def basicStatsTable(vals, trait_type=None, cellid=None, heritability=None):
valsOnly = []
dataXZ = vals[:]
for i in range(len(dataXZ)):
valsOnly.append(dataXZ[i][1])
traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(valsOnly) #ZS: Should convert this from reaper to R in the future
tbl = HT.TableLite(cellpadding=20, cellspacing=0)
dataXZ = vals[:]
dataXZ.sort(webqtlUtil.cmpOrder)
tbl.append(HT.TR(HT.TD("Statistic",align="left", Class="fs14 fwb ffl b1 cw cbrb", width = 180),
HT.TD("Value", align="right", Class="fs14 fwb ffl b1 cw cbrb", width = 60)))
tbl.append(HT.TR(HT.TD("N of Samples",align="left", Class="fs13 b1 cbw c222"),
HT.TD(N,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Mean",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmean,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Median",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmedian,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD("Variance",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitvar,nowrap="yes",align="left", Class="fs13 b1 cbw c222")))
tbl.append(HT.TR(HT.TD("Standard Error (SE)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitsem,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Standard Deviation (SD)", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitstdev,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Minimum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[0][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Maximum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[-1][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
if (trait_type != None and trait_type == 'ProbeSet'):
#IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14")
#IRQuest.append(HT.BR())
#IRQuest.append(" (fold difference)")
tbl.append(HT.TR(HT.TD("Range (log2)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD(HT.Span(HT.Href(url="/glossary.html#Interquartile", target="_blank", text="Interquartile Range", Class="non_bold")), align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.2f" % pow(2.0,(dataXZ[int((N-1)*3.0/4.0)][1]-dataXZ[int((N-1)/4.0)][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#XZ, 04/01/2009: don't try to get H2 value for probe.
if cellid:
pass
else:
if heritability:
tbl.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("%s" % heritability, nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
else:
pass
# Lei Yan
# 2008/12/19
return tbl
def basicStatsTable(vals, trait_type=None, cellid=None, heritability=None, trait=None):
valsOnly = []
dataXZ = vals[:]
for i in range(len(dataXZ)):
valsOnly.append(dataXZ[i][1])
traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(valsOnly) #ZS: Should convert this from reaper to R in the future
tbl = HT.TableLite(cellpadding=20, cellspacing=0)
dataXZ = vals[:]
dataXZ.sort(webqtlUtil.cmpOrder)
tbl.append(HT.TR(HT.TD("Statistic",align="left", Class="fs14 fwb ffl b1 cw cbrb", width = 180),
HT.TD("Value", align="right", Class="fs14 fwb ffl b1 cw cbrb", width = 60)))
tbl.append(HT.TR(HT.TD("N of Samples",align="left", Class="fs13 b1 cbw c222"),
HT.TD(N,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Mean",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmean,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Median",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmedian,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD("Variance",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitvar,nowrap="yes",align="left", Class="fs13 b1 cbw c222")))
tbl.append(HT.TR(HT.TD("Standard Error (SE)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitsem,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Standard Deviation (SD)", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitstdev,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Minimum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[0][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
tbl.append(HT.TR(HT.TD("Maximum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[-1][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
if (trait_type != None and trait_type == 'ProbeSet'):
if trait != None and trait.db.datascale == "linear":
try:
tbl.append(HT.TR(
HT.TD("Range", align="left", Class="fs13 b1 cbw c222", nowrap="yes"),
HT.TD("%2.3f" % (dataXZ[-1][1] - dataXZ[0][1]), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
except Exception, e:
pass
try:
tbl.append(HT.TR(
HT.TD("Range (fold)", align="left", Class="fs13 b1 cbw c222", nowrap="yes"),
HT.TD("%2.3f" % pow(2.0, (dataXZ[-1][1] - dataXZ[0][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
except Exception, e:
pass
try:
tbl.append(HT.TR(
HT.TD(HT.Href(url="/glossary.html#Interquartile", target="_blank", text="Interquartile Range", Class="non_bold"), align="left", Class="fs13 b1 cbw c222", nowrap="yes"),
HT.TD("%2.3f" % pow(2.0, (dataXZ[int((N-1)*3.0/4.0)][1] - dataXZ[int((N-1)/4.0)][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
except Exception, e:
pass
def __init__(self, fd):
templatePage.__init__(self, fd)
if not self.openMysql():
return
fd.readGenotype()
TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee')
self.database = fd.formdata.getfirst('database')
self.ProbeSetID = fd.formdata.getfirst('ProbeSetID')
self.CellID = fd.formdata.getfirst('CellID')
self.db = webqtlDataset(self.database, self.cursor)
thisTrait = webqtlTrait(db= self.db, cursor=self.cursor, name=self.ProbeSetID) #, cellid=CellID)
thisTrait.retrieveInfo()
try:
self.cursor.execute('SELECT ProbeFreeze.Name FROM ProbeFreeze,ProbeSetFreeze WHERE ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId and ProbeSetFreeze.Name = "%s"' % self.db.name)
self.probeDatabase = self.cursor.fetchall()[0][0]
self.probeInfoDatabase = 'Probe'
except:
heading = 'Probe Information'
intro = ['Trying to retrieve the probe information for ProbeSet ',HT.Span('%s' % self.ProbeSetID, Class="fwb cdg"),' in Database ',HT.Href(text='%s' % self.db.fullname,url=webqtlConfig.infopagehref % self.database)]
detail = ['The information you just requested is not available at this time.']
self.error(heading=heading,intro=intro,detail=detail)
return
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':'_','RISet':fd.RISet, 'incparentsf1':'on'}
if fd.RISet == 'BXD':
hddn['parentsf1']='ON'
for key in hddn.keys():
form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
#Buttons on search page
linkinfo ="%s/probeInfo.html" % webqtlConfig.PORTADDR
mintmap = ""
probeinfo = HT.Input(type='button' ,name='mintmap',value='Info', onClick="openNewWin('%s');" % linkinfo, Class="button")
cormatrix = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('showDatabase')[0], 'corMatrix');")
cormatrix_img = HT.Image("/images/correlation_matrix1_final.jpg", alt="Correlation Matrix and PCA", title="Correlation Matrix and PCA", style="border:none;")
cormatrix.append(cormatrix_img)
heatmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('showDatabase')[0], 'heatmap');")
heatmap_img = HT.Image("/images/heatmap2_final.jpg", name='mintmap', alt="QTL Heat Map and Clustering", title="QTL Heatmap and Clustering", style="border:none;")
heatmap.append(heatmap_img)
if self.ProbeSetID[-2:] in ('_A', '_B'):
thisProbeSetID = self.ProbeSetID[:-2]
else:
thisProbeSetID = self.ProbeSetID
thisurl = 'http://www.ensembl.org/Mus_musculus/featureview?type=AffyProbe&id=%s' % thisProbeSetID
verifyButton = HT.Input(type="button",value="Verify Ensembl",onClick= "openNewWin('%s')" % thisurl, 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")
selectpm = HT.Input(type='button' ,name='selectall',value='Select PM', onClick="checkPM(this.form);",Class="button")
selectmm = HT.Input(type='button' ,name='selectall',value='Select MM', onClick="checkMM(this.form);",Class="button")
selectinvert = HT.Input(type='button' ,name='selectinvert',value='Select Invert', onClick="checkInvert(this.form);",Class="button")
reset = HT.Input(type='reset',name='',value='Select None',Class="button")
chrMenu = HT.Input(type='hidden',name='chromosomes',value='all')
probedata = HT.Input(type='hidden',name='probedata',value='all')
url_rudi_track = self.getProbeTrackURL(self.probeDatabase, self.ProbeSetID)
if url_rudi_track:
rudi_track = HT.Input(type='button', name='ruditrack', value='Probe Track', onClick="openNewWin('%s')"%url_rudi_track, Class="button")
else: rudi_track = None
pinfopage = "/probeInfo.html"
#updated by NL: 07-22-2011 get chosenStrains
_f1, _f12, _mat, _pat = webqtlUtil.ParInfo[fd.RISet]
chosenStrains="%s,%s"%(_mat,_pat)
tblobj = {}
tblobj['header']=[]
tblobj['header'].append([
THCell(HT.TD("", Class="cbrb cw fwb fs13 b1", rowspan=2,nowrap='ON'), sort=0),
THCell(HT.TD(HT.Href(target="_PROBEINFO", url=pinfopage+"#probe", text=HT.Span('Probe', Class="cw fwb fs13")), HT.Sup(HT.Italic('1')), Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,nowrap='ON'), text="probe", idx=1),
THCell(HT.TD(HT.Href(text=HT.Span('Sequence', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#Sequence"),HT.Sup(HT.Italic('2')), Class="cbrb cw fwb fs13 b1", align='center',rowspan=2,nowrap='ON'), text="seq", idx=2),
THCell(HT.TD(HT.Href(text=HT.Span('bl2seq', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#bl2seq"),HT.Sup(HT.Italic('3')), Class="cbrb cw fwb fs13 b1", align='center',rowspan=2,nowrap='ON'), sort=0),
THCell(HT.TD(HT.Href(text=HT.Span('Exons', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#Exon"),HT.Sup(HT.Italic('4')), Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,nowrap='ON'), sort=0),
THCell(HT.TD(HT.Href(text=HT.Span('Tm °C', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#Tm"),HT.Sup(HT.Italic('5')), Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,nowrap='ON'), text="tm", idx=5),
THCell(HT.TD(HT.Href(text=HT.Span('Stacking Energy K', HT.Sub('B'),'T', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#KBT"),HT.Sup(HT.Italic('6')), Class="cbrb cw fwb fs13 b1",align='center',colspan=2,NOWRAP="yes",nowrap='ON'), sort=0),
THCell(HT.TD(HT.Href(text=HT.Span('Mean', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#Mean"),HT.Sup(HT.Italic('7')), Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,nowrap='ON'), text="mean", idx=8),
THCell(HT.TD(HT.Href(text=HT.Span('Stdev', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#Stdev"),HT.Sup(HT.Italic('8')), Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,nowrap='ON'), text="std", idx=9),
THCell(HT.TD(HT.Href(text=HT.Span('Probe h2', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#h2"),HT.Sup(HT.Italic('9')), Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,NOWRAP="yes"), text="h2", idx=10),
THCell(HT.TD(HT.Href(text=HT.Span('Probe Location', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#location"), HT.Sup(HT.Italic('10')),Class="cbrb cw fwb fs13 b1",align='center',colspan=3)),
THCell(HT.TD(HT.Href(text=HT.Span('SNPs', HT.BR(), '(Across all strains)', Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#snps"), HT.Sup(HT.Italic('11')),Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,NOWRAP="yes")),
THCell(HT.TD(HT.Href(text=HT.Span('SNPs', HT.BR(),'(Different alleles only between %s and %s)'%(_mat,_pat), Class="cw fwb fs13"), target="_PROBEINFO", url=pinfopage+"#snps"), HT.Sup(HT.Italic('11')),Class="cbrb cw fwb fs13 b1",align='center',rowspan=2,NOWRAP="yes"))
])
tblobj['header'].append([
THCell(HT.TD(HT.Span('GSB', Class="cw fwb fs13"),align='center', Class="cbrb ffl fwb fs13 b1",), text="gsb", idx=6),
THCell(HT.TD(HT.Span('NSB', Class="cw fwb fs13"),align='center', Class="cbrb ffl fwb fs13 b1",), text="nsb", idx=7),
THCell(HT.TD(HT.Span('Chr', Class="cw fwb fs13"), align='center', Class="cbrb ffl2 fwb fs13 b1",)),
THCell(HT.TD(HT.Span('Start', Class="cw fwb fs13"),align='center', Class="cbrb ffl fwb fs13 b1",)),
THCell(HT.TD(HT.Span('End', Class="cw fwb fs13"),align='center', Class="cbrb ffl fwb fs13 b1",)),
])
tblobj['body'] = []
blatbutton = ''
fetchField = ['Probe.Name','Probe.Sequence','Probe.ExonNo','Probe.Tm', 'Probe.E_GSB','Probe.E_NSB', 'ProbeH2.h2', 'ProbeH2.weight']
query = "SELECT %s FROM (Probe, ProbeSet, ProbeFreeze) left join ProbeH2 on ProbeH2.ProbeId = Probe.Id and ProbeH2.ProbeFreezeId = ProbeFreeze.Id WHERE ProbeSet.Name = '%s' and Probe.ProbeSetId = ProbeSet.Id and ProbeFreeze.Name = '%s' order by Probe.SerialOrder" % (string.join(fetchField,','), self.ProbeSetID, self.probeDatabase)
self.cursor.execute(query)
results = self.cursor.fetchall()
blatsequence = ""
# add by NL: get strains' name in SnpPattern database table
strainsInSnpPatternDBtable=self.getStrainNameIndexPair() # after snpBrowserPage.py change to MVC, this function can be removed in this class and called from other class;
allStrainNameList=[v[0] for v in strainsInSnpPatternDBtable]
speciesid = webqtlDatabaseFunction.retrieveSpeciesId(cursor=self.cursor,RISet=fd.RISet)
for result in results:
"""
ProbeId, CellID,Sequence,ExonNo,Tm, E_GSB,E_NSB = map(self.nullRecord,result)
h2 = ''
query = "SELECT h2 FROM ProbeH2 WHERE ProbeFreezeId = '%s' and ProbeId=%s" % (self.probeDatabase, ProbeId)
self.cursor.execute(query)
results = self.cursor.fetchall()
"""
CellID,Sequence,ExonNo,Tm, E_GSB,E_NSB,h2, weight = map(self.nullRecord,result)
Average = ''
STDEV = ''
mean = -10000.0
stdev = -10000.0
try:
thisTrait.cellid = CellID
thisTrait.retrieveData()
mean, median, var, stdev, sem, N = reaper.anova(thisTrait.exportInformative()[1])
if mean:
Average = '%2.2f' % mean
if stdev:
STDEV = '%2.2f' % stdev
except:
pass
if CellID == self.CellID:
bkColor = "cbrdull fs11 b1"
else:
bkColor = "fs11 b1"
seqcolor= ''
if thisTrait.blatseq:
blatsequence = thisTrait.blatseq
if int(CellID[-1]) % 2 == 1:
seqcolor= 'cdg'
else:
if int(CellID[-1]) % 2 == 1:
seqcolor= 'cdg'
blatsequence += string.strip(Sequence)
if thisTrait.genbankid and (int(CellID[-1]) % 2 == 1):
probeurl = 'http://www.ncbi.nlm.nih.gov/blast/bl2seq/wblast2.cgi?one=%s&sseq=%s' % (thisTrait.genbankid, Sequence)
probefy1 = HT.Input(type="button",value="Blast",onClick= "openNewWin('%s')" % probeurl, Class="buttonsmaller")
else:
probefy1 = ''
traitName = str(thisTrait)
#XZ, Aug 08, 2011: Note that probesets on some affy chips are not name as "xxx_at" (i.e., Affy Mouse Gene 1.0 ST (GPL6246)).
#EnsemblProbeSetID = self.ProbeSetID[0:self.ProbeSetID.index('_at')+3]
EnsemblProbeSetID = self.ProbeSetID
if '_at' in self.ProbeSetID:
EnsemblProbeSetID = self.ProbeSetID[0:self.ProbeSetID.index('_at')+3]
self.cursor.execute('''
SELECT EnsemblProbeLocation.*
FROM EnsemblProbeLocation, EnsemblProbe, EnsemblChip, GeneChipEnsemblXRef, ProbeFreeze
WHERE EnsemblProbeLocation.ProbeId=EnsemblProbe.Id and EnsemblProbe.ChipId=GeneChipEnsemblXRef.EnsemblChipId and
GeneChipEnsemblXRef.GeneChipId=ProbeFreeze.ChipId and EnsemblProbe.Name=%s and EnsemblProbe.ProbeSet=%s and
ProbeFreeze.Name=%s group by Chr, Start, End'''
,(CellID, EnsemblProbeSetID, self.probeDatabase))
LocationFields = self.cursor.fetchall()
Chr=''
Start=''
End=''
if (len(LocationFields)>=1):
Chr,Start,End,Strand,MisMatch,ProbeId = map(self.nullRecord,LocationFields[0])
Start /= 1000000.0
End /= 1000000.0
if (len(LocationFields)>1):
self.cursor.execute('''
SELECT ProbeSet.Chr, ProbeSet.Mb FROM ProbeSet, ProbeFreeze
WHERE ProbeSet.ChipId=ProbeFreeze.ChipId and ProbeSet.Name=%s and ProbeFreeze.Name=%s'''
,(self.ProbeSetID, self.probeDatabase))
ProbeSetChr, ProbeSetMb = map(self.nullRecord,self.cursor.fetchall()[0])
self.cursor.execute('''
SELECT EnsemblProbeLocation.*, ABS(EnsemblProbeLocation.Start/1000000-%s) as Mb
FROM EnsemblProbeLocation, EnsemblProbe, EnsemblChip, GeneChipEnsemblXRef, ProbeFreeze
WHERE EnsemblProbeLocation.ProbeId=EnsemblProbe.Id and EnsemblProbe.ChipId=GeneChipEnsemblXRef.EnsemblChipId and
GeneChipEnsemblXRef.GeneChipId=ProbeFreeze.ChipId and EnsemblProbe.Name=%s and EnsemblProbe.ProbeSet=%s and
EnsemblProbeLocation.Chr=%s and ProbeFreeze.Name=%s order by Mb limit 1'''
,(ProbeSetMb, CellID, EnsemblProbeSetID, ProbeSetChr, self.probeDatabase))
NewLocationFields = self.cursor.fetchall()
if (len(NewLocationFields)>0):
Chr,Start,End,Strand,MisMatch,ProbeId,Mb = map(self.nullRecord,NewLocationFields[0])
Start /= 1000000.0
End /= 1000000.0
snp_collection = []
snpDiff_collection=[]
startIndex=3
if Chr != '' and Start != '' and End != '' and speciesid != None:
self.cursor.execute('''
SELECT a.SnpName, a.Id, b.* FROM SnpAll a, SnpPattern b
WHERE a.Chromosome=%s and a.Position>=%s and a.Position<=%s
and a.SpeciesId=%s and a.Id=b.SnpId'''
,(Chr, Start, End, speciesid)) #chr,Start, End, 1))
snpresults = self.cursor.fetchall()
index1=allStrainNameList.index(_mat) #_mat index in results
index2=allStrainNameList.index(_pat) #_pat index in results
for v in snpresults:
#updated by NL: 07-22-2011 check 'limit to' to get snpBrowser snpresults
snp_collection.append(HT.Href(text=v[0], url=os.path.join(webqtlConfig.CGIDIR,
"main.py?FormID=SnpBrowserResultPage&submitStatus=1&customStrain=1")+ "&geneName=%s" % v[0], Class="fs12 fwn", target="_blank"))
snp_collection.append(HT.BR())
#updated by NL: 07-27-2011 link snp info for different allele only
strain1_allele=v[startIndex+index1]
strain2_allele=v[startIndex+index2]
if strain1_allele!=strain2_allele:
snpDiff_collection.append(HT.Href(text=v[0], url=os.path.join(webqtlConfig.CGIDIR,
"main.py?FormID=SnpBrowserResultPage&submitStatus=1&customStrain=1&diffAlleles=1&chosenStrains=%s"%chosenStrains)+ "&geneName=%s" % v[0], Class="fs12 fwn", target="_blank"))
snpDiff_collection.append(HT.BR())
tr = []
tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class='checkbox', name="searchResult",value=traitName, onClick="highlight(this)"), align="right", Class=bkColor, nowrap="on"), text=traitName))
tr.append(TDCell(HT.TD(HT.Href(text=CellID, url = "javascript:showDatabase2('%s','%s','%s');" % (self.database,self.ProbeSetID,CellID),Class="fs12 fwn"),Class=bkColor), traitName, traitName.upper()))
tr.append(TDCell(HT.TD(Sequence, Class=bkColor + " %s ffmono fs14" % seqcolor),Sequence,Sequence.upper()))
tr.append(TDCell(HT.TD(probefy1,align='center',Class=bkColor)))
tr.append(TDCell(HT.TD(ExonNo,align='center',Class=bkColor)))
try:
TmValue = float(Tm)
except:
TmValue = 0.0
tr.append(TDCell(HT.TD(Tm,align='center',Class=bkColor), Tm, TmValue))
try:
E_GSBValue = float(E_GSB)
except:
E_GSBValue = -10000.0
tr.append(TDCell(HT.TD(E_GSB,align='center',Class=bkColor), E_GSB, E_GSBValue))
try:
E_NSBValue = float(E_NSB)
except:
E_NSBValue = -10000.0
tr.append(TDCell(HT.TD(E_NSB,align='center',Class=bkColor), E_NSB, E_NSBValue))
tr.append(TDCell(HT.TD(Average,align='center',Class=bkColor), Average, mean))
tr.append(TDCell(HT.TD(STDEV,align='center',Class=bkColor), STDEV, stdev))
try:
h2Value = float(h2)
except:
h2Value = -10000.0
tr.append(TDCell(HT.TD(h2,align='center',Class=bkColor), h2, h2Value))
tr.append(TDCell(HT.TD(Chr,align='left',Class=bkColor)))
tr.append(TDCell(HT.TD(Start,align='left',Class=bkColor)))
tr.append(TDCell(HT.TD(End,align='left',Class=bkColor)))
snp_td = HT.TD(align='left',Class=bkColor)
for one_snp_href in snp_collection:
snp_td.append(one_snp_href)
tr.append(TDCell(snp_td))
#07-27-2011:add by NL: show SNP results for different allele only
snpDiff_td= HT.TD(align='left', valign='top', Class=bkColor)
for one_snpDiff_href in snpDiff_collection:
snpDiff_td.append(one_snpDiff_href)
tr.append(TDCell(snpDiff_td))
tblobj['body'].append(tr)
# import cPickle
filename = webqtlUtil.genRandStr("Probe_")
objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
cPickle.dump(tblobj, objfile)
objfile.close()
# NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py;
div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=("", ""), tableID = "sortable", addIndex = "1"), Id="sortable")
#UCSC
_Species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, RISet=fd.RISet)
if _Species == "rat":
thisurl = webqtlConfig.UCSC_BLAT % ('rat', 'rn3', blatsequence)
elif _Species == "mouse":
thisurl = webqtlConfig.UCSC_BLAT % ('mouse', 'mm9', blatsequence)
else:
thisurl = ""
if thisurl:
blatbutton = HT.Input(type='button' ,name='blatPM',value='Verify UCSC', onClick="window.open('%s','_blank')" % thisurl,Class="button")
else:
blatbutton = ""
#GenBank
genbankSeq = ""
if thisTrait.genbankid:
self.cursor.execute("SELECT Sequence FROM Genbank WHERE Id = '%s'" % thisTrait.genbankid )
genbankSeq = self.cursor.fetchone()
if genbankSeq:
genbankSeq = genbankSeq[0]
if genbankSeq:
if _Species == "rat":
thisurl2 = webqtlConfig.UCSC_BLAT % ('rat', 'rn3', genbankSeq)
if _Species == "mouse":
thisurl2 = webqtlConfig.UCSC_BLAT % ('mouse', 'mm9', genbankSeq)
else:
thisurl2 = ''
if thisurl2:
blatbutton2 = HT.Input(type='button' ,name='blatPM',value='Verify GenBank', onClick="window.open('%s','_blank')" % thisurl2,Class="button")
else:
blatbutton2 = ""
#Snp
snpBrowser = ""
if thisTrait.symbol and _Species == 'mouse':
self.cursor.execute("select geneSymbol from GeneList where geneSymbol = %s", thisTrait.symbol)
geneName = self.cursor.fetchone()
if geneName:
snpurl = os.path.join(webqtlConfig.CGIDIR, "main.py?FormID=snpBrowser") + "&geneName=%s" % geneName[0]
else:
if thisTrait.chr and thisTrait.mb:
snpurl = os.path.join(webqtlConfig.CGIDIR, "main.py?FormID=snpBrowser") + \
"&chr=%s&start=%2.6f&end=%2.6f" % (thisTrait.chr, thisTrait.mb-0.002, thisTrait.mb+0.002)
else:
snpurl = ""
if snpurl:
snpBrowser = HT.Input(type="button",value="SNP Browser",onClick= \
"openNewWin('%s')" % snpurl, Class="button")
else:
snpBrowser = ""
#end if
heading = HT.Paragraph('Probe Information', Class="title")
intro = HT.Paragraph('The table below lists information of all probes of probe set ',HT.Span(self.ProbeSetID, Class="fwb fs13"),' from database ', HT.Span(self.probeDatabase, Class="fwb fs13"), ".")
buttons = HT.Paragraph(probedata,probeinfo,heatmap,cormatrix,blatbutton,blatbutton2,verifyButton,snpBrowser, HT.P(),selectall,selectpm,selectmm,selectinvert,reset,addselect)
if rudi_track:
buttons.append(rudi_track)
form.append(buttons,div,HT.P())
TD_LR.append(heading,intro,form, HT.P())
self.dict['basehref'] = ''
self.dict['body'] = str(TD_LR)
self.dict['title'] = self.db.shortname + ' : ' + self.ProbeSetID +' / Probe Information'
# updated by NL, javascript function xmlhttpPost(strURL, div, querystring) and function updatepage(Id, str)
# have been moved to dhtml.js
self.dict['js1'] = ''
def basicStatsTable(vals, trait_type=None, cellid=None, heritability=None):
print("basicStatsTable called - len of vals", len(vals))
st = {} # This is the dictionary where we'll put everything for the template
valsOnly = []
dataXZ = vals[:]
for i in range(len(dataXZ)):
valsOnly.append(dataXZ[i][1])
(st['traitmean'],
st['traitmedian'],
st['traitvar'],
st['traitstdev'],
st['traitsem'],
st['N']) = reaper.anova(valsOnly) #ZS: Should convert this from reaper to R in the future
#tbl = HT.TableLite(cellpadding=20, cellspacing=0)
#dataXZ = vals[:]
dataXZ = sorted(vals, webqtlUtil.cmpOrder)
print("data for stats is:", pf(dataXZ))
for num, item in enumerate(dataXZ):
print(" %i - %s" % (num, item))
print(" length:", len(dataXZ))
st['min'] = dataXZ[0][1]
st['max'] = dataXZ[-1][1]
numbers = [x[1] for x in dataXZ]
stats = Stats(numbers)
at75 = stats.percentile(75)
at25 = stats.percentile(25)
print("should get a stack")
traceback.print_stack()
print("Interquartile:", at75 - at25)
#tbl.append(HT.TR(HT.TD("Statistic",align="left", Class="fs14 fwb ffl b1 cw cbrb", width = 180),
# HT.TD("Value", align="right", Class="fs14 fwb ffl b1 cw cbrb", width = 60)))
#tbl.append(HT.TR(HT.TD("N of Samples",align="left", Class="fs13 b1 cbw c222"),
# HT.TD(N,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD("Mean",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitmean,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD("Median",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitmedian,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
##tbl.append(HT.TR(HT.TD("Variance",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
## HT.TD("%2.3f" % traitvar,nowrap="yes",align="left", Class="fs13 b1 cbw c222")))
#tbl.append(HT.TR(HT.TD("Standard Error (SE)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitsem,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD("Standard Deviation (SD)", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitstdev,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD("Minimum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%s" % dataXZ[0][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD("Maximum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%s" % dataXZ[-1][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
if (trait_type != None and trait_type == 'ProbeSet'):
#tbl.append(HT.TR(HT.TD("Range (log2)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
#tbl.append(HT.TR(HT.TD(HT.Span(HT.Href(url="/glossary.html#Interquartile", target="_blank", text="Interquartile Range", Class="non_bold")), align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.2f" % pow(2.0,(dataXZ[int((N-1)*3.0/4.0)][1]-dataXZ[int((N-1)/4.0)][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
st['range_log2'] = dataXZ[-1][1]-dataXZ[0][1]
st['range_fold'] = pow(2.0, (dataXZ[-1][1]-dataXZ[0][1]))
st['interquartile'] = pow(2.0, (dataXZ[int((st['N']-1)*3.0/4.0)][1]-dataXZ[int((st['N']-1)/4.0)][1]))
#XZ, 04/01/2009: don't try to get H2 value for probe.
if not cellid:
if heritability:
# This field needs to still be put into the Jinja2 template
st['heritability'] = heritability
#tbl.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("%s" % heritability, nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
# Lei Yan
# 2008/12/19
return st
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 getCollectionTableBody(self, RISet=None, traitList=None, formName=None, species=''):
tblobj_body = []
className = "fs12 fwn b1 c222"
for thisTrait in traitList:
tr = []
if not thisTrait.haveinfo:
thisTrait.retrieveInfo(QTL=1)
if thisTrait.riset != RISet:
continue
trId = str(thisTrait)
#XZ: check box column
tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkallbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class=className), text=trId))
#XZ: Dataset column
tr.append(TDCell(HT.TD(thisTrait.db.displayname, Class="fs12 fwn b1 c222"), thisTrait.db.displayname, thisTrait.db.displayname.upper()))
#XZ: Trait ID column
if thisTrait.cellid:
tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.cellid,url="javascript:showDatabase3('%s','%s','%s','%s')" % (formName, thisTrait.db.name, thisTrait.name, thisTrait.cellid), Class="fs12 fwn"), nowrap="yes",align="left", Class=className),str(thisTrait.cellid), thisTrait.cellid))
else:
tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.getGivenName(),url="javascript:showDatabase3('%s','%s','%s','')" % (formName, thisTrait.db.name, thisTrait.name), Class="fs12 fwn"), nowrap="yes",align="left", Class=className),str(thisTrait.name), thisTrait.name))
#XZ: Symbol column and Description column
if (thisTrait.db.type == "Publish"):
AbbreviationString = "--"
if (thisTrait.post_publication_abbreviation != None):
AbbreviationString = thisTrait.post_publication_abbreviation
PhenotypeString = thisTrait.post_publication_description
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 = "--"
PhenotypeString = thisTrait.pre_publication_description
if AbbreviationString == "--":
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
else:
tr.append(TDCell(HT.TD(AbbreviationString, Class=className), AbbreviationString, AbbreviationString.upper()))
if PhenotypeString:
PhenotypeString_upper = PhenotypeString.upper()
else:
PhenotypeString_upper = PhenotypeString
tr.append(TDCell(HT.TD(PhenotypeString, Class=className), PhenotypeString, PhenotypeString_upper))
elif (thisTrait.db.type == "ProbeSet" or thisTrait.db.type == "Temp"):
description_string = str(thisTrait.description).strip()
if (thisTrait.db.type == "ProbeSet"):
if (thisTrait.symbol != None):
if thisTrait.geneid:
symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % thisTrait.geneid, Class="font_black fs12 fwn")
else:
symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gene&term=%s" % thisTrait.symbol, Class="font_black fs12 fwn")
tr.append(TDCell(HT.TD(symbolurl, align="left", Class="fs12 fwn b1 c222 fsI"), thisTrait.symbol, thisTrait.symbol))
else:
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
target_string = str(thisTrait.probe_target_description).strip()
description_display = ''
if len(description_string) > 1 and description_string != 'None':
description_display = description_string
else:
description_display = thisTrait.symbol
if len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None':
description_display = description_display + '; ' + target_string.strip()
description_string = description_display
else:
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
tr.append(TDCell(HT.TD(description_string, Class=className), description_string, description_string))
else:
if (thisTrait.name != None):
tr.append(TDCell(HT.TD(thisTrait.name, Class="fs12 fwn b1 c222"), thisTrait.name, thisTrait.name))
else:
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
#XZ: Location column
if (thisTrait.db.type == "Publish"):
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
else:
if thisTrait.db.type == "ProbeSet" and thisTrait.cellid:
EnsemblProbeSetID = thisTrait.name
if '_at' in thisTrait.name:
EnsemblProbeSetID = thisTrait.name[0:thisTrait.name.index('_at')+3]
#These tables (Ensembl) were created by Xusheng Wang in 2010 and are mm9 (so they'll need to be changed at some point to be mm10.
self.cursor.execute('''
SELECT EnsemblProbeLocation.*
FROM EnsemblProbeLocation, EnsemblProbe, EnsemblChip, GeneChipEnsemblXRef, ProbeFreeze, ProbeSetFreeze
WHERE EnsemblProbeLocation.ProbeId=EnsemblProbe.Id and EnsemblProbe.ChipId=GeneChipEnsemblXRef.EnsemblChipId and
GeneChipEnsemblXRef.GeneChipId=ProbeFreeze.ChipId and EnsemblProbe.Name=%s and EnsemblProbe.ProbeSet=%s and
ProbeSetFreeze.Id=%s and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id group by Chr, Start, End'''
,(thisTrait.cellid, EnsemblProbeSetID, thisTrait.db.id))
LocationFields = self.cursor.fetchall()
Chr=''
Mb=''
Start=''
End=''
if (len(LocationFields)>=1):
Chr,Start,Start_2016,End,End_2016,Strand,MisMatch,ProbeId = map(self.nullRecord,LocationFields[0])
Start /= 1000000.0
End /= 1000000.0
Mb = Start
if (len(LocationFields)>1):
self.cursor.execute('''
SELECT ProbeSet.Chr, ProbeSet.Mb FROM ProbeSet, ProbeFreeze, ProbeSetFreeze
WHERE ProbeSet.ChipId=ProbeFreeze.ChipId and ProbeSet.Name=%s and ProbeSetFreeze.Id=%s and
ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id'''
,(thisTrait.name, thisTrait.db.id))
ProbeSetChr, ProbeSetMb = map(self.nullRecord,self.cursor.fetchall()[0])
self.cursor.execute('''
SELECT EnsemblProbeLocation.*, ABS(EnsemblProbeLocation.Start/1000000-%s) as Mb
FROM EnsemblProbeLocation, EnsemblProbe, EnsemblChip, GeneChipEnsemblXRef, ProbeFreeze, ProbeSetFreeze
WHERE EnsemblProbeLocation.ProbeId=EnsemblProbe.Id and EnsemblProbe.ChipId=GeneChipEnsemblXRef.EnsemblChipId and
GeneChipEnsemblXRef.GeneChipId=ProbeFreeze.ChipId and EnsemblProbe.Name=%s and EnsemblProbe.ProbeSet=%s and
EnsemblProbeLocation.Chr=%s and ProbeSetFreeze.Id=%s and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id order by Mb limit 1'''
,(ProbeSetMb, thisTrait.cellid, EnsemblProbeSetID, ProbeSetChr, thisTrait.db.id))
NewLocationFields = self.cursor.fetchall()
if (len(NewLocationFields)>0):
Chr,Start,Start_2016,End,End_2016,Strand,MisMatch,ProbeId,Mb = map(self.nullRecord,NewLocationFields[0])
Start /= 1000000.0
End /= 1000000.0
Mb = Start
#ZS: trait_location_value is used for sorting
trait_location_repr = "--"
trait_location_value = 1000000
if Chr and Mb:
try:
trait_location_value = int(Chr)*1000 + Mb
except:
if Chr.upper() == "X":
trait_location_value = 20*1000 + Mb
else:
trait_location_value = ord(str(Chr).upper()[0])*1000 + Mb
trait_location_repr = "Chr%s: %.6f" % (Chr, float(Mb) )
tr.append(TDCell(HT.TD(trait_location_repr, nowrap='ON', Class=className), trait_location_repr, trait_location_value))
else:
#ZS: trait_location_value is used for sorting
trait_location_repr = "--"
trait_location_value = 1000000
if hasattr(thisTrait, 'chr') and hasattr(thisTrait, 'mb') and thisTrait.chr and thisTrait.mb:
try:
trait_location_value = int(thisTrait.chr)*1000 + thisTrait.mb
except:
if thisTrait.chr.upper() == "X":
trait_location_value = 20*1000 + thisTrait.mb
else:
trait_location_value = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb
trait_location_repr = "Chr%s: %.6f" % (thisTrait.chr, float(thisTrait.mb) )
tr.append(TDCell(HT.TD(trait_location_repr, nowrap='ON', Class=className), trait_location_repr, trait_location_value))
#XZ: Mean column
if (thisTrait.db.type == "ProbeSet"):
if thisTrait.cellid:
mean = -10000.0
try:
thisTrait.retrieveData()
mean, median, var, stdev, sem, N = reaper.anova(thisTrait.exportInformative()[1])
except:
pass
repr = '%2.3f' % mean
mean = '%2.2f' % mean
tr.append(TDCell(HT.TD(repr, Class=className, align='right', nowrap='ON'),repr, mean))
else:
self.cursor.execute("""
select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
where ProbeSetXRef.ProbeSetFreezeId = %d and
ProbeSet.Id = ProbeSetXRef.ProbeSetId and
ProbeSet.Name = '%s'
""" % (thisTrait.db.id, thisTrait.name))
result = self.cursor.fetchone()
if result:
if result[0]:
mean = result[0]
else:
mean=0
else:
mean = 0
#XZ, 06/05/2009: It is neccessary to turn on nowrap
repr = "%2.3f" % mean
tr.append(TDCell(HT.TD(repr, Class=className, align='right', nowrap='ON'),repr, mean))
elif (thisTrait.db.type == "Publish"):
self.cursor.execute("""
select count(PublishData.value), sum(PublishData.value) from PublishData, PublishXRef, PublishFreeze
where PublishData.Id = PublishXRef.DataId and
PublishXRef.Id = %s and
PublishXRef.InbredSetId = PublishFreeze.InbredSetId and
PublishFreeze.Id = %d
""" % (thisTrait.name, thisTrait.db.id))
result = self.cursor.fetchone()
if result:
if result[0] and result[1]:
mean = result[1]/result[0]
else:
mean = 0
else:
mean = 0
repr = "%2.3f" % mean
tr.append(TDCell(HT.TD(repr, Class=className, align='right', nowrap='ON'),repr, mean))
else:
tr.append(TDCell(HT.TD("--", Class=className, align='left', nowrap='ON'),"--", 0))
#Number of cases
n_cases_value = 0
n_cases_repr = "--"
if (thisTrait.db.type == "Publish"):
self.cursor.execute("""
select count(PublishData.value) from PublishData, PublishXRef, PublishFreeze
where PublishData.Id = PublishXRef.DataId and
PublishXRef.Id = %s and
PublishXRef.InbredSetId = PublishFreeze.InbredSetId and
PublishFreeze.Id = %d
""" % (thisTrait.name, thisTrait.db.id))
result = self.cursor.fetchone()
if result:
if result[0]:
n_cases_value = result[0]
n_cases_repr = result[0]
if (n_cases_value == "--"):
tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
else:
tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value))
elif (thisTrait.db.type == "ProbeSet"):
self.cursor.execute("""
select count(ProbeSetData.value) from ProbeSet, ProbeSetXRef, ProbeSetData, ProbeSetFreeze
where ProbeSet.Name='%s' and
ProbeSetXRef.ProbeSetId = ProbeSet.Id and
ProbeSetXRef.DataId = ProbeSetData.Id and
ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id and
ProbeSetFreeze.Name = '%s'
""" % (thisTrait.name, thisTrait.db.name))
result = self.cursor.fetchone()
if result:
if result[0]:
n_cases_value = result[0]
n_cases_repr = result[0]
if (n_cases_value == "--"):
tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
else:
tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value))
elif (thisTrait.db.type == "Geno"):
self.cursor.execute("""
select count(GenoData.value) from GenoData, GenoXRef, GenoFreeze, Geno, Strain
where Geno.SpeciesId = %s and Geno.Name='%s' and
GenoXRef.GenoId = Geno.Id and
GenoXRef.DataId = GenoData.Id and
GenoXRef.GenoFreezeId = GenoFreeze.Id and
GenoData.StrainId = Strain.Id and
GenoFreeze.Name = '%s'
""" % (webqtlDatabaseFunction.retrieveSpeciesId(self.cursor, thisTrait.db.riset), thisTrait.name, thisTrait.db.name))
result = self.cursor.fetchone()
if result:
if result[0]:
n_cases_value = result[0]
n_cases_repr = result[0]
if (n_cases_value == "--"):
tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
else:
tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value))
else:
tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
#XZ: Max LRS column and Max LRS Location column
if (thisTrait.db.type != "Geno"):
#LRS value
LRS_score_repr = '--'
LRS_score_value = 0
if hasattr(thisTrait, 'lrs') and thisTrait.lrs:
LRS_score_repr = '%3.1f' % thisTrait.lrs
LRS_score_value = thisTrait.lrs
tr.append(TDCell(HT.TD(LRS_score_repr, Class=className, align='right', nowrap="on"), LRS_score_repr, LRS_score_value))
#LRS location
LRS_location_repr = '--'
LRS_location_value = 1000000
LRS_flag = 1
#Max LRS and its Locus location
if hasattr(thisTrait, 'lrs') and hasattr(thisTrait, 'locus') and thisTrait.lrs and thisTrait.locus:
self.cursor.execute("""
select Geno.Chr, Geno.Mb from Geno, Species
where Species.Name = '%s' and
Geno.Name = '%s' and
Geno.SpeciesId = Species.Id
""" % (species, thisTrait.locus))
result = self.cursor.fetchone()
if result:
if result[0] and result[1]:
LRS_Chr = result[0]
LRS_Mb = result[1]
#XZ: LRS_location_value is used for sorting
try:
LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb)
except:
if LRS_Chr.upper() == 'X':
LRS_location_value = 20*1000 + float(LRS_Mb)
else:
LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb)
LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb))
LRS_flag = 0
tr.append(TDCell(HT.TD(LRS_location_repr, Class=className), LRS_location_repr, LRS_location_value))
if LRS_flag:
tr.append(TDCell(HT.TD(LRS_location_repr, Class=className), LRS_location_repr, LRS_location_value))
else:
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", 0))
tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", 1000000))
try:
additive = '%3.3f' % thisTrait.additive
except Exception:
additive = ''
tr.append(TDCell(HT.TD(additive, Class=className, align="right", nowrap="on"), additive, additive))
tblobj_body.append(tr)
return tblobj_body
def __init__(self, fd):
templatePage.__init__(self, fd)
if not fd.genotype:
fd.readData()
fd.parentsf14regression = fd.formdata.getvalue('parentsf14regression')
weightedRegression = fd.formdata.getvalue('applyVarianceSE')
if fd.parentsf14regression and fd.genotype_2:
_genotype = fd.genotype_2
else:
_genotype = fd.genotype_1
_strains, _vals, _vars, N = fd.informativeStrains(_genotype.prgy, weightedRegression)
self.data = fd
if self.data.identification:
heading2 = HT.Paragraph('Trait ID: %s' % self.data.identification)
heading2.__setattr__("class","subtitle")
self.dict['title'] = '%s: Composite Regression' % self.data.identification
else:
heading2 = ""
self.dict['title'] = 'Composite Regression'
if self.data.traitInfo:
symbol,chromosome,MB = string.split(fd.traitInfo,'\t')
heading3 = HT.Paragraph('[ ',HT.Strong(HT.Italic('%s' % symbol,id="green")),' on Chr %s @ %s Mb ]' % (chromosome,MB))
else:
heading3 = ""
if N < webqtlConfig.KMININFORMATIVE:
heading = "Composite Regression"
detail = ['Fewer than %d strain data were entered for %s data set. No mapping attempted.' % (webqtlConfig.KMININFORMATIVE, self.data.RISet)]
self.error(heading=heading,detail=detail)
return
else:
heading = HT.Paragraph('Trait Data Entered for %s Set' % self.data.RISet)
heading.__setattr__("class","title")
tt = HT.TableLite()
for ii in range(N/2):
tt.append(HT.TR(HT.TD(_strains[2*ii],nowrap="yes"),HT.TD(width=10), HT.TD(_vals[2*ii], nowrap="yes"), \
HT.TD(width=20), HT.TD(_strains[2*ii+1],nowrap="yes"),HT.TD(width=10), HT.TD(_vals[2*ii+1],nowrap="yes")))
if N % 2:
tt.append(HT.TR(HT.TD(_strains[N-1],nowrap="yes"),HT.TD(width=10), HT.TD(_vals[N-1],nowrap="yes"), \
HT.TD(width=20), HT.TD("",nowrap="yes"),HT.TD(width=10), HT.TD("",nowrap="yes")))
indata = tt
mean, median, var, stdev, sem, N = reaper.anova(_vals)
stats = HT.Paragraph('Number of entered values = %d ' % N,HT.BR(),\
'Mean value = %8.3f ' % mean, HT.BR(), \
'Median value = %8.3f ' % median, HT.BR(), \
'Variance = %8.3f ' % var, HT.BR(), \
'Standard Deviation = %8.3f ' % stdev, HT.BR(), \
'Standard Error = %8.3f ' % sem)
self.controlLocus = fd.formdata.getvalue('controlLocus')
heading4 = HT.Blockquote('Control Background Selected for %s Data Set:' % self.data.RISet)
heading4.__setattr__("class","subtitle")
datadiv = HT.TD(heading, HT.Center(heading2,heading3,indata, stats, heading4,HT.Center(self.controlLocus)), width='45%',valign='top', bgColor='#eeeeee')
resultstable = self.GenReport(fd, _genotype, _strains, _vals, _vars)
self.dict['body'] = str(datadiv)+str(resultstable)
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
strainlisthead = []
for strain in strainlist:
strainlisthead += [strain + "_Value"]
strainlisthead += [strain + "_SE"]
strainlisthead += [strain + "_N"]
fields = ["Index", "Species", "Cross", "Database", "ProbeSetID/RecordID", "PubMed_ID",
"Pre Publication Description", "Post Publication Description", "Original Description", "Pre Publication Abbreviation", "Post Publication Abbreviation",
"Mean_Expression"] + strainlisthead
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):
text.append([str(i+1), self.Species, self.RISet, thisTrait.db.fullname, thisTrait.name, thisTrait.pubmed_id,
thisTrait.pre_publication_description, thisTrait.post_publication_description,
thisTrait.original_description,
thisTrait.pre_publication_abbreviation, thisTrait.post_publication_abbreviation])
testval = thisTrait.exportData(strainlist)
try:
mean = reaper.anova(testval)[0]
except:
mean = 'N/A'
text[-1].append(mean)
testvar = thisTrait.exportData(strainlist, type="var")
testn = thisTrait.exportData(strainlist, type="N")
testdata = zip(testval, testvar, testn)
testdatalist = []
for data in testdata:
testdatalist += list(data)
text[-1] += testdatalist
self.content_type = 'application/xls'
self.content_disposition = 'attachment; filename=%s' % ('export-%s.txt' % time.strftime("%y-%m-%d-%H-%M"))
self.attachment += ("Data source: The GeneNetwork at %s\n" % webqtlConfig.PORTADDR)
self.attachment += ("Citations: Please see %s/reference.html\n" % webqtlConfig.PORTADDR)
self.attachment += ("Date: %s\n" % time.strftime("%B %d, %Y", time.gmtime()))
self.attachment += ("Time: %s GMT\n" % time.strftime("%H:%M", time.gmtime()))
self.attachment += ("Status of data ownership: Possibly unpublished data; please see %s/statusandContact.html for details on sources, ownership, and usage of these data.\n" % webqtlConfig.PORTADDR)
self.attachment += ("This output file contains data from %d GeneNetwork databases listed below.\n" % len(traitList))
self.attachment += ("\n")
self.attachment += ("Funding for The GeneNetwork:\n")
self.attachment += ("NIGMS Systems Genetics and Precision Medicine project (R01 GM123489, 2017-2021)\n")
self.attachment += ("NIDA NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017-2022)\n")
self.attachment += ("NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013-2018)\n")
self.attachment += ("NIAAA Integrative Neuroscience Initiative on Alcoholism (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017)\n")
self.attachment += ("NIDA, NIMH, and NIAAA (P20-DA 21131, 2001-2012)\n")
self.attachment += ("NCI MMHCC (U01CA105417), NCRR, BIRN, (U24 RR021760)\n")
self.attachment += ("PLEASE RETAIN DATA SOURCE INFORMATION WHENEVER POSSIBLE\n")
self.attachment += ("\n")
for item in text:
self.attachment += string.join(map(lambda cell : self.trim(str(cell)), 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 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):
templatePage.__init__(self, fd)
if not fd.genotype:
fd.readGenotype()
strainlist2 = fd.strainlist
if fd.allstrainlist:
strainlist2 = fd.allstrainlist
fd.readData(strainlist2)
specialStrains = []
setStrains = []
for item in strainlist2:
if item not in fd.strainlist and item.find('F1') < 0:
specialStrains.append(item)
else:
setStrains.append(item)
specialStrains.sort()
#So called MDP Panel
if specialStrains:
specialStrains = fd.f1list+fd.parlist+specialStrains
self.plotType = fd.formdata.getvalue('ptype', '0')
plotStrains = strainlist2
if specialStrains:
if self.plotType == '1':
plotStrains = setStrains
if self.plotType == '2':
plotStrains = specialStrains
self.dict['title'] = 'Basic Statistics'
if not self.openMysql():
return
self.showstrains = 1
self.identification = "unnamed trait"
self.fullname = fd.formdata.getvalue('fullname', '')
if self.fullname:
self.Trait = webqtlTrait(fullname=self.fullname, cursor=self.cursor)
self.Trait.retrieveInfo()
else:
self.Trait = None
if fd.identification:
self.identification = fd.identification
self.dict['title'] = self.identification + ' / '+self.dict['title']
TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee')
##should not display Variance, but cannot convert Variance to SE
#print plotStrains, fd.allTraitData.keys()
if len(fd.allTraitData) > 0:
vals=[]
InformData = []
for _strain in plotStrains:
if fd.allTraitData.has_key(_strain):
_val, _var = fd.allTraitData[_strain].val, fd.allTraitData[_strain].var
if _val != None:
vals.append([_strain, _val, _var])
InformData.append(_val)
if len(vals) >= self.plotMinInformative:
supertable2 = HT.TableLite(border=0, cellspacing=0, cellpadding=5,width="800")
staIntro1 = HT.Paragraph("The table and plots below list the basic statistical analysis result of trait",HT.Strong(" %s" % self.identification))
#####
#anova
#####
traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(InformData)
TDStatis = HT.TD(width="360", valign="top")
tbl2 = HT.TableLite(cellpadding=5, cellspacing=0, Class="collap")
dataXZ = vals[:]
dataXZ.sort(self.cmpValue)
tbl2.append(HT.TR(HT.TD("Statistic",align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 200),
HT.TD("Value", align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 140)))
tbl2.append(HT.TR(HT.TD("N of Cases",align="center", Class="fs13 b1 cbw c222"),
HT.TD(N,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Mean",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmean,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Median",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmedian,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
#tbl2.append(HT.TR(HT.TD("Variance",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitvar,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("SEM",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitsem,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("SD",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitstdev,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Minimum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[0][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Maximum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[-1][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
if self.Trait and self.Trait.db.type == 'ProbeSet':
#IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14")
#IRQuest.append(HT.BR())
#IRQuest.append(" (fold difference)")
tbl2.append(HT.TR(HT.TD("Range (log2)",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD(HT.Span("Quartile Range",HT.BR()," (fold difference)"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.2f" % pow(2.0,(dataXZ[int((N-1)*3.0/4.0)][1]-dataXZ[int((N-1)/4.0)][1])), nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
# (Lei Yan)
# 2008/12/19
self.Trait.retrieveData()
#XZ, 04/01/2009: don't try to get H2 value for probe.
if self.Trait.cellid:
pass
else:
self.cursor.execute("SELECT DataId, h2 from ProbeSetXRef WHERE DataId = %d" % self.Trait.mysqlid)
dataid, heritability = self.cursor.fetchone()
if heritability:
tbl2.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("%s" % heritability, nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
else:
tbl2.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("NaN", nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
# Lei Yan
# 2008/12/19
TDStatis.append(tbl2)
plotHeight = 220
plotWidth = 120
xLeftOffset = 60
xRightOffset = 25
yTopOffset = 20
yBottomOffset = 53
canvasHeight = plotHeight + yTopOffset + yBottomOffset
canvasWidth = plotWidth + xLeftOffset + xRightOffset
canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
XXX = [('', InformData[:])]
Plot.plotBoxPlot(canvas, XXX, offset=(xLeftOffset, xRightOffset, yTopOffset, yBottomOffset), XLabel= "Trait")
filename= webqtlUtil.genRandStr("Box_")
canvas.save(webqtlConfig.IMGDIR+filename, format='gif')
img=HT.Image('/image/'+filename+'.gif',border=0)
#supertable2.append(HT.TR(HT.TD(staIntro1, colspan=3 )))
tb = HT.TableLite(border=0, cellspacing=0, cellpadding=0)
tb.append(HT.TR(HT.TD(img, align="left", style="border: 1px solid #999999; padding:0px;")))
supertable2.append(HT.TR(TDStatis, HT.TD(tb)))
dataXZ = vals[:]
tvals = []
tnames = []
tvars = []
for i in range(len(dataXZ)):
tvals.append(dataXZ[i][1])
tnames.append(webqtlUtil.genShortStrainName(fd, dataXZ[i][0]))
tvars.append(dataXZ[i][2])
nnStrain = len(tnames)
sLabel = 1
###determine bar width and space width
if nnStrain < 20:
sw = 4
elif nnStrain < 40:
sw = 3
else:
sw = 2
### 700 is the default plot width minus Xoffsets for 40 strains
defaultWidth = 650
if nnStrain > 40:
defaultWidth += (nnStrain-40)*10
defaultOffset = 100
bw = int(0.5+(defaultWidth - (nnStrain-1.0)*sw)/nnStrain)
if bw < 10:
bw = 10
plotWidth = (nnStrain-1)*sw + nnStrain*bw + defaultOffset
plotHeight = 500
#print [plotWidth, plotHeight, bw, sw, nnStrain]
c = pid.PILCanvas(size=(plotWidth,plotHeight))
Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title='%s by Case (sorted by name)' % self.identification, sLabel = sLabel, barSpace = sw)
filename= webqtlUtil.genRandStr("Bar_")
c.save(webqtlConfig.IMGDIR+filename, format='gif')
img0=HT.Image('/image/'+filename+'.gif',border=0)
dataXZ = vals[:]
dataXZ.sort(self.cmpValue)
tvals = []
tnames = []
tvars = []
for i in range(len(dataXZ)):
tvals.append(dataXZ[i][1])
tnames.append(webqtlUtil.genShortStrainName(fd, dataXZ[i][0]))
tvars.append(dataXZ[i][2])
c = pid.PILCanvas(size=(plotWidth,plotHeight))
Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title='%s by Case (ranked)' % self.identification, sLabel = sLabel, barSpace = sw)
filename= webqtlUtil.genRandStr("Bar_")
c.save(webqtlConfig.IMGDIR+filename, format='gif')
img1=HT.Image('/image/'+filename+'.gif',border=0)
# Lei Yan
# 05/18/2009
# report
title = HT.Paragraph('REPORT on the variation of Shh (or PCA Composite Trait XXXX) (sonic hedgehog) in the (insert Data set name) of (insert Species informal name, e.g., Mouse, Rat, Human, Barley, Arabidopsis)', Class="title")
header = HT.Paragraph('''This report was generated by GeneNetwork on May 11, 2009, at 11.20 AM using the Basic Statistics module (v 1.0) and data from the Hippocampus Consortium M430v2 (Jun06) PDNN data set. For more details and updates on this data set please link to URL:get Basic Statistics''')
hr = HT.HR()
p1 = HT.Paragraph('''Trait values for Shh were taken from the (insert Database name, Hippocampus Consortium M430v2 (Jun06) PDNN). GeneNetwork contains data for NN (e.g., 99) cases. In general, data are averages for each case. A summary of mean, median, and the range of these data are provided in Table 1 and in the box plot (Figure 1). Data for individual cases are provided in Figure 2A and 2B, often with error bars (SEM). ''')
p2 = HT.Paragraph('''Trait values for Shh range 5.1-fold: from a low of 8.2 (please round value) in 129S1/SvImJ to a high of 10.6 (please round value) in BXD9. The interquartile range (the difference between values closest to the 25% and 75% levels) is a more modest 1.8-fold. The mean value is XX. ''')
t1 = HT.Paragraph('''Table 1. Summary of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
f1 = HT.Paragraph('''Figure 1. ''')
f1.append(HT.Href(text="Box plot", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs14"))
f1.append(HT.Text(''' of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set'''))
f2A = HT.Paragraph('''Figure 2A: Bar chart of Shh data ordered by case from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
f2B = HT.Paragraph('''Figure 2B: Bar chart of Shh values ordered by from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
TD_LR.append(HT.Blockquote(title, HT.P(), header, hr, p1, HT.P(), p2, HT.P(), supertable2, t1, f1, HT.P(), img0, f2A, HT.P(), img1, f2B))
self.dict['body'] = str(TD_LR)
else:
heading = "Basic Statistics"
detail = ['Fewer than %d case data were entered for %s data set. No statitical analysis has been attempted.' % (self.plotMinInformative, fd.RISet)]
self.error(heading=heading,detail=detail)
return
else:
heading = "Basic Statistics"
detail = ['Empty data set, please check your data.']
self.error(heading=heading,detail=detail)
return
