={}
+		for i, item in enumerate(shortNameList):
+			tissueAbbrDict[item]=fullNameList[i]	
+			
+		if tissueAbbrDict:
+		
+			# Creates the table for the fullname and shortname of Tissue
+			tissueAbbrTable = HT.TableLite(border=1, cellspacing=5, cellpadding=3, Class="collap")
+			shortNameList = tissueAbbrDict.keys()
+			shortNameList.sort()
+			abbrHeaderStyle="fs14 fwb ffl"
+			abbrStyle="fs14 fwn ffl"
+			
+			tissueAbbrTable.append(HT.TR(HT.TD('Abbr&nbsp;&nbsp;', Class=abbrHeaderStyle, NOWRAP = 1),HT.TD('Full Name&nbsp;&nbsp;', Class=abbrHeaderStyle, NOWRAP = 1)))
+			for item in shortNameList:
+				thisTR = HT.TR(HT.TD(item, Class=abbrStyle, NOWRAP = 1))
+				thisTR.append(HT.TD(tissueAbbrDict[item], Class=abbrStyle, NOWRAP = 1))
+
+				tissueAbbrTable.append(thisTR)
+				
+			self.dict['body'] = HT.TD(HT.Paragraph("Tissue Abbreviation", Class="title"), HT.Blockquote(tissueAbbrTable))
+			self.dict['title'] = "Tissue Abbreviation" 
+		else:
+			heading = "Tissue abbreviation"
+			detail = ["Cannot found Tissue Abbreviation. Please try again later."]
+			self.error(heading=heading,detail=detail)
+			return			
+           
+
diff --git a/web/webqtl/correlationMatrix/TissueCorrelationPage.py b/web/webqtl/correlationMatrix/TissueCorrelationPage.py
new file mode 100755
index 00000000..7cb86d8c
--- /dev/null
+++ b/web/webqtl/correlationMatrix/TissueCorrelationPage.py
@@ -0,0 +1,673 @@
+# 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)
+# user can search correlation value and P-Value by inputting one pair gene symbols or multiple gene symbols.
+
+# Created by GeneNetwork Core Team 2010/07/07
+# Last updated by NL, 2011/03/25
+
+from htmlgen import HTMLgen2 as HT
+import os
+import sys
+import time
+import string
+import pyXLWriter as xl
+import cPickle
+
+from base.templatePage import templatePage
+from base import webqtlConfig
+from base.webqtlTrait import webqtlTrait
+from correlationMatrix.tissueCorrelationMatrix import tissueCorrelationMatrix
+from utility import webqtlUtil
+from utility.THCell import THCell
+from utility.TDCell import TDCell
+
+
+#########################################
+#      Tissue Correlation Page
+#########################################
+
+class  TissueCorrelationPage(templatePage):
+
+	def __init__(self, fd):
+
+		templatePage.__init__(self, fd)
+				
+		if not self.openMysql():
+			return	
+		
+		#read input fields
+		self.action = fd.formdata.getvalue("action", "").strip()
+		self.geneSymbols = fd.formdata.getvalue("geneSymbols","").strip()
+		self.tissueProbeSetFeezeId = fd.formdata.getvalue("tissueProbeSetFeezeId", "").strip()
+		self.recordReturnNum = fd.formdata.getvalue("recordReturnNum", "0").strip()
+		self.calculateMethod = fd.formdata.getvalue("calculateMethod", "0").strip()
+		
+		TissueCorrMatrixObject = tissueCorrelationMatrix(tissueProbeSetFreezeId=self.tissueProbeSetFeezeId)
+		
+		if not self.geneSymbols:	
+			# default page 
+			
+			Heading = HT.Paragraph("Tissue Correlation", Class="title")
+			Intro = HT.Blockquote("This function computes correlations between transcript expression across different organs and tissues.")
+			Intro.append(HT.BR(),"Select a data set from the pull-down menu and then compute correlations.")
+
+			formName='searchTissueCorrelation'
+			form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), target='_blank',enctype='multipart/form-data', name= formName, submit=HT.Input(type='hidden'))
+			form.append(HT.Input(type="hidden", name="FormID", value=""))
+			form.append(HT.Input(type="hidden", name="action", value="disp"))
+			
+			# added by NL 10/12/2010, retreive dataSet info from TissueProbeSetFreeze to get all TissueProbeSetFreezeId, datasetName and FullName
+			tissProbeSetFreezeIds,dataSetNames,dataSetfullNames = TissueCorrMatrixObject.getTissueDataSet()
+
+			dataSetList=[]
+			for i in range(len(tissProbeSetFreezeIds)):
+				dataSetList.append((dataSetfullNames[i], tissProbeSetFreezeIds[i]))
+			dataSetMenu = HT.Select(dataSetList,name="tissueProbeSetFeezeId")
+
+			InfoFile =HT.Input(type="button", Class="button", value=" Info ", onClick="tissueDatasetInfo(this.form.tissueProbeSetFeezeId,%s);"%(dataSetNames))	
+			form.append(HT.Strong("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"),dataSetMenu,InfoFile,HT.BR());
+			
+			form.append(HT.BR(),HT.Strong("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Please enter only one gene symbol/ENTREZ gene Id per line."),HT.BR(),HT.Strong("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"),HT.Textarea(name="geneSymbols", rows=10, cols=50, text=""),HT.BR(),HT.BR())				
+			# calculate method radio button
+			calculateMethodMenu =HT.Input(type="radio", name="calculateMethod", value="0", checked="checked")
+			calculateMethodMenu1 =HT.Input(type="radio", name="calculateMethod", value="1")
+			# record Return method dropdown menu
+			recordReturnMenu = HT.Select(name="recordReturnNum")
+			recordReturnMenu.append(('Top 100','0'))
+			recordReturnMenu.append(('Top 200','1'))
+			recordReturnMenu.append(('Top 500','2'))
+			recordReturnMenu.append(('Top 1000','3'))
+			recordReturnMenu.append(('Top 2000','4'))
+			recordReturnMenu.append(('All','5'))	
+			
+			# working for input symbol has only one; 
+			form.append(HT.Strong("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"),HT.Span("Return:", Class="ffl fwb fs12"),HT.Strong("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"),recordReturnMenu,HT.BR());
+			form.append(HT.BR(),HT.Strong("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;"),'Pearson',calculateMethodMenu,"&nbsp;"*3,'Spearman Rank',calculateMethodMenu1,HT.BR(),HT.BR());
+			form.append(HT.Strong("&nbsp;&nbsp;&nbsp;"),HT.Input(type="button", value="&nbsp;Compute&nbsp;", Class="button",onClick="selectFormIdForTissueCorr('%s');"%formName))
+			form.append(HT.Strong("&nbsp;&nbsp;&nbsp;&nbsp;"),HT.Input(type="button", Class="button", value="&nbsp;Make Default&nbsp;", onClick = "makeTissueCorrDefault(this.form);"))
+
+			TD_LR = HT.TD(height=200,width="100%",bgcolor='#eeeeee',align="left")		
+			TD_LR.append(Heading,Intro,form)
+			self.content_type = 'text/html'
+			self.dict['js1'] = '<SCRIPT SRC="/javascript/correlationMatrix.js"></SCRIPT><BR>'
+			# get tissueProbesetFreezeId from cookie
+			self.dict['js2'] = 'onload ="getTissueCorrDefault(\'searchTissueCorrelation\');"'
+			self.dict['body'] = str(TD_LR)
+			self.dict['title'] = "Tissue Correlation"
+		elif self.action == 'disp':
+			TissueCount =TissueCorrMatrixObject.getTissueCountofCurrentDataset()
+	
+			# add by NL for first Note part in the tissue correlation page. 2010-12-23
+			note =""
+			dataSetName=""
+			datasetFullName=""
+			dataSetName, datasetFullName= TissueCorrMatrixObject.getFullnameofCurrentDataset()
+
+			noteURL = "../dbdoc/"+ dataSetName+".html"
+			noteText = " was used to compute expression correlation across %s samples of tissues and organs.&nbsp["%TissueCount			
+			# dataset download
+			datasetURL = "../dbdoc/"+ dataSetName+".xls"
+			datasetDownload =HT.Href(text="Download experiment data",url=datasetURL,Class='fs13',target="_blank")
+			note = HT.Blockquote(HT.Href(text=datasetFullName,url=noteURL,Class='fs13',target="_blank"),noteText, datasetDownload,"]",HT.BR())
+			
+			geneSymbolLst = [] # gene Symbol list
+			geneSymbolLst = TissueCorrMatrixObject.getGeneSymbolLst(self.geneSymbols)
+			
+			symbolCount = len(geneSymbolLst)			
+			# The input symbol limit is 100.
+			heading = "Tissue Correlation" 
+			if symbolCount > 100:
+				detail = ['The Gene symbols you have input are more than 100. Please limit them to 100.'] 
+				self.error(heading=heading,detail=detail)
+				return		
+			elif symbolCount==0:
+				detail = ['No Gene Symbol was input. No Tissue Correlation matrix generated.' ]
+				self.error(heading=heading,detail=detail)
+				return
+			else:
+				# search result page
+				# The input symbols should be no less than 1.				
+				self.content_type = 'text/html'				
+				if symbolCount == 1:	
+					self.displaySingleSymbolResultPage(primaryGeneSymbol=geneSymbolLst[0],datasetFullName=datasetFullName,tProbeSetFreezeId=self.tissueProbeSetFeezeId, TissueCorrMatrixObject =TissueCorrMatrixObject,recordReturnNum=self.recordReturnNum,method=self.calculateMethod, note=note,TissueCount =TissueCount)			
+				else:	
+					self.displayMultiSymbolsResultPage(geneSymbolLst=geneSymbolLst, symbolCount=symbolCount, tProbeSetFreezeId=self.tissueProbeSetFeezeId,TissueCorrMatrixObject =TissueCorrMatrixObject,note=note,TissueCount =TissueCount)
+
+		else:
+			heading = "Tissue Correlation"
+			detail = ['There\'s something wrong with input gene symbol(s), or the value of parameter [action] is not right.' ]
+			self.error(heading=heading,detail=detail)
+			return
+#############################
+# functions
+#############################
+
+	# result page when input symbol has only one
+	def displaySingleSymbolResultPage(self,primaryGeneSymbol=None, datasetFullName=None,tProbeSetFreezeId=None, TissueCorrMatrixObject =None,recordReturnNum=None,method=None,note=None,TissueCount =None):
+		formName = webqtlUtil.genRandStr("fm_")
+		form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data',name= formName, submit=HT.Input(type='hidden'))
+		# the following hidden elements are required parameter in Class(PlotCorrelationPage). So we need to define them here. 
+		form.append(HT.Input(type="hidden", name="action", value="disp"))
+		form.append(HT.Input(type="hidden", name="FormID", value="dispSingleTissueCorrelation"))
+		form.append(HT.Input(type="hidden", name="X_geneSymbol", value=""))
+		form.append(HT.Input(type="hidden", name="Y_geneSymbol", value=""))
+		form.append(HT.Input(type="hidden", name="ProbeSetID", value=""))
+		# RISet is not using in Tissue correlation, but is a required parameter in Class(PlotCorrelationPage). So we set dummy value(BXD). 
+		form.append(HT.Input(type="hidden", name="RISet", value="BXD"))
+		form.append(HT.Input(type="hidden", name="ShowLine", value="1"))
+		form.append(HT.Input(type="hidden", name="TissueProbeSetFreezeId", value=tProbeSetFreezeId))
+		form.append(HT.Input(type="hidden", name="rankOrder", value=0))	
+		
+		traitList =[]
+		try:
+			symbolCorrDict, symbolPvalueDict = TissueCorrMatrixObject.calculateCorrOfAllTissueTrait(primaryTraitSymbol=primaryGeneSymbol,method=method)
+		except:
+			heading = "Tissue Correlation"
+			detail = ['Please use the official NCBI gene symbol.' ]
+			self.error(heading=heading,detail=detail)			
+			return
+		
+		symbolList0,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict=TissueCorrMatrixObject.getTissueProbeSetXRefInfo(GeneNameLst=[])					
+		# In case, upper case and lower case issue of symbol, mappedByTargetList function will update input geneSymbolLst based on database search result
+		tempPrimaryGeneSymbol =self.mappedByTargetList(primaryList=symbolList0,targetList=[primaryGeneSymbol])
+		primaryGeneSymbol =tempPrimaryGeneSymbol[0]
+		
+		returnNum = self.getReturnNum(recordReturnNum)
+		symbolListSorted=[]
+		symbolList=[]
+		# get key(list) of symbolCorrDict(dict) based on sorting symbolCorrDict(dict) by its' value in desc order
+		symbolListSorted=sorted(symbolCorrDict, key=symbolCorrDict.get, reverse=True)
+		symbolList = self.mappedByTargetList(primaryList=symbolList0,targetList=symbolListSorted)
+		
+		if returnNum==None:
+			returnNum =len(symbolList0)
+			IntroReturnNum ="All %d "%returnNum
+		else:
+			IntroReturnNum ="The Top %d" %returnNum
+			
+		symbolList = symbolList[:returnNum]
+
+		pageTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="100%", border=0, align="Left")
+		
+		##############
+		# 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")
+		#There are 6 lines of header in this file. 
+		worksheet = self.createExcelFileWithTitleAndFooter(workbook=workbook, datasetName=datasetFullName, returnNumber=returnNum)
+		newrow = 6				
+		pageTable.append(HT.TR(HT.TD(xlsUrl,height=40)))
+		
+		# get header part of result table and export excel file
+		tblobj = {}
+		tblobj['header'], worksheet = self.getTableHeader( method=method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle)
+		newrow += 1
+
+		# get body part of result table and export excel file
+		tblobj['body'], worksheet = self.getTableBody(symbolCorrDict=symbolCorrDict, symbolPvalueDict=symbolPvalueDict,symbolList=symbolList,geneIdDict=geneIdDict,ChrDict=ChrDict,MbDict=MbDict,descDict=descDict,pTargetDescDict=pTargetDescDict,primarySymbol=primaryGeneSymbol,TissueCount=TissueCount, formName=formName, worksheet=worksheet, newrow=newrow,method=method)
+		workbook.close()
+		# creat object for result table for sort function
+		objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
+		cPickle.dump(tblobj, objfile)
+		objfile.close()	
+
+		sortby = ("tissuecorr", "down")
+		div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), Id="sortable")								
+		
+		if method =="0":
+			IntroMethod="Pearson\'s r "
+		else:
+			IntroMethod="Spearman\'s rho "				
+		Intro = HT.Blockquote('%s correlations ranked by the %s are displayed.' % (IntroReturnNum,IntroMethod),
+				' You can resort this list using the small arrowheads in the top row.')	
+		Intro.append(HT.BR(),' Click the correlation values to generate scatter plots. Select the symbol to open NCBI Entrez.')
+				
+		pageTable.append(HT.TR(HT.TD(div)))
+		form.append(HT.P(), HT.P(),pageTable)
+		corrHeading = HT.Paragraph('Tissue Correlation Table', Class="title")
+		TD_LR = HT.TD(height=200,width="100%",bgcolor='#eeeeee',align="left")
+		TD_LR.append(corrHeading,note,Intro, form, HT.P())
+
+		self.dict['body'] =  str(TD_LR)
+		self.dict['js1'] = '<SCRIPT SRC="/javascript/correlationMatrix.js"></SCRIPT><BR>'
+		self.dict['title'] = 'Tissue Correlation Result'						
+	
+		return
+
+	# result page when input symbols are more than 1	
+	def displayMultiSymbolsResultPage(self, geneSymbolLst=None, symbolCount=None, tProbeSetFreezeId=None,TissueCorrMatrixObject=None,note=None,TissueCount =None):
+		
+		formName = webqtlUtil.genRandStr("fm_")
+		form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data',name= formName, submit=HT.Input(type='hidden'))
+		# the following hidden elements are required parameter in Class(PlotCorrelationPage). So we need to define them here. 
+		form.append(HT.Input(type="hidden", name="action", value="disp"))
+		form.append(HT.Input(type="hidden", name="FormID", value="dispMultiTissueCorrelation"))
+		form.append(HT.Input(type="hidden", name="X_geneSymbol", value=""))
+		form.append(HT.Input(type="hidden", name="Y_geneSymbol", value=""))
+		form.append(HT.Input(type="hidden", name="ProbeSetID", value=""))
+		# RISet is not using in Tissue correlation, but is a required parameter in Class(PlotCorrelationPage). So we set dummy value(BXD). 
+		form.append(HT.Input(type="hidden", name="RISet", value="BXD"))
+		form.append(HT.Input(type="hidden", name="ShowLine", value="1"))
+		form.append(HT.Input(type="hidden", name="TissueProbeSetFreezeId", value=tProbeSetFreezeId))
+		form.append(HT.Input(type="hidden", name="rankOrder", value=0))			
+
+		# updated by NL, 2011-01-06, build multi list for later use to descrease access to db again
+		symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict = TissueCorrMatrixObject.getTissueProbeSetXRefInfo(GeneNameLst=geneSymbolLst)
+		# In case, upper case and lower case issue of symbol, mappedByTargetList function will update input geneSymbolLst based on database search result
+		geneSymbolLst =self.mappedByTargetList(primaryList=symbolList,targetList=geneSymbolLst)
+		
+		# Added by NL, 2011-01-06, get all shortNames, verboseNames, verboseNames2, verboseNames3, exportArray 
+		# for Short Label, Long Label, Export functions			
+		geneIdLst,shortNames, verboseNames, verboseNames2, verboseNames3, exportArray = self.getAllLabelsInfo(geneSymbolList =geneSymbolLst, geneIdDict=geneIdDict,ChrDict=ChrDict, MbDict=MbDict, descDict=descDict, pTargetDescDict=pTargetDescDict)
+	
+		heading = "Tissue Correlation Matrix" 
+		
+		#get correlation value and p value based on Gene Symbols list, and return the values in corrArray and pvArray seperately 
+		corrArray,pvArray = TissueCorrMatrixObject.getTissueCorrPvArray(geneNameLst=geneSymbolLst,dataIdDict=dataIdDict)
+
+		# in the matrix table, top right corner displays Spearman Rank Correlation's Values and P-Values for each pair of geneSymbols; 
+		#                      left bottom displays Pearson Correlation values and P-Vlues for each pair of geneSymbols.
+		tissueCorrMatrixHeading = HT.Paragraph(heading,Class="title")
+		tcmTable = HT.TableLite(Class="collap", border=0, cellspacing=1, cellpadding=5, width='100%')	
+		row1 = HT.TR(HT.TD(Class="fs14 fwb ffl b1 cw cbrb"),HT.TD('Spearman Rank Correlation (rho)' , Class="fs14 fwb ffl b1 cw cbrb", colspan= symbolCount+2,align="center"))
+		col1 = HT.TR(HT.TD("P e a r s o n &nbsp;&nbsp;&nbsp; r", rowspan= symbolCount+1,Class="fs14 fwb ffl b1 cw cbrb", width=10,align="center"),HT.TD("Gene Symbol",Class="fs13 fwb cb b1", width=300))
+		for i in range(symbolCount):
+			GeneSymbol=geneSymbolLst[i].strip()
+			geneId = geneIdLst[i]
+
+			if geneId!=0:
+				_url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % geneId
+				curURL = HT.Href(text=GeneSymbol,url=_url,Class='fs13',target="_blank")
+			else:
+				curURL = GeneSymbol
+			col1.append(HT.TD(curURL,Class="b1", align="center"))				
+
+		tcmTable.append(row1,col1)
+		# to decide to whether to show note for "*" or not
+		flag = 0
+		for i in range(symbolCount):
+			GeneSymbol=geneSymbolLst[i].strip()  
+			geneId = geneIdLst[i]
+			
+			newrow = HT.TR()
+			newrow.append(HT.Input(name="Symbol", value=GeneSymbol, type='hidden'))
+
+			if geneId!=0:
+				_url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" %geneId
+				geneIdURL = HT.Href(text="%s "%GeneSymbol,url=_url,Class="b1",target="_blank")
+			else:
+				# flag =1 will show note for "*"
+				flag = 1
+				geneIdURL =HT.Italic("%s"%GeneSymbol,HT.Font('*', color='red'))				
+			newrow.append(HT.TD(geneIdURL,shortNames[i],verboseNames[i],verboseNames2[i],verboseNames3[i], Class="b1", align="left",NOWRAP="ON"))
+
+			for j in range(symbolCount):
+				GeneSymbol2=geneSymbolLst[j].strip() 
+				corr = corrArray[i][j]
+				pValue = pvArray[i][j]
+				Color=''
+
+				if j==i:
+					newrow.append(HT.TD(HT.Font(HT.Italic("n"),HT.BR(),str(TissueCount),Class="fs11 fwn b1",align="center", color="000000"), bgColor='#cccccc', align="center", Class="b1", NOWRAP="ON"))
+					exportArray[i+1][j+1] = '%d/%d' % (TissueCount,TissueCount)
+				else:
+					if corr:
+						corr = float(corr)
+						tCorr = "%2.3f" % corr
+						pValue = float(pValue)
+						tPV = "%2.3f" % pValue
+
+						# updated by NL, based on Rob's requirement: delete p value, 2010-02-14	
+						# set color for cells by correlationValue
+						if corr > 0.7:
+							fontcolor="red"
+						elif corr > 0.5:
+							fontcolor="#FF6600"
+						elif corr < -0.7:
+							fontcolor="blue"
+						elif corr < -0.5:
+							fontcolor="#009900"
+						else:
+							fontcolor ="#000000"
+							
+						# set label for cells
+						# if rank is equal to 0, pearson correlation plot will be the first one; 
+						# if rank is equal to 1, spearman ran correlation plot will be the first one.
+						if j>i:				
+							exportArray[i+1][j+1] =tCorr+"/"+tPV
+							rank =1
+						elif j<i:
+							exportArray[i+1][j+1] =tCorr+"/"+tPV
+							rank =0
+						
+						tCorrStr= tCorr
+						tPVStr = tPV 
+						tCorrPlotURL = "javascript:showTissueCorrPlot('%s','%s','%s',%d)" %(formName,GeneSymbol, GeneSymbol2,rank) 					
+						corrURL= HT.Href(text=HT.Font(tCorrStr,HT.BR(),color=fontcolor, Class="fs11 fwn"), url = tCorrPlotURL)						
+					else:
+						corr = 'N/A'
+						corrURL= HT.Font(corr)
+						exportArray[i+1][j+1] ="---/---"
+						
+					newrow.append(HT.TD(corrURL,bgColor=Color,Class="b1",NOWRAP="ON",align="middle"))
+
+			tcmTable.append(newrow)
+
+										
+		
+		Intro = HT.Blockquote('Lower left cells list Pearson ',HT.EM('r'),' values; upper right cells list Spearman rho values. Each cell also contains the n samples of tissues and organs. Values higher than 0.7 are displayed in ',HT.Font('red', color='red'),'; those between 0.5 and 0.7 in  ',HT.Font('orange', color='#FF6600'),'; Values lower than -0.7 are in ',HT.Font('blue', color='blue'),'; between -0.5 and -0.7 in ',HT.Font('green', color='#009900'),'.', HT.BR(),HT.BR(), HT.Strong('Make scatter plots by clicking on cell values '),'(', HT.EM('r'),' or rho). ', Class="fs13 fwn")
+        		
+		shortButton = HT.Input(type='button' ,name='dispShort',value=' Short Labels ', onClick="displayTissueShortName();",Class="button")
+		verboseButton = HT.Input(type='button' ,name='dispVerbose',value=' Long Labels ', onClick="displayTissueVerboseName();", Class="button")
+		exportbutton = HT.Input(type='button',  name='export', value='Export', onClick="exportTissueText(allCorrelations);",Class="button")
+		lableNote = HT.Blockquote(HT.Italic(HT.Font('*', color='red',Class="fs9 fwn"), ' Symbol(s) can not be found in database.'))		
+		
+		# flag =1 will show note for "*", which means there's unidentified symbol.
+		if flag==1:
+			form.append(HT.Blockquote(tcmTable,lableNote,HT.P(),shortButton,verboseButton,exportbutton))
+		else:
+			form.append(HT.Blockquote(tcmTable,HT.P(),shortButton,verboseButton,exportbutton))
+			
+		exportScript = """
+			<SCRIPT language=JavaScript>
+			var allCorrelations = %s;
+			</SCRIPT>
+		"""
+		exportScript = exportScript % str(exportArray)
+		self.dict['js1'] = exportScript+'<SCRIPT SRC="/javascript/correlationMatrix.js"></SCRIPT><BR>'
+		
+		TD_LR = HT.TD(colspan=2,width="100%",bgcolor="#eeeeee")
+		TD_LR.append(tissueCorrMatrixHeading,note,Intro,form,HT.P())
+		self.dict['body'] = str(TD_LR)
+		self.dict['title'] = 'Tissue Correlation Result'
+		return
+
+	# Added by NL, 2011-01-06, get all shortNames, verboseNames, verboseNames2, verboseNames3, exportArray 
+	# for Short Label, Long Label, Export functions	
+	def getAllLabelsInfo(self, geneSymbolList=None,geneIdDict=None,ChrDict=None,MbDict=None,descDict=None,pTargetDescDict=None):
+	
+		symbolCount= len(geneSymbolList)
+		geneIdLst =[]
+		exportArray = [([0] * (symbolCount+1))[:] for i in range(symbolCount+1)]
+		exportArray[0][0] = 'Tissue Correlation'
+		shortNames = []
+		verboseNames = []
+		verboseNames2 = []
+		verboseNames3 = []	
+		
+		# added by NL, 2010-12-21, build DIV and array for short label, long label and export functions
+		for i, geneSymbolItem in enumerate(geneSymbolList):
+			geneSymbol =geneSymbolItem.lower()
+			_shortName =HT.Italic("%s" %geneSymbolItem)
+			_verboseName =''
+			_verboseName2 = ''
+			_verboseName3 = ''
+			if geneIdDict.has_key(geneSymbol):
+				geneIdLst.append(geneIdDict[geneSymbol])
+			else:
+				geneIdLst.append(0)
+			if ChrDict.has_key(geneSymbol) and MbDict.has_key(geneSymbol):
+				_verboseName = ' on Chr %s @ %s Mb' % (ChrDict[geneSymbol],MbDict[geneSymbol])
+			if descDict.has_key(geneSymbol):
+				_verboseName2 = '%s' % (descDict[geneSymbol])
+			if pTargetDescDict.has_key(geneSymbol):
+				_verboseName3 = '%s' % (pTargetDescDict[geneSymbol])         
+
+			shortName = HT.Div(id="shortName_" + str(i), style="display:none")
+			shortName.append('Symbol: ')
+			shortName.append(_shortName)
+			shortNames.append(shortName)
+			
+			verboseName = HT.Div(id="verboseName_" + str(i), style="display:none")
+			verboseName.append(_shortName)
+			verboseName.append(_verboseName)
+			verboseNames.append(verboseName)
+			verboseName2 = HT.Div(id="verboseName2_" + str(i), style="display:none")
+			verboseName2.append(_verboseName2)
+			verboseNames2.append(verboseName2)
+			verboseName3 = HT.Div(id="verboseName3_" + str(i), style="display:none")
+			verboseName3.append(_verboseName3)
+			verboseNames3.append(verboseName3)
+			
+			# exportTissueText in webqtl.js is using '/' as delimilator; add '/', otherwise the last letter in geneSymbol will missing
+			exportArray[i+1][0] =geneSymbolItem+ '/' + geneSymbolItem + '/' +geneSymbolItem + ':' + str(_verboseName) + ' : ' + str(_verboseName2) + ' : ' + str(_verboseName3)
+			exportArray[0][i+1] =geneSymbolItem+ '/'	
+
+		return geneIdLst,shortNames, verboseNames, verboseNames2, verboseNames3, exportArray
+
+
+########################################################################
+# functions for display and download when input symbol has only one    #
+########################################################################
+
+	# build header and footer parts for export excel file
+	def createExcelFileWithTitleAndFooter(self, workbook=None, datasetName=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
+		worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle)
+		worksheet.write([2, 0], "Dataset : %s" % datasetName, titleStyle)
+		worksheet.write([3, 0], "Date : %s" % time.strftime("%B %d, %Y", time.gmtime()), titleStyle)
+		worksheet.write([4, 0], "Time : %s GMT" % time.strftime("%H:%M ", time.gmtime()), titleStyle)
+		worksheet.write([5, 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([8 + 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([9 + returnNumber, 0], "PLEASE RETAIN DATA SOURCE INFORMATION WHENEVER POSSIBLE", titleStyle)
+
+		return worksheet
+
+	# build header of table when input symbol has only one
+	def getTableHeader(self, method='0', worksheet=None, newrow=None, headingStyle=None):
+
+		tblobj_header = []
+		exportList=[]
+		header=[]
+		header = [THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), sort=0),
+								  THCell(HT.TD('Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="symbol", idx=1),
+								  THCell(HT.TD('Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="desc", idx=2),
+								  THCell(HT.TD('Location',HT.BR(),'Chr and Mb ', Class="fs13 fwb ffl b1 cw cbrb"), text="location", idx=3),
+								  THCell(HT.TD('N Cases',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="nstr", idx=4)]
+		if method =="0":# Pearson Correlation		
+			header.append(	THCell(HT.TD(HT.Href(
+										text = HT.Span(' r ', HT.Sup('  ?', style="color:#f00"),HT.BR(),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=5))
+			header.append(	THCell(HT.TD(HT.Href(
+										text = HT.Span(' p(r) ', HT.Sup('  ?', style="color:#f00"),HT.BR(),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=6))
+							
+			exportList =[ 'Gene ID',  'Symbol', 'Description', 'Location', 'N Cases', ' r ', ' p(r) ']
+			
+		else:# Spearman Correlation
+			header.append(	THCell(HT.TD(HT.Href(
+										text = HT.Span(' rho ', HT.Sup('  ?', style="color:#f00"),HT.BR(),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=5))
+			header.append(	THCell(HT.TD(HT.Href(
+										text = HT.Span('p(rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), 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=6))
+			exportList = ['Gene ID', 'Symbol', 'Description', 'Location', 'N Cases','rho', ' p(rho) ']											
+		
+		# build header of excel for download function
+		for ncol, item in enumerate(exportList):
+			worksheet.write([newrow, ncol], item, headingStyle)
+			worksheet.set_column([ncol, ncol], 2*len(item))
+
+		tblobj_header.append(header)
+
+		return tblobj_header, worksheet
+
+	# build body of table when input symbol has only one
+	def getTableBody(self, symbolCorrDict={}, symbolPvalueDict={},symbolList=[],geneIdDict={},ChrDict={},MbDict={},descDict={},pTargetDescDict={},primarySymbol=None, TissueCount=None,formName=None, worksheet=None, newrow=None,method="0"):
+		
+		tblobj_body = []
+		
+		for symbolItem in symbolList:
+			symbol =symbolItem.lower()
+			if symbol:
+				pass
+			else:
+				symbol ="N/A"
+				
+			if geneIdDict.has_key(symbol) and geneIdDict[symbol]:
+				geneId = geneIdDict[symbol]
+				ncbiUrl = HT.Href(text="NCBI",target='_blank',url=webqtlConfig.NCBI_LOCUSID % geneIdDict[symbol], Class="fs10 fwn")				
+			else:
+				geneId ="N/A"
+				symbolItem =symbolItem.replace('"','') # some symbol is saved in ["symbol"]format
+				ncbiUrl = HT.Href(text="NCBI",target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gene&term=%s" % symbol, Class="fs10 fwn")
+			
+			_Species="mouse"
+			similarTraitUrl = "%s?cmd=sch&gene=%s&alias=1&species=%s" % (os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), symbolItem, _Species)	
+			gnUrl = HT.Href(text="GN",target='_blank',url=similarTraitUrl, Class="fs10 fwn")
+			
+			tr = []
+			# updated by NL, 04/25/2011: add checkbox and highlight function
+			# first column of table
+			# updated by NL. 12-7-2011
+			tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="tissueResult",value=symbol, onClick="highlight(this)"), align='right',Class="fs12 fwn b1 c222 fsI",nowrap='ON'),symbol,symbol))
+			# updated by NL, 04/26/2011: add GN and NCBI links
+			#gene symbol (symbol column)
+			tr.append(TDCell(HT.TD(HT.Italic(symbolItem), HT.BR(),gnUrl,"&nbsp;&nbsp|&nbsp;&nbsp", ncbiUrl, Class="fs12 fwn b1 c222"),symbolItem, symbolItem))
+
+			#description and probe target description(description column)
+			description_string=''
+			if descDict.has_key(symbol):
+				description_string = str(descDict[symbol]).strip()
+			if pTargetDescDict.has_key(symbol):
+				target_string = str(pTargetDescDict[symbol]).strip()
+
+			description_display = ''
+			if len(description_string) > 1 and description_string != 'None':
+				description_display = description_string
+			else:
+				description_display = symbolItem
+
+			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))
+
+			#trait_location_value is used for sorting (location column)
+			trait_location_repr = 'N/A'
+			trait_location_value = 1000000
+
+			if ChrDict.has_key(symbol) and MbDict.has_key(symbol):
+				
+				if ChrDict[symbol] and MbDict[symbol]:		
+					mb = float(MbDict[symbol])
+					try:
+						trait_location_value = int(ChrDict[symbol])*1000 + mb
+					except:
+						if ChrDict[symbol].upper() == 'X':
+							trait_location_value = 20*1000 + mb
+						else:
+							trait_location_value = ord(str(ChrDict[symbol]).upper()[0])*1000 + mb
+
+					trait_location_repr = 'Chr%s: %.6f' % (ChrDict[symbol], mb )
+				else:
+					trait_location_repr="N/A"
+					trait_location_value ="N/A"
+					
+			tr.append(TDCell(HT.TD(trait_location_repr, Class="fs12 fwn b1 c222", nowrap="on"), trait_location_repr, trait_location_value))
+			
+            # number of overlaped cases  (N Case column)
+			tr.append(TDCell(HT.TD(TissueCount, Class="fs12 fwn ffl b1 c222", align='right'),TissueCount,TissueCount))
+			
+			#tissue correlation (Tissue r column)
+			TCorr = 0.0
+			TCorrStr = "N/A"
+			if symbolCorrDict.has_key(symbol):				
+				TCorr = symbolCorrDict[symbol]
+				TCorrStr = "%2.3f" % TCorr
+				symbol2 =symbolItem.replace('"','') # some symbol is saved in "symbol" format
+				# add a new parameter rankOrder for js function 'showTissueCorrPlot'
+				rankOrder = int(method)
+				TCorrPlotURL = "javascript:showTissueCorrPlot('%s','%s','%s',%d)" %(formName, primarySymbol, symbol2,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)))
+
+			#p value of tissue correlation (Tissue p(r) column)
+			TPValue = 1.0
+			TPValueStr = "N/A"
+			if symbolPvalueDict.has_key(symbol):
+				TPValue = symbolPvalueDict[symbol]
+				#TPValueStr = "%2.3f" % TPValue
+				TPValueStr=webqtlUtil.SciFloat(TPValue)
+			tr.append(TDCell(HT.TD(TPValueStr, Class="fs12 fwn b1 c222", align='right'), TPValueStr, TPValue))
+
+			tblobj_body.append(tr)
+			# build body(records) of excel for download function			
+			for ncol, item in enumerate([geneId, symbolItem, description_display, trait_location_repr,TissueCount, TCorr, TPValue]):
+				worksheet.write([newrow, ncol], item)
+
+			newrow += 1
+
+		return tblobj_body, worksheet
+
+		
+	# get return number of records when input symbol has only one
+	def getReturnNum(self,recordReturnNum="0"):
+		if recordReturnNum=="0":
+			returnNum=100
+		elif recordReturnNum=="1":
+			returnNum=200
+		elif recordReturnNum=="2":
+			returnNum=500
+		elif recordReturnNum=="3":
+			returnNum=1000
+		elif recordReturnNum=="4":
+			returnNum=2000
+		elif recordReturnNum=="5":
+			returnNum= None
+			
+		return returnNum
+		
+	# map list based on the order of target List
+	# if item.lower() exist in both lists, then compare the difference of item's original value of two lists
+	# if not equal, then replace the item in targetList by using the item  in primaryList(list from database) 
+	
+	def mappedByTargetList(self,primaryList=[],targetList=[]):
+
+		tempPrimaryList =[x.lower() for x in primaryList]
+		testTargetList =[y.lower() for y in targetList]
+	
+		for i, item in enumerate(tempPrimaryList):
+			if item in testTargetList:
+				index = testTargetList.index(item)
+				if primaryList[i]!=targetList[index]:
+					targetList[index]= primaryList[i]
+
+		return	targetList		
diff --git a/web/webqtl/correlationMatrix/__init__.py b/web/webqtl/correlationMatrix/__init__.py
new file mode 100755
index 00000000..e69de29b
--- /dev/null
+++ b/web/webqtl/correlationMatrix/__init__.py
diff --git a/web/webqtl/correlationMatrix/tissueCorrelationMatrix.py b/web/webqtl/correlationMatrix/tissueCorrelationMatrix.py
new file mode 100755
index 00000000..23dc14eb
--- /dev/null
+++ b/web/webqtl/correlationMatrix/tissueCorrelationMatrix.py
@@ -0,0 +1,132 @@
+# 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 2010/11/10
+#
+# Last updated by Ning Liu, 2011/01/26
+
+
+#tissueCorrelationMatrix: funciton part for TissueCorrelationPage.py
+from htmlgen import HTMLgen2 as HT
+from correlation import correlationFunction
+from dbFunction import webqtlDatabaseFunction
+import sys
+
+#########################################
+#      Tissue Correlation Page
+#########################################
+
+class  tissueCorrelationMatrix:
+	def __init__(self,tissueProbeSetFreezeId=None):
+		
+		#initialize parameters
+		self.tProbeSetFreezeId = tissueProbeSetFreezeId
+		self.cursor = webqtlDatabaseFunction.getCursor()
+
+
+
+	#retreive dataSet info from database table TissueProbeSetFreeze to get all TissueProbeSetFreezeId(List), Name(List) and FullName(List)
+	def getTissueDataSet(self):	
+		tissProbeSetFreezeIds,Names,fullNames = webqtlDatabaseFunction.getTissueDataSet(cursor=self.cursor)
+		return tissProbeSetFreezeIds,Names,fullNames
+
+
+	#retrieve DatasetName, DatasetFullName based on TissueProbeSetFreezeId, return DatasetName(string), DatasetFullName(string)
+	def getFullnameofCurrentDataset(self):
+	
+		DatasetName, DatasetFullName =webqtlDatabaseFunction.getDatasetNamesByTissueProbeSetFreezeId(cursor=self.cursor, TissueProbeSetFreezeId=self.tProbeSetFreezeId)		
+		return DatasetName, DatasetFullName
+
+				
+	#retrieve how many tissue used in the specific dataset based on TissueProbeSetFreezeId, return TissueCount(int)
+	def getTissueCountofCurrentDataset(self):
+	
+		TissueCount =webqtlDatabaseFunction.getTissueCountByTissueProbeSetFreezeId(cursor=self.cursor,TissueProbeSetFreezeId=self.tProbeSetFreezeId)
+		return TissueCount
+
+
+		
+	#retrieve corrArray(array), pvArray(array) for display by calling  calculation function:calZeroOrderCorrForTiss
+	def getTissueCorrPvArray(self,geneNameLst=None,dataIdDict=None):	
+		#retrieve SymbolValuePairDict(Dict), dictionary of Symbol and Value Pair.key is symbol, value is one list of expression values of one probeSet
+		symbolValuepairDict =correlationFunction.getGeneSymbolTissueValueDict(cursor=self.cursor,symbolList=geneNameLst,dataIdDict=dataIdDict)
+		corrArray,pvArray = correlationFunction.getCorrPvArray(cursor=self.cursor,priGeneSymbolList=geneNameLst,symbolValuepairDict=symbolValuepairDict)
+		return corrArray,pvArray
+
+
+		
+	#retrieve symbolList,geneIdList,dataIdList,ChrList,MbList,descList,pTargetDescList (all are list type) to 
+	#get multi lists for short and long label functions, and for getSymbolValuePairDict and 
+	#getGeneSymbolTissueValueDict to build dict to get CorrPvArray
+	def getTissueProbeSetXRefInfo(self,GeneNameLst=[]):
+		symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict =correlationFunction.getTissueProbeSetXRefInfo(cursor=self.cursor,GeneNameLst=GeneNameLst,TissueProbeSetFreezeId=self.tProbeSetFreezeId)
+		return symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict		
+
+
+
+	#retrieve corrArray(array), pvArray(array) for gene symbol pair
+	def getCorrPvArrayForGeneSymbolPair(self,geneNameLst=None):
+		corrArray = None
+		pvArray = None
+
+		if len(geneNameLst) == 2:
+			#retrieve SymbolValuePairDict(Dict), dictionary of Symbol and Value Pair.key is symbol, value is one list of expression values of one probeSet
+			symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict =correlationFunction.getTissueProbeSetXRefInfo(cursor=self.cursor,GeneNameLst=geneNameLst,TissueProbeSetFreezeId=self.tProbeSetFreezeId)	
+			symbolValuepairDict =correlationFunction.getGeneSymbolTissueValueDict(cursor=self.cursor,symbolList=geneNameLst,dataIdDict=dataIdDict)
+			corrArray,pvArray = correlationFunction.getCorrPvArray(cursor=self.cursor,priGeneSymbolList=geneNameLst,symbolValuepairDict=symbolValuepairDict)
+
+		return corrArray,pvArray
+
+
+	#retrieve symbolCorrDict(dict), symbolPvalueDict(dict) to get all tissues' correlation value and P value; key is symbol	
+	def calculateCorrOfAllTissueTrait(self, primaryTraitSymbol=None, method='0'):
+		symbolCorrDict, symbolPvalueDict = correlationFunction.calculateCorrOfAllTissueTrait(cursor=self.cursor, primaryTraitSymbol=primaryTraitSymbol, TissueProbeSetFreezeId=self.tProbeSetFreezeId,method=method)
+		
+		return symbolCorrDict, symbolPvalueDict
+
+	#Translate GeneId to gene symbol and keep the original order.
+	def getGeneSymbolLst(self, geneSymbols=None):
+		geneSymbolLst=[]
+		geneIdLst=[]
+		#split the input string at every occurrence of the delimiter '\r', and return the substrings in an array. 
+		tokens=geneSymbols.strip().split('\r')
+
+		#Ning: To keep the original order of input symbols and GeneIds
+		for i in tokens:
+			i=i.strip()
+			if (len(i) >0) and (i not in geneSymbolLst):
+				geneSymbolLst.append(i)
+				# if input includes geneId(s), then put it/them into geneIdLst
+				if i.isdigit():
+					geneIdLst.append(i)
+
+		#Ning: Replace GeneId with symbol if applicable
+		if len(geneIdLst)>0:
+			# if input includes geneId(s), replace geneId by geneSymbol; 
+			geneIdSymbolPair =webqtlDatabaseFunction.getGeneIdSymbolPairByGeneId(cursor=self.cursor, geneIdLst =geneIdLst)
+			for geneId in geneIdLst:
+				if geneIdSymbolPair[geneId]:
+					index = geneSymbolLst.index(geneId)
+					geneSymbolLst[index] =geneIdSymbolPair[geneId]
+
+		return geneSymbolLst
+			
+		
+