def calculate_correlation(self, values, method):
"""Calculate the correlation value and p value according to the method specified"""
#ZS: This takes the list of values of the trait our selected trait is being correlated against and removes the values of the samples our trait has no value for
#There's probably a better way of dealing with this, but I'll have to ask Christian
updated_raw_values = []
updated_values = []
for i in range(len(values)):
if values[i] != "None":
updated_raw_values.append(self.raw_values[i])
updated_values.append(values[i])
self.raw_values = updated_raw_values
values = updated_values
if method == METHOD_SAMPLE_PEARSON or method == METHOD_LIT or method == METHOD_TISSUE_PEARSON:
corr, nOverlap = webqtlUtil.calCorrelation(self.raw_values, values, len(values))
else:
corr, nOverlap = webqtlUtil.calCorrelationRank(self.raw_values, values, len(values))
self.correlation = corr
self.overlap = nOverlap
if self.overlap < 3:
self.p_value = 1.0
else:
#ZS - This is probably the wrong way to deal with this. Correlation values of 1.0 definitely exist (the trait correlated against itself), so zero division needs to br prevented.
if abs(self.correlation) >= 1.0:
self.p_value = 0.0
else:
ZValue = 0.5*log((1.0+self.correlation)/(1.0-self.correlation))
ZValue = ZValue*sqrt(self.overlap-3)
self.p_value = 2.0*(1.0 - reaper.normp(abs(ZValue)))
def calculate_correlation(self, values, method):
"""Calculate the correlation value and p value according to the method specified"""
# ZS: This takes the list of values of the trait our selected trait is being correlated against and removes the values of the samples our trait has no value for
# There's probably a better way of dealing with this, but I'll have to ask Christian
updated_raw_values = []
updated_values = []
for i in range(len(values)):
if values[i] != "None":
updated_raw_values.append(self.raw_values[i])
updated_values.append(values[i])
self.raw_values = updated_raw_values
values = updated_values
if method == METHOD_SAMPLE_PEARSON or method == METHOD_LIT or method == METHOD_TISSUE_PEARSON:
corr, nOverlap = webqtlUtil.calCorrelation(self.raw_values, values, len(values))
else:
corr, nOverlap = webqtlUtil.calCorrelationRank(self.raw_values, values, len(values))
self.correlation = corr
self.overlap = nOverlap
if self.overlap < 3:
self.p_value = 1.0
else:
# ZS - This is probably the wrong way to deal with this. Correlation values of 1.0 definitely exist (the trait correlated against itself), so zero division needs to br prevented.
if abs(self.correlation) >= 1.0:
self.p_value = 0.0
else:
ZValue = 0.5 * log((1.0 + self.correlation) / (1.0 - self.correlation))
ZValue = ZValue * sqrt(self.overlap - 3)
self.p_value = 2.0 * (1.0 - reaper.normp(abs(ZValue)))
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:
if thisTrait.symbol:
abbreviation = thisTrait.symbol
else:
abbreviation = ''
_shortName = 'Symbol: %s ' % thisTrait.symbol
if thisTrait.symbol:
_verboseName = thisTrait.symbol
else:
_verboseName = ''
_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 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':
if False:
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 __init__(self,fd,InputData=None):
templatePage.__init__(self, fd)
if not self.openMysql():
return
if not fd.genotype:
fd.readGenotype()
self.searchResult = fd.formdata.getvalue('searchResult')
self.tissueProbeSetFeezeId = "1" #XZ, Jan 03, 2010: currently, this dataset is "UTHSC Illumina V6.2 RankInv B6 D2 average CNS GI average (May 08)"
TissueCorrMatrixObject = tissueCorrelationMatrix(tissueProbeSetFreezeId=self.tissueProbeSetFeezeId)
if type("1") == type(self.searchResult):
self.searchResult = string.split(self.searchResult, '\t')
if (not self.searchResult or (len(self.searchResult) < 2)):
heading = 'Network Graph'
detail = ['You need to select at least two traits in order to generate Network Graph.']
self.error(heading=heading,detail=detail)
print 'Content-type: text/html\n'
self.write()
return
if self.searchResult:
if len(self.searchResult) > webqtlConfig.MAXCORR:
heading = 'Network Graph'
detail = ['In order to display Network Graph properly, Do not select more than %d traits for Network Graph.' % webqtlConfig.MAXCORR]
self.error(heading=heading,detail=detail)
print 'Content-type: text/html\n'
self.write()
return
else:
pass
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 = 'Network Graph'
detail = [HT.Font('Error : ',color='red'),HT.Font('Error occurs while retrieving data from database.',color='black')]
self.error(heading=heading,detail=detail)
print 'Content-type: text/html\n'
self.write()
return
NNN = len(traitList)
if NNN < 2:
templatePage.__init__(self, fd)
heading = 'Network Graph'
detail = ['You need to select at least two traits in order to generate a Network Graph']
print 'Content-type: text/html\n'
self.write()
return
else:
pearsonArray = [([0] * (NNN))[:] for i in range(NNN)]
spearmanArray = [([0] * (NNN))[:] for i in range(NNN)]
GeneIdArray = []
GeneSymbolList = [] #XZ, Jan 03, 2011: holds gene symbols for calculating tissue correlation
traitInfoArray = []
i = 0
nnCorr = len(fd.strainlist)
for i, thisTrait in enumerate(traitList):
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)
pearsonArray[i][j] = corr
pearsonArray[j][i] = corr
elif j == i:
pearsonArray[i][j] = 1.0
spearmanArray[i][j] = 1.0
else:
corr,nOverlap = webqtlUtil.calCorrelationRank(traitDataList[i],traitDataList[j],nnCorr)
spearmanArray[i][j] = corr
spearmanArray[j][i] = corr
GeneId1 = None
tmpSymbol = None
if thisTrait.db.type == 'ProbeSet':
try:
GeneId1 = int(thisTrait.geneid)
except:
GeneId1 = 0
if thisTrait.symbol:
tmpSymbol = thisTrait.symbol.lower()
GeneIdArray.append(GeneId1)
GeneSymbolList.append(tmpSymbol)
_traits = []
_matrix = []
for i in range(NNN):
turl = webqtlConfig.CGIDIR + webqtlConfig.SCRIPTFILE + '?FormID=showDatabase&database=%s&ProbeSetID=%s' % (traitList[i].db.name, traitList[i].name)
if traitList[i].cellid:
turl += "&CellID=%s" % traitList[i].cellid
if traitList[i].db.type == 'ProbeSet':
if traitList[i].symbol:
_symbol = traitList[i].symbol
else:
_symbol = 'unknown'
elif traitList[i].db.type == 'Publish':
_symbol = traitList[i].name
if traitList[i].confidential:
if webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=traitList[i].authorized_users):
if traitList[i].post_publication_abbreviation:
_symbol = traitList[i].post_publication_abbreviation
else:
if traitList[i].pre_publication_abbreviation:
_symbol = traitList[i].pre_publication_abbreviation
else:
if traitList[i].post_publication_abbreviation:
_symbol = traitList[i].post_publication_abbreviation
#XZ, 05/26/2009: Xiaodong add code for Geno data
elif traitList[i].db.type == 'Geno':
_symbol = traitList[i].name
else:
_symbol = traitList[i].description
#####if this trait entered by user
if _symbol.__contains__('entered'):
_symbol = _symbol[:_symbol.index('entered')]
#####if this trait generaged by genenetwork
elif _symbol.__contains__('generated'):
_symbol = _symbol[_symbol.rindex(':')+1:]
newTrait = smpTrait.Trait(name=str(traitList[i]), href=turl, symbol=_symbol)
newTrait.color = "black"
_traits.append(newTrait)
for j in range(i+1, NNN):
dataPoint = smpTrait.RawPoint(i, j)
dataPoint.spearman = spearmanArray[i][j]
dataPoint.pearson = pearsonArray[i][j]
#XZ: get literature correlation info.
if GeneIdArray[i] and GeneIdArray[j]:
if GeneIdArray[i] == GeneIdArray[j]:
dataPoint.literature = 1
else:
self.cursor.execute("SELECT Value from LCorrRamin3 WHERE (GeneId1 = %d and GeneId2 = %d) or (GeneId1 = %d and GeneId2 = %d)" % (GeneIdArray[i], GeneIdArray[j], GeneIdArray[j], GeneIdArray[i]))
try:
dataPoint.literature = self.cursor.fetchone()[0]
except:
dataPoint.literature = 0
else:
dataPoint.literature = 0
#XZ: get tissue correlation info
if GeneSymbolList[i] and GeneSymbolList[j]:
dataPoint.tissue = 0
geneSymbolPair = []
geneSymbolPair.append(GeneSymbolList[i])
geneSymbolPair.append(GeneSymbolList[j])
corrArray,pvArray = TissueCorrMatrixObject.getCorrPvArrayForGeneSymbolPair(geneNameLst=geneSymbolPair)
if corrArray[1][0]:
dataPoint.tissue = corrArray[1][0]
else:
dataPoint.tissue = 0
_matrix.append(dataPoint)
OrigDir = os.getcwd()
sessionfile = fd.formdata.getvalue('session')
inputFilename = fd.formdata.getvalue('inputFile')
#If there is no sessionfile generate one and dump all matrix/trait values
if not sessionfile:
filename = webqtlUtil.generate_session()
webqtlUtil.dump_session([_matrix, _traits], os.path.join(webqtlConfig.TMPDIR, filename + '.session'))
sessionfile = filename
startTime = time.time()
#Build parameter dictionary used by networkGraphPage class using buildParamDict function
params = networkGraphUtils.buildParamDict(fd, sessionfile)
nodes = len(_traits)
rawEdges = len(_matrix)
if params["tune"] == "yes":
params = networkGraphUtils.tuneParamDict(params, nodes, rawEdges)
matrix = networkGraphUtils.filterDataMatrix(_matrix, params)
optimalNode = networkGraphUtils.optimalRadialNode(matrix)
if not inputFilename:
inputFilename = tempfile.mktemp()
inputFilename = webqtlConfig.IMGDIR + inputFilename.split("/")[2]
#writes out 4 graph files for exporting
graphFile = "/image/" + networkGraphUtils.writeGraphFile(matrix, _traits, inputFilename, params)
networkGraphUtils.processDataMatrix(matrix, params)
edges = 0
for edge in matrix:
if edge.value != 0:
edges +=1
for trait in _traits:
trait.name = networkGraphUtils.fixLabel(trait.name)
RootDir = webqtlConfig.IMGDIR
RootDirURL = "/image/"
#This code writes the datafile that the graphviz function runNeato uses to generate the
#"digraph" file that defines the graphs parameters
datafile = networkGraphUtils.writeNeatoFile(matrix=matrix, traits=_traits, filename=inputFilename, GeneIdArray=GeneIdArray, p=params)
#Generate graph in various file types
layoutfile = networkGraphUtils.runNeato(datafile, "dot", "dot", params["gType"]) # XZ, 09/11/2008: add module name
# ZS 03/04/2010 This second output file (layoutfile_pdf) is rotated by 90 degrees to prevent an issue with pdf output being cut off at the edges
layoutfile_pdf = networkGraphUtils.runNeato(datafile + "_pdf", "dot", "dot", params["gType"]) # ZS 03/04/2010
pngfile = networkGraphUtils.runNeato(layoutfile, "png", "png", params["gType"])
mapfile = networkGraphUtils.runNeato(layoutfile, "cmapx", "cmapx", params["gType"])# XZ, 09/11/2008: add module name
giffile = networkGraphUtils.runNeato(layoutfile, "gif", "gif", params["gType"])# XZ, 09/11/2008:add module name
psfile = networkGraphUtils.runNeato(layoutfile_pdf, "ps", "ps", params["gType"])# XZ, 09/11/2008: add module name
pdffile = networkGraphUtils.runPsToPdf(psfile, params["width"], params["height"])# XZ, 09/11/2008: add module name
#This generates text files in XGGML (standardized graphing language) and plain text
#so the user can create his/her own graphs in a program like Cytoscape
htmlfile1 = datafile + ".html"
htmlfile2 = datafile + ".graph.html"
os.chdir(OrigDir)
#This generates the graph in various image formats
giffile = RootDirURL + giffile
pngfile = RootDirURL + pngfile
pdffile = RootDirURL + pdffile
endTime = time.time()
totalTime = endTime - startTime
os.chdir(RootDir)
page2 = GraphPage(giffile, mapfile)
page2.writeToFile(htmlfile2)
#This generates the HTML for the body of the Network Graph page
page1 = networkGraphPageBody(fd, matrix, _traits, htmlfile2, giffile, pdffile, nodes, edges, rawEdges, totalTime, params, page2.content, graphFile, optimalNode)
#Adds the javascript colorSel to the body to allow line color selection
self.dict["js1"] = '<SCRIPT SRC="/javascript/colorSel.js"></SCRIPT><BR>'
#self.dict["js1"] += '<SCRIPT SRC="/javascript/networkGraph.js"></SCRIPT>'
#Set body of current templatePage to body of the templatePage networkGraphPage
self.dict['body'] = page1.dict['body']
