def session(self,mytitle="",myHeading=""):
'generate a auto-refreshing temporary html file(waiting page)'
self.filename = webqtlUtil.generate_session()
self.dict['title'] = mytitle
self.dict['basehref'] = webqtlConfig.REFRESHSTR % (webqtlConfig.CGIDIR, self.filename) + "" #webqtlConfig.BASEHREF
TD_LR = HT.TD(align="center", valign="middle", height=200,width="100%", bgColor='#eeeeee')
Heading = HT.Paragraph(myHeading, Class="fwb fs16 cr")
# NL, 07/27/2010. variable 'PROGRESSBAR' has been moved from templatePage.py to webqtlUtil.py;
TD_LR.append(Heading, HT.BR(), webqtlUtil.PROGRESSBAR)
self.dict['body'] = TD_LR
self.writeFile(self.filename + '.html')
return self.filename
def buildCanvas(self, colorScheme='', targetDescriptionChecked='', clusterChecked='', sessionfile='', genotype=None, strainlist=None, ppolar=None, mpolar=None, traitList=None, traitDataList=None, userPrivilege=None, userName=None):
labelFont = pid.Font(ttf="tahoma",size=14,bold=0)
topHeight = 0
NNN = len(traitList)
#XZ: It's necessory to define canvas here
canvas = pid.PILCanvas(size=(80+NNN*20,880))
names = map(webqtlTrait.displayName, traitList)
#XZ, 7/29/2009: create trait display and find max strWidth
strWidth = 0
for j in range(len(names)):
thisTrait = traitList[j]
if targetDescriptionChecked:
if thisTrait.db.type == 'ProbeSet':
if thisTrait.probe_target_description:
names[j] += ' [%s at Chr %s @ %2.3fMB, %s]' % (thisTrait.symbol, thisTrait.chr, thisTrait.mb, thisTrait.probe_target_description)
else:
names[j] += ' [%s at Chr %s @ %2.3fMB]' % (thisTrait.symbol, thisTrait.chr, thisTrait.mb)
elif thisTrait.db.type == 'Geno':
names[j] += ' [Chr %s @ %2.3fMB]' % (thisTrait.chr, thisTrait.mb)
elif thisTrait.db.type == 'Publish':
if thisTrait.confidential:
if webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=userPrivilege, userName=userName, authorized_users=thisTrait.authorized_users):
if thisTrait.post_publication_abbreviation:
names[j] += ' [%s]' % (thisTrait.post_publication_abbreviation)
else:
if thisTrait.pre_publication_abbreviation:
names[j] += ' [%s]' % (thisTrait.pre_publication_abbreviation)
else:
if thisTrait.post_publication_abbreviation:
names[j] += ' [%s]' % (thisTrait.post_publication_abbreviation)
else:
pass
i = canvas.stringWidth(names[j], font=labelFont)
if i > strWidth:
strWidth = i
width = NNN*20
xoffset = 40
yoffset = 40
cellHeight = 3
nLoci = reduce(lambda x,y: x+y, map(lambda x: len(x),genotype),0)
if nLoci > 2000:
cellHeight = 1
elif nLoci > 1000:
cellHeight = 2
elif nLoci < 200:
cellHeight = 10
else:
pass
pos = range(NNN)
neworder = []
BWs = Plot.BWSpectrum()
colors100 = Plot.colorSpectrum()
colors = Plot.colorSpectrum(130)
finecolors = Plot.colorSpectrum(250)
colors100.reverse()
colors.reverse()
finecolors.reverse()
scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
if not clusterChecked: #XZ: this part is for original order
for i in range(len(names)):
neworder.append((xoffset+20*(i+1), i))
canvas = pid.PILCanvas(size=(80+NNN*20+240,80+ topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight))
self.drawTraitNameBottom(canvas,names,yoffset,neworder,strWidth,topHeight,labelFont)
else: #XZ: this part is to cluster traits
topHeight = 400
canvas = pid.PILCanvas(size=(80+NNN*20+240,80+ topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight))
corArray = [([0] * (NNN))[:] for i in range(NNN)]
nnCorr = len(strainlist)
#XZ, 08/04/2009: I commented out pearsonArray, spearmanArray
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)
if (1-corr) < 0:
distance = 0.0
else:
distance = 1-corr
corArray[i][j] = distance
corArray[j][i] = distance
elif j == i:
corArray[i][j] = 0.0
else:
pass
#XZ, 7/29/2009: The parameter d has info of cluster (group member and distance). The format of d is tricky. Print it out to see it's format.
d = slink.slink(corArray)
#XZ, 7/29/2009: Attention: The 'neworder' is changed by the 'draw' function
#XZ, 7/30/2009: Only toppos[1][0] and top[1][1] are used later. Then what toppos[0] is used for?
toppos = self.draw(canvas,names,d,xoffset,yoffset,neworder,topHeight)
self.drawTraitNameTop(canvas,names,yoffset,neworder,strWidth,topHeight,labelFont)
#XZ, 7/29/2009: draw the top vertical line
canvas.drawLine(toppos[1][0],toppos[1][1],toppos[1][0],yoffset)
#XZ: draw string 'distance = 1-r'
canvas.drawString('distance = 1-r',neworder[-1][0] + 50, topHeight*3/4,font=labelFont,angle=90)
#draw Scale
scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
x = neworder[-1][0]
canvas.drawLine(x+5, topHeight+yoffset, x+5, yoffset, color=pid.black)
y = 0
while y <=2:
canvas.drawLine(x+5, topHeight*y/2.0+yoffset, x+10, topHeight*y/2.0+yoffset)
canvas.drawString('%2.1f' % (2-y), x+12, topHeight*y/2.0+yoffset, font=scaleFont)
y += 0.5
chrname = 0
chrnameFont=pid.Font(ttf="tahoma",size=24,bold=0)
Ncol = 0
nearestMarkers = self.getNearestMarker(traitList, genotype)
# import cPickle
if sessionfile:
fp = open(os.path.join(webqtlConfig.TMPDIR, sessionfile + '.session'), 'rb')
permData = cPickle.load(fp)
fp.close()
else:
permData = {}
areas = []
#XZ, 7/31/2009: This for loop is to generate the heatmap
#XZ: draw trait by trait instead of marker by marker
for order in neworder:
#startHeight = 40+400+5+5+strWidth
startHeight = topHeight + 40+5+5+strWidth
startWidth = order[0]-5
if Ncol and Ncol % 5 == 0:
drawStartPixel = 8
else:
drawStartPixel = 9
tempVal = traitDataList[order[1]]
_vals = []
_strains = []
for i in range(len(strainlist)):
if tempVal[i] != None:
_strains.append(strainlist[i])
_vals.append(tempVal[i])
qtlresult = genotype.regression(strains = _strains, trait = _vals)
if sessionfile:
LRSArray = permData[str(traitList[order[1]])]
else:
LRSArray = genotype.permutation(strains = _strains, trait = _vals, nperm = 1000)
permData[str(traitList[order[1]])] = LRSArray
sugLRS = LRSArray[369]
sigLRS = LRSArray[949]
prechr = 0
chrstart = 0
nearest = nearestMarkers[order[1]]
midpoint = []
for item in qtlresult:
if item.lrs > webqtlConfig.MAXLRS:
adjustlrs = webqtlConfig.MAXLRS
else:
adjustlrs = item.lrs
if item.locus.chr != prechr:
if prechr:
canvas.drawRect(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight+3,edgeColor=pid.white, edgeWidth=0, fillColor=pid.white)
startHeight+= 3
if not chrname:
canvas.drawString(prechr,xoffset-20,(chrstart+startHeight)/2,font = chrnameFont,color=pid.dimgray)
prechr = item.locus.chr
chrstart = startHeight
if colorScheme == '0':
if adjustlrs <= sugLRS:
colorIndex = int(65*adjustlrs/sugLRS)
else:
colorIndex = int(65 + 35*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
if colorIndex > 99:
colorIndex = 99
colorIndex = colors100[colorIndex]
elif colorScheme == '1':
sugLRS = LRSArray[369]/2.0
if adjustlrs <= sugLRS:
colorIndex = BWs[20+int(50*adjustlrs/sugLRS)]
else:
if item.additive > 0:
colorIndex = int(80 + 50*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
else:
colorIndex = int(50 - 50*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
if colorIndex > 129:
colorIndex = 129
if colorIndex < 0:
colorIndex = 0
colorIndex = colors[colorIndex]
elif colorScheme == '2':
if item.additive > 0:
colorIndex = int(150 + 100*(adjustlrs/sigLRS))
else:
colorIndex = int(100 - 100*(adjustlrs/sigLRS))
if colorIndex > 249:
colorIndex = 249
if colorIndex < 0:
colorIndex = 0
colorIndex = finecolors[colorIndex]
else:
colorIndex = pid.white
if startHeight > 1:
canvas.drawRect(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight+cellHeight,edgeColor=colorIndex, edgeWidth=0, fillColor=colorIndex)
else:
canvas.drawLine(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight, Color=colorIndex)
if item.locus.name == nearest:
midpoint = [startWidth,startHeight-5]
startHeight+=cellHeight
#XZ, map link to trait name and band
COORDS = "%d,%d,%d,%d" %(startWidth-drawStartPixel,topHeight+40,startWidth+10,startHeight)
HREF = "javascript:showDatabase2('%s','%s','%s');" % (traitList[order[1]].db.name, traitList[order[1]].name, traitList[order[1]].cellid)
area = (COORDS, HREF, '%s' % names[order[1]])
areas.append(area)
if midpoint:
traitPixel = ((midpoint[0],midpoint[1]),(midpoint[0]-6,midpoint[1]+12),(midpoint[0]+6,midpoint[1]+12))
canvas.drawPolygon(traitPixel,edgeColor=pid.black,fillColor=pid.orange,closed=1)
if not chrname:
canvas.drawString(prechr,xoffset-20,(chrstart+startHeight)/2,font = chrnameFont,color=pid.dimgray)
chrname = 1
Ncol += 1
#draw Spectrum
startSpect = neworder[-1][0] + 30
startHeight = topHeight + 40+5+5+strWidth
if colorScheme == '0':
for i in range(100):
canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=colors100[i])
scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
canvas.drawString('LRS = 0',startSpect,startHeight+55,font=scaleFont)
canvas.drawLine(startSpect+64,startHeight+45,startSpect+64,startHeight+39,color=pid.black)
canvas.drawString('Suggestive LRS',startSpect+64,startHeight+55,font=scaleFont)
canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
canvas.drawString('Significant LRS',startSpect+105,startHeight+40,font=scaleFont)
elif colorScheme == '1':
for i in range(50):
canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+40,color=BWs[20+i])
for i in range(50,100):
canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+20,color=colors[100-i])
canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=colors[30+i])
canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
canvas.drawString('LRS = 0',startSpect,startHeight+60,font=scaleFont)
canvas.drawLine(startSpect+50,startHeight+45,startSpect+50,startHeight+39,color=pid.black)
canvas.drawString('0.5*Suggestive LRS',startSpect+50,startHeight+ 60,font=scaleFont)
canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
canvas.drawString('Significant LRS',startSpect+105,startHeight+50,font=scaleFont)
textFont=pid.Font(ttf="verdana",size=18,bold=0)
canvas.drawString('%s +' % ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red)
canvas.drawString('%s +' % mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue)
elif colorScheme == '2':
for i in range(100):
canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+20,color=finecolors[100-i])
canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=finecolors[150+i])
canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
canvas.drawString('LRS = 0',startSpect,startHeight+60,font=scaleFont)
canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
canvas.drawString('Significant LRS',startSpect+105,startHeight+50,font=scaleFont)
textFont=pid.Font(ttf="verdana",size=18,bold=0)
canvas.drawString('%s +' % ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red)
canvas.drawString('%s +' % mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue)
filename= webqtlUtil.genRandStr("Heatmap_")
canvas.save(webqtlConfig.IMGDIR+filename, format='png')
if not sessionfile:
sessionfile = webqtlUtil.generate_session()
webqtlUtil.dump_session(permData, os.path.join(webqtlConfig.TMPDIR, sessionfile +'.session'))
self.filename=filename
self.areas=areas
self.sessionfile=sessionfile
def __init__(self, fd):
templatePage.__init__(self, fd)
if not self.openMysql():
return
fd.incparentsf1 = 1
if not fd.genotype:
fd.readGenotype()
locusChr = {}
locusMb = {}
for chr in fd.genotype:
for locus in chr:
locusChr[locus.name] = locus.chr
locusMb[locus.name] = locus.Mb
self.searchResult = fd.formdata.getvalue("searchResult")
if not self.searchResult:
templatePage.__init__(self, fd)
heading = "Export Collection"
detail = ["You need to select at least one trait to export."]
self.error(heading=heading, detail=detail)
return
self.RISet = fd.formdata.getvalue("RISet")
self.cursor.execute(
"Select Species.Name from Species, InbredSet where InbredSet.SpeciesId = Species.Id and InbredSet.Name = '%s'"
% self.RISet
)
self.Species = self.cursor.fetchone()[0]
if type("1") == type(self.searchResult):
self.searchResult = string.split(self.searchResult, "\t")
strainlist = fd.f1list + fd.strainlist
fields = [
"ID",
"Species",
"Cross",
"Database",
"ProbeSetID / RecordID",
"Symbol",
"Description",
"ProbeTarget",
"PubMed_ID",
"Phenotype",
"Chr",
"Mb",
"Alias",
"Gene_ID",
"HomoloGene_ID",
"UniGene_ID",
"Strand_Probe ",
"Strand_Gene ",
"Probe_set_specificity",
"Probe_set_BLAT_score",
"Probe_set_BLAT_Mb_start",
"Probe_set_BLAT_Mb_end ",
"QTL_Chr",
"QTL_Mb",
"Locus_at_Peak",
"Max_LRS",
"P_value_of_MAX",
"Mean_Expression",
] + strainlist
if self.searchResult:
traitList = []
for item in self.searchResult:
thisTrait = webqtlTrait(fullname=item, cursor=self.cursor)
thisTrait.retrieveInfo(QTL=1)
thisTrait.retrieveData(strainlist=strainlist)
traitList.append(thisTrait)
text = [fields]
for i, thisTrait in enumerate(traitList):
if thisTrait.db.type == "ProbeSet":
if not thisTrait.cellid: # ProbeSet
# 12/22/2009, XZ: We calculated LRS for each marker(locus) in geno file and record the max LRS and its corresponding marker in MySQL database. But after the calculation, Rob deleted several markers. If one of the deleted markers happen to be the one recorded in database, error will occur. So we have to deal with this situation.
if locusChr.has_key(thisTrait.locus) and locusMb.has_key(thisTrait.locus):
text.append(
[
str(i + 1),
self.Species,
self.RISet,
thisTrait.db.fullname,
thisTrait.name,
thisTrait.symbol,
thisTrait.description,
thisTrait.probe_target_description,
"",
"",
thisTrait.chr,
thisTrait.mb,
thisTrait.alias,
thisTrait.geneid,
thisTrait.homologeneid,
thisTrait.unigeneid,
thisTrait.strand_probe,
thisTrait.strand_gene,
thisTrait.probe_set_specificity,
thisTrait.probe_set_blat_score,
thisTrait.probe_set_blat_mb_start,
thisTrait.probe_set_blat_mb_end,
locusChr[thisTrait.locus],
locusMb[thisTrait.locus],
thisTrait.locus,
thisTrait.lrs,
thisTrait.pvalue,
]
)
else:
text.append(
[
str(i + 1),
self.Species,
self.RISet,
thisTrait.db.fullname,
thisTrait.name,
thisTrait.symbol,
thisTrait.description,
thisTrait.probe_target_description,
"",
"",
thisTrait.chr,
thisTrait.mb,
thisTrait.alias,
thisTrait.geneid,
thisTrait.homologeneid,
thisTrait.unigeneid,
thisTrait.strand_probe,
thisTrait.strand_gene,
thisTrait.probe_set_specificity,
thisTrait.probe_set_blat_score,
thisTrait.probe_set_blat_mb_start,
thisTrait.probe_set_blat_mb_end,
"",
"",
"",
"",
"",
]
)
else: # Probe
text.append(
[
str(i + 1),
self.Species,
self.RISet,
thisTrait.db.fullname,
thisTrait.name + " : " + thisTrait.cellid,
thisTrait.symbol,
thisTrait.description,
thisTrait.probe_target_description,
"",
"",
thisTrait.chr,
thisTrait.mb,
thisTrait.alias,
thisTrait.geneid,
thisTrait.homologeneid,
thisTrait.unigeneid,
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
]
)
elif thisTrait.db.type == "Publish":
# XZ: need to consider confidential phenotype
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
text.append(
[
str(i + 1),
self.Species,
self.RISet,
thisTrait.db.fullname,
thisTrait.name,
"",
"",
"",
thisTrait.pubmed_id,
PhenotypeString,
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
]
)
elif thisTrait.db.type == "Temp":
text.append(
[
str(i + 1),
self.Species,
self.RISet,
thisTrait.db.fullname,
thisTrait.name,
"",
thisTrait.description,
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
]
)
elif thisTrait.db.type == "Geno":
text.append(
[
str(i + 1),
self.Species,
self.RISet,
thisTrait.db.fullname,
thisTrait.name,
"",
thisTrait.name,
"",
"",
"",
thisTrait.chr,
thisTrait.mb,
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
]
)
else:
continue
testval = thisTrait.exportData(strainlist)
try:
mean = reaper.anova(testval)[0]
except:
count = 0
sum = 0
for oneValue in testval:
try:
oneValue = float(oneValue)
sum = sum + oneValue
count = count + 1
except:
pass
if count == 0:
mean = 0
else:
mean = sum / count
text[-1].append(mean)
text[-1] += testval
if len(text[0]) < 255 or len(text) < 255:
transpose = 0
if len(text[0]) >= 255:
text = webqtlUtil.transpose(text)
transpose = 1
filename = os.path.join(webqtlConfig.TMPDIR, webqtlUtil.generate_session() + ".xls")
# Create a new Excel workbook
workbook = xl.Writer(filename)
worksheet = workbook.add_worksheet()
headingStyle = workbook.add_format(align="center", bold=1, size=13, color="green")
titleStyle = workbook.add_format(align="left", bold=0, size=13, border=1, border_color="gray")
##Write title Info
# Modified by Hongqiang Li
# worksheet.write([0, 0], "Data source: The GeneNetwork at web2qtl.utmem.edu:88", titleStyle)
# worksheet.write([1, 0], "Citations: Please see web2qtl.utmem.edu:88/reference.html", titleStyle)
worksheet.write([0, 0], "Data source: The GeneNetwork at %s" % webqtlConfig.PORTADDR, titleStyle)
worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle)
#
worksheet.write([2, 0], "Date : %s" % time.strftime("%B %d, %Y", time.gmtime()), titleStyle)
worksheet.write([3, 0], "Time : %s GMT" % time.strftime("%H:%M ", time.gmtime()), titleStyle)
# Modified by Hongqiang Li
# worksheet.write([4, 0], "Status of data ownership: Possibly unpublished data; please see web2qtl.utmem.edu:88/statusandContact.html for details on sources, ownership, and usage of these data.", titleStyle)
worksheet.write(
[4, 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,
)
#
worksheet.write(
[6, 0],
"This output file contains data from %d GeneNetwork databases listed below" % len(traitList),
titleStyle,
)
# Row and column are zero indexed
nrow = startRow = 8
for row in text:
for ncol, cell in enumerate(row):
if nrow == startRow:
worksheet.write([nrow, ncol], cell.strip(), headingStyle)
worksheet.set_column([ncol, ncol], 2 * len(cell))
else:
worksheet.write([nrow, ncol], cell)
nrow += 1
worksheet.write(
[nrow + 1, 0],
"Funding for The GeneNetwork: NIAAA (U01AA13499, U24AA13513), NIDA, NIMH, and NIAAA (P20-DA 21131), NCI MMHCC (U01CA105417), and NCRR (U24 RR021760)",
titleStyle,
)
worksheet.write([nrow + 2, 0], "PLEASE RETAIN DATA SOURCE INFORMATION WHENEVER POSSIBLE", titleStyle)
workbook.close()
fp = open(filename, "rb")
text = fp.read()
fp.close()
self.content_type = "application/xls"
self.content_disposition = "attachment; filename=%s" % (
"export-%s.xls" % time.strftime("%y-%m-%d-%H-%M")
)
self.attachment = text
else:
self.content_type = "application/xls"
self.content_disposition = "attachment; filename=%s" % (
"export-%s.txt" % time.strftime("%y-%m-%d-%H-%M")
)
for item in text:
self.attachment += string.join(map(str, item), "\t") + "\n"
self.cursor.close()
else:
fd.req.content_type = "text/html"
heading = "Export Collection"
detail = [
HT.Font("Error : ", color="red"),
HT.Font("Error occurs while retrieving data from database.", color="black"),
]
self.error(heading=heading, detail=detail)
def __init__(self,fd):
templatePage.__init__(self, fd)
if not self.openMysql():
return
if not fd.genotype:
fd.readGenotype()
self.searchResult = fd.formdata.getvalue('searchResult')
if not self.searchResult:
templatePage.__init__(self, fd)
heading = 'QTL Heatmap'
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 = string.split(self.searchResult,'\t')
if self.searchResult:
if len(self.searchResult) > webqtlConfig.MAXCORR:
heading = 'QTL Heatmap'
detail = ['In order to display the QTL heat map properly, do not select more than %d traits for analysis.' % webqtlConfig.MAXCORR]
self.error(heading=heading,detail=detail)
return
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 = 'QTL Heatmap'
detail = [HT.Font('Error : ',color='red'),HT.Font('Error occurs while retrieving data from database.',color='black')]
self.error(heading=heading,detail=detail)
return
self.colorScheme = fd.formdata.getvalue('colorScheme')
if not self.colorScheme:
self.colorScheme = '1'
self.dict['title'] = 'QTL heatmap'
NNN = len(traitList)
if NNN == 0:
heading = "QTL Heatmap"
detail = ['No trait was selected for %s data set. No QTL heatmap was generated.' % fd.RISet]
self.error(heading=heading,detail=detail)
return
elif NNN < 2:
templatePage.__init__(self, fd)
heading = 'QTL Heatmap'
detail = ['You need to select at least two traits in order to generate QTL heatmap.']
self.error(heading=heading,detail=detail)
return
else:
#XZ: It's necessory to define canvas here
canvas = pid.PILCanvas(size=(80+NNN*20,880))
names = map(webqtlTrait.displayName, traitList)
self.targetDescriptionChecked = fd.formdata.getvalue('targetDescriptionCheck', '')
#XZ, 7/29/2009: create trait display and find max strWidth
strWidth = 0
for j in range(len(names)):
thisTrait = traitList[j]
if self.targetDescriptionChecked:
if thisTrait.db.type == 'ProbeSet':
if thisTrait.probe_target_description:
names[j] += ' [%s at Chr %s @ %2.3fMB, %s]' % (thisTrait.symbol, thisTrait.chr, thisTrait.mb, thisTrait.probe_target_description)
else:
names[j] += ' [%s at Chr %s @ %2.3fMB]' % (thisTrait.symbol, thisTrait.chr, thisTrait.mb)
elif thisTrait.db.type == 'Geno':
names[j] += ' [Chr %s @ %2.3fMB]' % (thisTrait.chr, thisTrait.mb)
elif thisTrait.db.type == 'Publish':
if thisTrait.abbreviation:
names[j] += ' [%s]' % (thisTrait.abbreviation)
else:
pass
else:
pass
i = canvas.stringWidth(names[j],font=self.labelFont)
if i > strWidth:
strWidth = i
width = NNN*20
xoffset = 40
yoffset = 40
cellHeight = 3
nLoci = reduce(lambda x,y: x+y, map(lambda x: len(x),fd.genotype),0)
if nLoci > 2000:
cellHeight = 1
elif nLoci > 1000:
cellHeight = 2
elif nLoci < 200:
cellHeight = 10
else:
pass
pos = range(NNN)
neworder = []
BWs = Plot.BWSpectrum()
colors100 = Plot.colorSpectrum()
colors = Plot.colorSpectrum(130)
finecolors = Plot.colorSpectrum(250)
colors100.reverse()
colors.reverse()
finecolors.reverse()
scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
self.clusterChecked = fd.formdata.getvalue('clusterCheck', '')
if not self.clusterChecked: #XZ: this part is for original order
for i in range(len(names)):
neworder.append((xoffset+20*(i+1), i))
canvas = pid.PILCanvas(size=(80+NNN*20+240,80+ self.topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight))
self.drawTraitNameBottom(canvas,names,yoffset,neworder,strWidth)
else: #XZ: this part is to cluster traits
self.topHeight = 400
canvas = pid.PILCanvas(size=(80+NNN*20+240,80+ self.topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight))
corArray = [([0] * (NNN))[:] for i in range(NNN)]
nnCorr = len(fd.strainlist)
#XZ, 08/04/2009: I commented out pearsonArray, spearmanArray
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)
if (1-corr) < 0:
distance = 0.0
else:
distance = 1-corr
corArray[i][j] = distance
corArray[j][i] = distance
elif j == i:
corArray[i][j] = 0.0
else:
pass
#XZ, 7/29/2009: The parameter d has info of cluster (group member and distance). The format of d is tricky. Print it out to see it's format.
d = slink.slink(corArray)
#XZ, 7/29/2009: Attention: The 'neworder' is changed by the 'draw' function
#XZ, 7/30/2009: Only toppos[1][0] and top[1][1] are used later. Then what toppos[0] is used for?
toppos = self.draw(canvas,names,d,xoffset,yoffset,neworder)
self.drawTraitNameTop(canvas,names,yoffset,neworder,strWidth)
#XZ, 7/29/2009: draw the top vertical line
canvas.drawLine(toppos[1][0],toppos[1][1],toppos[1][0],yoffset)
#XZ: draw string 'distance = 1-r'
canvas.drawString('distance = 1-r',neworder[-1][0] + 50, self.topHeight*3/4,font=self.labelFont,angle=90)
#draw Scale
scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
x = neworder[-1][0]
canvas.drawLine(x+5, self.topHeight+yoffset, x+5, yoffset, color=pid.black)
y = 0
while y <=2:
canvas.drawLine(x+5, self.topHeight*y/2.0+yoffset, x+10, self.topHeight*y/2.0+yoffset)
canvas.drawString('%2.1f' % (2-y), x+12, self.topHeight*y/2.0+yoffset, font=scaleFont)
y += 0.5
chrname = 0
chrnameFont=pid.Font(ttf="tahoma",size=24,bold=0)
Ncol = 0
gifmap = HT.Map(name='traitMap')
nearestMarkers = self.getNearestMarker(traitList, fd.genotype)
# import cPickle
sessionfile = fd.formdata.getvalue("session")
if sessionfile:
fp = open(os.path.join(webqtlConfig.TMPDIR, sessionfile + '.session'), 'rb')
permData = cPickle.load(fp)
fp.close()
else:
permData = {}
#XZ, 7/31/2009: This for loop is to generate the heatmap
#XZ: draw trait by trait instead of marker by marker
for order in neworder:
#startHeight = 40+400+5+5+strWidth
startHeight = self.topHeight + 40+5+5+strWidth
startWidth = order[0]-5
if Ncol and Ncol % 5 == 0:
drawStartPixel = 8
else:
drawStartPixel = 9
tempVal = traitDataList[order[1]]
_vals = []
_strains = []
for i in range(len(fd.strainlist)):
if tempVal[i] != None:
_strains.append(fd.strainlist[i])
_vals.append(tempVal[i])
qtlresult = fd.genotype.regression(strains = _strains, trait = _vals)
if sessionfile:
LRSArray = permData[str(traitList[order[1]])]
else:
LRSArray = fd.genotype.permutation(strains = _strains, trait = _vals, nperm = 1000)
permData[str(traitList[order[1]])] = LRSArray
sugLRS = LRSArray[369]
sigLRS = LRSArray[949]
prechr = 0
chrstart = 0
nearest = nearestMarkers[order[1]]
midpoint = []
for item in qtlresult:
if item.lrs > webqtlConfig.MAXLRS:
adjustlrs = webqtlConfig.MAXLRS
else:
adjustlrs = item.lrs
if item.locus.chr != prechr:
if prechr:
canvas.drawRect(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight+3,edgeColor=pid.white, edgeWidth=0, fillColor=pid.white)
startHeight+= 3
if not chrname:
canvas.drawString(prechr,xoffset-20,(chrstart+startHeight)/2,font = chrnameFont,color=pid.dimgray)
prechr = item.locus.chr
chrstart = startHeight
if self.colorScheme == '0':
if adjustlrs <= sugLRS:
colorIndex = int(65*adjustlrs/sugLRS)
else:
colorIndex = int(65 + 35*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
if colorIndex > 99:
colorIndex = 99
colorIndex = colors100[colorIndex]
elif self.colorScheme == '1':
sugLRS = LRSArray[369]/2.0
if adjustlrs <= sugLRS:
colorIndex = BWs[20+int(50*adjustlrs/sugLRS)]
else:
if item.additive > 0:
colorIndex = int(80 + 50*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
else:
colorIndex = int(50 - 50*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
if colorIndex > 129:
colorIndex = 129
if colorIndex < 0:
colorIndex = 0
colorIndex = colors[colorIndex]
elif self.colorScheme == '2':
if item.additive > 0:
colorIndex = int(150 + 100*(adjustlrs/sigLRS))
else:
colorIndex = int(100 - 100*(adjustlrs/sigLRS))
if colorIndex > 249:
colorIndex = 249
if colorIndex < 0:
colorIndex = 0
colorIndex = finecolors[colorIndex]
else:
colorIndex = pid.white
if startHeight > 1:
canvas.drawRect(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight+cellHeight,edgeColor=colorIndex, edgeWidth=0, fillColor=colorIndex)
else:
canvas.drawLine(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight, Color=colorIndex)
if item.locus.name == nearest:
midpoint = [startWidth,startHeight-5]
startHeight+=cellHeight
#XZ, map link to trait name and band
COORDS = "%d,%d,%d,%d" %(startWidth-drawStartPixel,self.topHeight+40,startWidth+10,startHeight)
HREF = "javascript:showDatabase2('%s','%s','%s');" % (traitList[order[1]].db.name, traitList[order[1]].name, traitList[order[1]].cellid)
Areas = HT.Area(shape='rect',coords=COORDS,href=HREF, title='%s' % names[order[1]])
gifmap.areas.append(Areas)
if midpoint:
traitPixel = ((midpoint[0],midpoint[1]),(midpoint[0]-6,midpoint[1]+12),(midpoint[0]+6,midpoint[1]+12))
canvas.drawPolygon(traitPixel,edgeColor=pid.black,fillColor=pid.orange,closed=1)
if not chrname:
canvas.drawString(prechr,xoffset-20,(chrstart+startHeight)/2,font = chrnameFont,color=pid.dimgray)
chrname = 1
Ncol += 1
#draw Spectrum
startSpect = neworder[-1][0] + 30
startHeight = self.topHeight + 40+5+5+strWidth
if self.colorScheme == '0':
for i in range(100):
canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=colors100[i])
scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
canvas.drawString('LRS = 0',startSpect,startHeight+55,font=scaleFont)
canvas.drawLine(startSpect+64,startHeight+45,startSpect+64,startHeight+39,color=pid.black)
canvas.drawString('Suggestive LRS',startSpect+64,startHeight+55,font=scaleFont)
canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
canvas.drawString('Significant LRS',startSpect+105,startHeight+40,font=scaleFont)
elif self.colorScheme == '1':
for i in range(50):
canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+40,color=BWs[20+i])
for i in range(50,100):
canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+20,color=colors[100-i])
canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=colors[30+i])
canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
canvas.drawString('LRS = 0',startSpect,startHeight+60,font=scaleFont)
canvas.drawLine(startSpect+50,startHeight+45,startSpect+50,startHeight+39,color=pid.black)
canvas.drawString('0.5*Suggestive LRS',startSpect+50,startHeight+ 60,font=scaleFont)
canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
canvas.drawString('Significant LRS',startSpect+105,startHeight+50,font=scaleFont)
textFont=pid.Font(ttf="verdana",size=18,bold=0)
canvas.drawString('%s +' % fd.ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red)
canvas.drawString('%s +' % fd.mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue)
elif self.colorScheme == '2':
for i in range(100):
canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+20,color=finecolors[100-i])
canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=finecolors[150+i])
canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
canvas.drawString('LRS = 0',startSpect,startHeight+60,font=scaleFont)
canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
canvas.drawString('Significant LRS',startSpect+105,startHeight+50,font=scaleFont)
textFont=pid.Font(ttf="verdana",size=18,bold=0)
canvas.drawString('%s +' % fd.ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red)
canvas.drawString('%s +' % fd.mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue)
filename= webqtlUtil.genRandStr("Heatmap_")
canvas.save(webqtlConfig.IMGDIR+filename, format='png')
img2=HT.Image('/image/'+filename+'.png',border=0,usemap='#traitMap')
imgUrl = 'Right-click or control-click on the link to download this graph as a <a href="/image/%s.png" class="normalsize" target="_blank">PNG file</a>' % filename
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':fd.RISet+"Geno",'CellID':'_','RISet':fd.RISet,'searchResult':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'))
heatmap = HT.Input(type='button' ,name='mintmap',value='Redraw QTL Heatmap', onClick="databaseFunc(this.form,'heatmap');",Class="button")
spects = {'0':'Single Spectrum','1':'Grey + Blue + Red','2':'Blue + Red'}
schemeMenu = HT.Select(name='colorScheme')
schemeMenu.append(('Single Spectrum',0))
schemeMenu.append(('Grey + Blue + Red',1))
schemeMenu.append(('Blue + Red',2))
schemeMenu.selected.append(spects[self.colorScheme])
clusterCheck= HT.Input(type='checkbox', Class='checkbox', name='clusterCheck',checked=0)
targetDescriptionCheck = HT.Input(type='checkbox', Class='checkbox', name='targetDescriptionCheck',checked=0)
form.append(gifmap,schemeMenu, heatmap, HT.P(), clusterCheck, ' Cluster traits ', targetDescriptionCheck, ' Add description', HT.P(),img2, HT.P(), imgUrl)
if not sessionfile:
filename = webqtlUtil.generate_session()
webqtlUtil.dump_session(permData, os.path.join(webqtlConfig.TMPDIR, filename +'.session'))
sessionfile=filename
form.append(HT.Input(name='session', value=sessionfile, type='hidden'))
heatmapHelp = HT.Input(type='button' ,name='heatmapHelpButton',value='Info', onClick="openNewWin('/heatmap.html');",Class="button")
heatmapHeading = HT.Paragraph('QTL Heatmap ', heatmapHelp, Class="title")
TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee')
TD_LR.append(heatmapHeading, HT.P(),HT.P(),HT.P(),HT.P(),HT.P(),form)
self.dict['body'] = str(TD_LR)
def exportDatasetPage(self, fd, PublishFreeze_Name):
#return PublishFreeze_Name
if not self.openMysql():
return
self.cursor.execute( "select InbredSet.Name from PublishFreeze, InbredSet where PublishFreeze.InbredSetId=InbredSet.Id and PublishFreeze.Name='%s'" % PublishFreeze_Name )
self.RISet = self.cursor.fetchone()[0]
fd.RISet = self.RISet
fd.incparentsf1 = 1
fd.readGenotype()
strainlist = fd.f1list + fd.strainlist
#return str(strainlist)
self.cursor.execute("Select Species.Name from Species, InbredSet where InbredSet.SpeciesId = Species.Id and InbredSet.Name = '%s'" % fd.RISet)
self.Species = self.cursor.fetchone()[0]
#return Species
self.searchResult = []
self.cursor.execute("Select PublishXRef.Id from PublishXRef, InbredSet where PublishXRef.InbredSetId = InbredSet.Id and InbredSet.Name = '%s'" % self.RISet)
result = self.cursor.fetchall()
for one_result in result:
self.searchResult.append( "%s::%s" % (PublishFreeze_Name, one_result[0]) )
#return self.searchResult
fields = ["ID", "Species", "Cross", "Database", "ProbeSetID / RecordID", "Symbol", "Description", "ProbeTarget", "PubMed_ID", "Phenotype", "Chr", "Mb", "Alias", "Gene_ID", "UniGene_ID", "Strand_Probe ", "Strand_Gene ",
"Probe_set_specificity", "Probe_set_BLAT_score", "Probe_set_BLAT_Mb_start", "Probe_set_BLAT_Mb_end ", "QTL_Chr", "Locus_at_Peak", "Max_LRS", "P_value_of_MAX", "Mean_Expression"] + strainlist
if self.searchResult:
traitList = []
for item in self.searchResult:
thisTrait = webqtlTrait(fullname=item, cursor=self.cursor)
thisTrait.retrieveInfo(QTL=1)
thisTrait.retrieveData(strainlist=strainlist)
traitList.append(thisTrait)
text = [fields]
for i, thisTrait in enumerate(traitList):
if thisTrait.db.type == 'ProbeSet':
if not thisTrait.cellid: #ProbeSet
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name, thisTrait.symbol, thisTrait.description, thisTrait.probe_target_description,"", "", thisTrait.chr, thisTrait.mb, thisTrait.alias, thisTrait.geneid, thisTrait.unigeneid, thisTrait.strand_probe, thisTrait.strand_gene, thisTrait.probe_set_specificity, thisTrait.probe_set_blat_score, thisTrait.probe_set_blat_mb_start, thisTrait.probe_set_blat_mb_end, locusChr[thisTrait.locus], thisTrait.locus, thisTrait.lrs, thisTrait.pvalue])
else: #Probe
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name + " : " + thisTrait.cellid, thisTrait.symbol, thisTrait.description, thisTrait.probe_target_description,"", "", thisTrait.chr, thisTrait.mb, thisTrait.alias, thisTrait.geneid, thisTrait.unigeneid, "", "", "", "", "", "", "", "", "", ""])
elif thisTrait.db.type == 'Publish':
if thisTrait.pre_publication_description:
if thisTrait.pubmed_id:
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name, "", "", "", thisTrait.pubmed_id, thisTrait.post_publication_description, "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""])
else:
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name, "", "", "", "", thisTrait.pre_publication_description, "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""])
else:
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name, "", "", "", thisTrait.pubmed_id, thisTrait.post_publication_description, "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""])
elif thisTrait.db.type == 'Temp':
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name, "", thisTrait.description, "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""])
elif thisTrait.db.type == 'Geno':
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name, "", thisTrait.name,"", "", "", thisTrait.chr, thisTrait.mb, "", "", "", "", "", "", "", "", "", "", "", "", ""])
else:
continue
testval = thisTrait.exportData(strainlist)
try:
mean = reaper.anova(testval)[0]
except:
mean = 'N/A'
text[-1].append(mean)
text[-1] += testval
if len(text[0]) < 255 or len(text) < 255:
transpose = 0
if len(text[0]) >= 255:
text = webqtlUtil.transpose(text)
transpose = 1
filename = os.path.join(webqtlConfig.TMPDIR, webqtlUtil.generate_session() +'.xls')
# Create a new Excel workbook
workbook = xl.Writer(filename)
worksheet = workbook.add_worksheet()
headingStyle = workbook.add_format(align = 'center', bold = 1, size=13, color = 'green')
titleStyle = workbook.add_format(align = 'left', bold = 0, size=13, border = 1, border_color="gray")
##Write title Info
worksheet.write([0, 0], "Data source: The GeneNetwork at %s" % webqtlConfig.PORTADDR, titleStyle)
worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle)
worksheet.write([2, 0], "Date : %s" % time.strftime("%B %d, %Y", time.gmtime()), titleStyle)
worksheet.write([3, 0], "Time : %s GMT" % time.strftime("%H:%M ", time.gmtime()), titleStyle)
worksheet.write([4, 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)
worksheet.write([6, 0], "This output file contains data from %d GeneNetwork databases listed below" % len(traitList), titleStyle)
# Row and column are zero indexed
nrow = startRow = 8
for row in text:
for ncol, cell in enumerate(row):
if nrow == startRow:
worksheet.write([nrow, ncol], cell.strip(), headingStyle)
worksheet.set_column([ncol, ncol], 2*len(cell))
else:
worksheet.write([nrow, ncol], cell)
nrow += 1
worksheet.write([nrow+1, 0], "Funding for The GeneNetwork: NIAAA (U01AA13499, U24AA13513), NIDA, NIMH, and NIAAA (P20-DA 21131), NCI MMHCC (U01CA105417), and NCRR (U24 RR021760)", titleStyle)
worksheet.write([nrow+2, 0], "PLEASE RETAIN DATA SOURCE INFORMATION WHENEVER POSSIBLE", titleStyle)
workbook.close()
fp = open(filename, 'rb')
text = fp.read()
fp.close()
self.content_type = 'application/xls'
self.content_disposition = 'attachment; filename=%s' % ('export-%s.xls' % time.strftime("%y-%m-%d-%H-%M"))
self.attachment = text
else:
self.content_type = 'application/xls'
self.content_disposition = 'attachment; filename=%s' % ('export-%s.txt' % time.strftime("%y-%m-%d-%H-%M"))
for item in text:
self.attachment += string.join(map(str, item), '\t')+ "\n"
self.cursor.close()
else:
fd.req.content_type = 'text/html'
heading = 'Export Collection'
detail = [HT.Font('Error : ',color='red'),HT.Font('Error occurs while retrieving data from database.',color='black')]
self.error(heading=heading,detail=detail)
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']
