def create_diploid_dataset(call_method_id=76,
file_name='/tmp/test.csv',
coding_type='normal'):
#Load parent list
parents = []
with open(env.env['data_dir'] + 'heterozygous_genotypes.csv') as f:
f.next()
for l in f:
parents.append(map(str.strip, l.split(',')))
snpsd = dp.load_snps_call_method(call_method_id)
l = zip(snpsd.accessions, range(len(snpsd.accessions)))
l.sort()
l = map(list, zip(*l))
acc_list = l[0]
orders = l[1]
sds = []
for i, sd in enumerate(snpsd.snpsDataList):
snps = sp.array(sd.snps, dtype='int8')
snps_list = []
p_list = []
for ps in parents:
f_id = bisect.bisect(acc_list, ps[0]) - 1
m_id = bisect.bisect(acc_list, ps[1]) - 1
if acc_list[f_id] == ps[0] and acc_list[m_id] == ps[1]:
f_gt = snps[:, orders[f_id]].flatten()
m_gt = snps[:, orders[m_id]].flatten()
if coding_type == 'normal':
o_gt = f_gt + m_gt
elif coding_type == 'dominant':
o_gt = sp.bitwise_xor(f_gt, m_gt)
snps_list.append(o_gt)
p_list.append('%s_%s' % (ps[0], ps[1]))
snps_list = sp.transpose(sp.array(snps_list, dtype='int8'))
snps = []
for s in snps_list:
snps.append(s)
sds.append(
snpsdata.SNPsData(snps,
sd.positions,
accessions=p_list,
chromosome=i + 1))
sd = snpsdata.SNPsDataSet(sds, [1, 2, 3, 4, 5])
sd.writeToFile(file_name)
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = [
"rFile=",
"chr=",
"delim=",
"missingval=",
"BoundaryStart=",
"removeOutliers=",
"addConstant=",
"logTransform",
"BoundaryEnd=",
"phenotypeFileType=",
"help",
"parallel=",
"parallelAll",
"LRT",
"minMAF=",
"kinshipDatafile=",
"phenotypeRanks",
"onlyMissing",
"onlyOriginal96",
"onlyOriginal192",
"onlyBelowLatidue=",
"complement",
"negate",
"srInput=",
"sr",
"srOutput=",
"srPar=",
"srSkipFirstRun",
"testRobustness",
"permutationFilter=",
"useLinearRegress",
"regressionCofactors=",
"FriLerAsCofactor",
"FriColAsCofactor",
"memReq=",
"walltimeReq=",
]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:c:d:m:h",
long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
phenotypeRanks = False
removeOutliers = None
addConstant = -1
phenotypeFileType = 1
rFile = None
delim = ","
missingVal = "NA"
help = 0
minMAF = 0.0
boundaries = [-1, -1]
chr = None
parallel = None
logTransform = False
negate = False
parallelAll = False
lrt = False
kinshipDatafile = None
onlyMissing = False
onlyOriginal96 = False
onlyOriginal192 = False
onlyBelowLatidue = None
complement = False
sr = False
srOutput = False
srInput = False
srSkipFirstRun = False
srTopQuantile = 0.95
srWindowSize = 30000
testRobustness = False
permutationFilter = 0.002
useLinearRegress = False
regressionCofactors = None
FriLerAsCofactor = False
FriColAsCofactor = False
memReq = "5g"
walltimeReq = "150:00:00"
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-o", "--rFile"):
rFile = arg
elif opt in ("--phenotypeFileType"):
phenotypeFileType = int(arg)
elif opt in ("--BoundaryStart"):
boundaries[0] = int(arg)
elif opt in ("--BoundaryEnd"):
boundaries[1] = int(arg)
elif opt in ("--addConstant"):
addConstant = float(arg)
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--minMAF"):
minMAF = float(arg)
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("--onlyMissing"):
onlyMissing = True
elif opt in ("--onlyOriginal96"):
onlyOriginal96 = True
elif opt in ("--onlyOriginal192"):
onlyOriginal192 = True
elif opt in ("--onlyBelowLatidue"):
onlyBelowLatidue = float(arg)
elif opt in ("--complement"):
complement = True
elif opt in ("--logTransform"):
logTransform = True
elif opt in ("--negate"):
negate = True
elif opt in ("--removeOutliers"):
removeOutliers = float(arg)
elif opt in ("--LRT"):
lrt = True
elif opt in ("-c", "--chr"):
chr = int(arg)
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
elif opt in ("--kinshipDatafile"):
kinshipDatafile = arg
elif opt in ("--phenotypeRanks"):
phenotypeRanks = True
elif opt in ("--sr"):
sr = True
elif opt in ("--srSkipFirstRun"):
srSkipFirstRun = True
elif opt in ("--srInput"):
srInput = arg
elif opt in ("--srOutput"):
srOutput = arg
elif opt in ("--srPar"):
vals = arg.split(",")
srTopQuantile = float(vals[0])
srWindowSize = int(vals[1])
elif opt in ("--testRobustness"):
testRobustness = True
elif opt in ("--permutationFilter"):
permutationFilter = float(arg)
elif opt in ("--FriLerAsCofactor"):
FriLerAsCofactor = True
elif opt in ("--FriColAsCofactor"):
FriColAsCofactor = True
elif opt in ("--useLinearRegress"):
useLinearRegress = True
elif opt in ("--regressionCofactors"):
regressionCofactors = arg
elif opt in ("--memReq"):
memReq = arg
elif opt in ("--walltimeReq"):
walltimeReq = arg
else:
if help == 0:
print "Unkown option!!\n"
print __doc__
sys.exit(2)
if len(args) < 3 and not parallel:
if help == 0:
print "Arguments are missing!!\n"
print __doc__
sys.exit(2)
print "Emma is being set up with the following parameters:"
print "output:", rFile
print "phenotypeRanks:", phenotypeRanks
print "phenotypeFileType:", phenotypeFileType
print "parallel:", parallel
print "parallelAll:", parallelAll
print "minMAF:", minMAF
print "LRT:", lrt
print "delim:", delim
print "missingval:", missingVal
print "kinshipDatafile:", kinshipDatafile
print "chr:", chr
print "boundaries:", boundaries
print "onlyMissing:", onlyMissing
print "onlyOriginal96:", onlyOriginal96
print "onlyOriginal192:", onlyOriginal192
print "onlyBelowLatidue:", onlyBelowLatidue
print "complement:", complement
print "negate:", negate
print "logTransform:", logTransform
print "addConstant:", addConstant
print "removeOutliers:", removeOutliers
print "sr:", sr
print "srSkipFirstRun:", srSkipFirstRun
print "srInput:", srInput
print "srOutput:", srOutput
print "srTopQuantile:", srTopQuantile
print "srWindowSize:", srWindowSize
print "testRobustness:", testRobustness
print "permutationFilter:", permutationFilter
print "useLinearRegress:", useLinearRegress
print "regressionCofactors:", regressionCofactors
print "FriLerAsCofactor:", FriLerAsCofactor
print "FriColAsCofactor:", FriColAsCofactor
print "walltimeReq:", walltimeReq
print "memReq:", memReq
def runParallel(phenotypeIndex, phed):
#Cluster specific parameters
print phenotypeIndex
phenName = phed.getPhenotypeName(phenotypeIndex)
outFileName = resultDir + "Emma_" + parallel + "_" + phenName
shstr = "#!/bin/csh\n"
shstr += "#PBS -l walltime=" + walltimeReq + "\n"
shstr += "#PBS -l mem=" + memReq + "\n"
shstr += "#PBS -q cmb\n"
shstr += "#PBS -N E" + phenName + "_" + parallel + "\n"
shstr += "set phenotypeName=" + parallel + "\n"
shstr += "set phenotype=" + str(phenotypeIndex) + "\n"
if useLinearRegress:
outFileName = resultDir + "LR_" + parallel + "_" + phenName
shstr += "(python " + emmadir + "Emma.py -o " + outFileName + " "
if useLinearRegress:
shstr += " --useLinearRegress "
if regressionCofactors:
shstr += " --regressionCofactors=" + str(regressionCofactors) + " "
if FriLerAsCofactor:
shstr += " --FriLerAsCofactor "
if FriColAsCofactor:
shstr += " --FriColAsCofactor "
if onlyOriginal96:
shstr += " --onlyOriginal96 "
elif onlyOriginal192:
shstr += " --onlyOriginal192 "
if onlyBelowLatidue:
shstr += " --onlyBelowLatidue=" + str(onlyBelowLatidue) + " "
if logTransform:
shstr += " --logTransform "
if negate:
shstr += " --negate "
if removeOutliers:
shstr += " --removeOutliers=" + str(removeOutliers) + " "
if phenotypeRanks:
shstr += " --phenotypeRanks "
if testRobustness:
shstr += " --testRobustness "
shstr += " --permutationFilter=" + str(permutationFilter) + " "
if sr:
shstr += " --sr "
if not srOutput:
output = resultDir + "Emma_" + parallel + "_" + phenName + ".sr.pvals"
shstr += " --srOutput=" + str(output) + " "
if srSkipFirstRun:
if not srInput:
output = resultDir + "Emma_" + parallel + "_" + phenName + ".pvals"
shstr += " --srInput=" + str(output) + " "
shstr += " --srSkipFirstRun "
shstr += " --srPar=" + str(srTopQuantile) + "," + str(
srWindowSize) + " "
if kinshipDatafile:
shstr += " --kinshipDatafile=" + str(kinshipDatafile) + " "
shstr += " --addConstant=" + str(addConstant) + " "
shstr += snpsDataFile + " " + phenotypeDataFile + " " + str(
phenotypeIndex) + " "
shstr += "> " + outFileName + "_job" + ".out) >& " + outFileName + "_job" + ".err\n"
f = open(parallel + ".sh", 'w')
f.write(shstr)
f.close()
#Execute qsub script
os.system("qsub " + parallel + ".sh ")
snpsDataFile = args[0]
phenotypeDataFile = args[1]
if parallel: #Running on the cluster..
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
if parallelAll:
for phenotypeIndex in phed.phenIds:
if onlyMissing:
phenName = phed.getPhenotypeName(phenotypeIndex)
pvalFile = resultDir + "Emma_" + parallel + "_" + phenName + ".pvals"
res = None
try:
res = os.stat(pvalFile)
except Exception:
print "File", pvalFile, "does not exist."
if res and res.st_size > 0:
print "File", pvalFile, "already exists, and is non-empty."
if sr:
srInput = resultDir + "Emma_" + parallel + "_" + phenName + ".sr.pvals"
srRes = None
try:
srRes = os.stat(srInput)
except Exception:
print "File", srInput, "does not exist."
if srRes and srRes.st_size > 0:
print "File", srInput, "already exists, and is non-empty."
else:
runParallel(phenotypeIndex, phed)
else:
print "Setting up the run."
runParallel(phenotypeIndex, phed)
else:
runParallel(phenotypeIndex, phed)
else:
phenotypeIndex = int(args[2])
runParallel(phenotypeIndex, phed)
return
else:
phenotypeIndex = int(args[2])
print "phenotypeIndex:", phenotypeIndex
print "\nStarting program now!\n"
snpsds = dataParsers.parseCSVData(snpsDataFile,
format=1,
deliminator=delim,
missingVal=missingVal)
#Load phenotype file
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile,
delimiter='\t') #Get Phenotype data
numAcc = len(snpsds[0].accessions)
#Removing outliers
if removeOutliers:
print "Remoing outliers"
phed.naOutliers(phenotypeIndex, removeOutliers)
#If onlyOriginal96, then remove all other phenotypes..
if onlyOriginal96:
print "Filtering for the first 96 accessions"
original_96_ecotypes = phenotypeData._getFirst96Ecotypes_()
original_96_ecotypes = map(str, original_96_ecotypes)
keepEcotypes = []
if complement:
for acc in phed.accessions:
if not acc in original_96_ecotypes:
keepEcotypes.append(acc)
else:
keepEcotypes = original_96_ecotypes
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
if onlyOriginal192:
print "Filtering for the first 192 accessions"
original_192_ecotypes = phenotypeData._getFirst192Ecotypes_()
original_192_ecotypes = map(str, original_192_ecotypes)
keepEcotypes = []
if complement:
for acc in phed.accessions:
if not acc in original_192_ecotypes:
keepEcotypes.append(acc)
else:
keepEcotypes = original_192_ecotypes
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
if onlyBelowLatidue:
print "Filtering for the accessions which orginate below latitude", onlyBelowLatidue
eiDict = phenotypeData._getEcotypeIdInfoDict_()
print eiDict
keepEcotypes = []
for acc in phed.accessions:
acc = int(acc)
if eiDict.has_key(acc) and eiDict[acc][
2] and eiDict[acc][2] < onlyBelowLatidue:
keepEcotypes.append(str(acc))
elif eiDict.has_key(acc) and eiDict[acc][2] == None:
keepEcotypes.append(str(acc))
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
sys.stdout.write("Finished prefiltering phenotype accessions.\n")
sys.stdout.flush()
phenotype = phed.getPhenIndex(phenotypeIndex)
accIndicesToKeep = []
phenAccIndicesToKeep = []
#Checking which accessions to keep and which to remove .
for i in range(0, len(snpsds[0].accessions)):
acc1 = snpsds[0].accessions[i]
for j in range(0, len(phed.accessions)):
acc2 = phed.accessions[j]
if acc1 == acc2 and phed.phenotypeValues[j][phenotype] != 'NA':
accIndicesToKeep.append(i)
phenAccIndicesToKeep.append(j)
break
print "\nFiltering accessions in genotype data:"
#Filter accessions which do not have the phenotype value (from the genotype data).
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.removeAccessionIndices(accIndicesToKeep)
print ""
print numAcc - len(
accIndicesToKeep
), "accessions removed from genotype data, leaving", len(
accIndicesToKeep), "accessions in all."
print "\nNow filtering accessions in phenotype data:"
phed.removeAccessions(
phenAccIndicesToKeep
) #Removing accessions that don't have genotypes or phenotype values
print "Verifying number of accessions: len(phed.accessions)==len(snpsds[0].accessions) is", len(
phed.accessions) == len(snpsds[0].accessions)
if len(phed.accessions) != len(snpsds[0].accessions):
raise Exception
#Filtering monomorphic
print "Filtering monomorphic SNPs"
for snpsd in snpsds:
print "Removed", str(snpsd.filterMonoMorphicSnps()), "Snps"
#Remove minor allele frequencies
if minMAF != 0:
sys.stdout.write("Filterting SNPs with MAF<" + str(minMAF) + ".")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.filterMinMAF(minMAF)
#Removing SNPs which are outside of boundaries.
if chr:
print "\nRemoving SNPs which are outside of boundaries."
snpsds[chr - 1].filterRegion(boundaries[0], boundaries[1])
snpsds = [snpsds[chr - 1]]
#Ordering accessions in genotype data to fit phenotype data.
print "Ordering genotype data accessions."
accessionMapping = []
i = 0
for acc in phed.accessions:
if acc in snpsds[0].accessions:
accessionMapping.append((snpsds[0].accessions.index(acc), i))
i += 1
#print zip(accessionMapping,snpsds[0].accessions)
print "len(snpsds[0].snps)", len(snpsds[0].snps)
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.orderAccessions(accessionMapping)
print "\nGenotype data has been ordered."
#Converting format to 01
newSnpsds = []
sys.stdout.write("Converting data format")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
newSnpsds.append(snpsd.getSnpsData(missingVal=missingVal))
print ""
print "Checking kinshipfile:", kinshipDatafile
if kinshipDatafile: #Is there a special kinship file?
kinshipSnpsds = dataParsers.parseCSVData(kinshipDatafile,
format=1,
deliminator=delim,
missingVal=missingVal)
accIndicesToKeep = []
#Checking which accessions to keep and which to remove (genotype data).
sys.stdout.write(
"Removing accessions which do not have a phenotype value for " +
phed.phenotypeNames[phenotype] + ".")
sys.stdout.flush()
for i in range(0, len(kinshipSnpsds[0].accessions)):
acc1 = kinshipSnpsds[0].accessions[i]
for j in range(0, len(phed.accessions)):
acc2 = phed.accessions[j]
if acc1 == acc2 and phed.phenotypeValues[j][phenotype] != 'NA':
accIndicesToKeep.append(i)
break
print accIndicesToKeep
for snpsd in kinshipSnpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.removeAccessionIndices(accIndicesToKeep)
print ""
print numAcc - len(
accIndicesToKeep
), "accessions removed from kinship genotype data, leaving", len(
accIndicesToKeep), "accessions in all."
print "Ordering kinship data accessions."
accessionMapping = []
i = 0
for acc in snpsds[0].accessions:
if acc in kinshipSnpsds[0].accessions:
accessionMapping.append(
(kinshipSnpsds[0].accessions.index(acc), i))
i += 1
print zip(accessionMapping, snpsds[0].accessions)
print "len(snpsds[0].snps)", len(snpsds[0].snps)
for snpsd in kinshipSnpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.orderAccessions(accessionMapping)
print "Kinship genotype data has been ordered."
newKinshipSnpsds = []
sys.stdout.write("Converting data format")
for snpsd in kinshipSnpsds:
sys.stdout.write(".")
sys.stdout.flush()
newKinshipSnpsds.append(snpsd.getSnpsData(
missingVal=missingVal)) #This data might have NAs
print ""
kinshipSnpsds = newKinshipSnpsds
else:
kinshipSnpsds = newSnpsds
print "Found kinship data."
#Ordering accessions according to the order of accessions in the genotype file
# accessionMapping = []
# i = 0
# for acc in snpsds[0].accessions:
# if acc in phed.accessions:
# accessionMapping.append((phed.accessions.index(acc),i))
# i += 1
# phed.orderAccessions(accessionMapping)
#Negating phenotypic values
if negate:
phed.negateValues(phenotypeIndex)
if logTransform and not phed.isBinary(
phenotypeIndex) and phed.getMinValue(phenotypeIndex) <= 0:
addConstant = 0
#Adding a constant.
if addConstant != -1:
if addConstant == 0:
addConstant = math.sqrt(phed.getVariance(phenotypeIndex)) / 10
addConstant = addConstant - phed.getMinValue(phenotypeIndex)
print "Adding a constant to phenotype:", addConstant
phed.addConstant(phenotypeIndex, addConstant)
#Log-transforming
if logTransform:
print "Log transforming phenotype"
phed.logTransform(phenotypeIndex)
#Converting phenotypes to Ranks
elif phenotypeRanks:
phed.transformToRanks(phenotypeIndex)
if not chr:
snpsDataset = snpsdata.SNPsDataSet(newSnpsds, [1, 2, 3, 4, 5])
kinshipSnpsDataset = snpsdata.SNPsDataSet(kinshipSnpsds,
[1, 2, 3, 4, 5])
else:
snpsDataset = snpsdata.SNPsDataSet(newSnpsds, [chr])
kinshipSnpsDataset = snpsdata.SNPsDataSet(kinshipSnpsds, [chr])
phenotypeName = phed.getPhenotypeName(phenotypeIndex)
sys.stdout.flush()
if testRobustness:
print "Starting a robustness test"
allSNPs = []
for snpsd in snpsDataset.snpsDataList:
allSNPs += snpsd.snps
phenVals = phed.getPhenVals(phenotypeIndex)
_robustness_test_(allSNPs, phenVals, rFile, filter=permutationFilter)
sys.exit(0)
if useLinearRegress:
phenVals = phed.getPhenVals(phenotypeIndex)
d0 = {}
d0["phen"] = phenVals
dh = {}
dh["phen"] = phenVals
import rpy, gc
if regressionCofactors: #Adds ler and col as cofactors
import pickle
f = open(regressionCofactors, "r")
co_factors = pickle.load(f)
f.close()
#inserting co factors into model
for factor in co_factors:
d[factor] = co_factors[factor]
import analyzeHaplotype as ah
(ler_factor, col_factor) = ah.getLerAndColAccessions(newSnpsds, True)
if FriColAsCofactor:
d0["col"] = col_factor
dh["col"] = col_factor
if FriLerAsCofactor:
d0["ler"] = ler_factor
dh["ler"] = ler_factor
chr_pos_pvals = []
stats = []
sys.stdout.write("Applying the linear model")
sys.stdout.flush()
for i in range(0, len(newSnpsds)): #[3]:#
snpsd = newSnpsds[i]
sys.stdout.write("|")
sys.stdout.flush()
gc.collect(
) #Calling garbage collector, in an attempt to clean up memory..
for j in range(0, len(snpsd.snps)):
if j % 5000 == 0:
sys.stdout.write(".")
sys.stdout.flush()
#if snpsd.positions[j]>1700000:
# break
snp = snpsd.snps[j]
d0["snp"] = snp
try:
rpy.set_default_mode(rpy.NO_CONVERSION)
aov0 = rpy.r.aov(r("phen ~ ."), data=d0)
aovh = rpy.r.aov(r("phen ~ ."), data=dh)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
s0 = rpy.r.summary(aov0)
sh = rpy.r.summary(aovh)
#print s0,sh
rss_0 = s0['Sum Sq'][-1]
if type(sh['Sum Sq']) != float:
rss_h = sh['Sum Sq'][-1]
else:
rss_h = sh['Sum Sq']
f = (rss_h - rss_0) / (rss_0 /
(len(phenVals) - len(d0) + 1))
pval = rpy.r.pf(f, 1, len(phenVals), lower_tail=False)
except Exception, err_str:
print "Calculating p-value failed" #,err_str
pval = 1.0
#print "dh:",dh
#print "d0:",d0
#print "rss_h,rss_0:",rss_h,rss_0
#print "f,p:",f,pval
chr_pos_pvals.append([i + 1, snpsd.positions[j], pval])
mafc = min(snp.count(snp[0]), len(snp) - snp.count(snp[0]))
maf = mafc / float(len(snp))
stats.append([maf, mafc])
sys.stdout.write("\n")
#Write out to a result file
sys.stdout.write("Writing results to file\n")
sys.stdout.flush()
pvalFile = rFile + ".pvals"
f = open(pvalFile, "w")
f.write("Chromosome,position,p-value,marf,maf\n")
for i in range(0, len(chr_pos_pvals)):
chr_pos_pval = chr_pos_pvals[i]
stat = stats[i]
f.write(
str(chr_pos_pval[0]) + "," + str(chr_pos_pval[1]) + "," +
str(chr_pos_pval[2]) + "," + str(stat[0]) + "," +
str(stat[1]) + "\n")
f.close()
#Plot results
print "Generating a GW plot."
phenotypeName = phed.getPhenotypeName(phenotypeIndex)
res = gwaResults.Result(pvalFile,
name="LM_" + phenotypeName,
phenotypeID=phenotypeIndex)
res.negLogTransform()
pngFile = pvalFile + ".png"
plotResults.plotResult(res,
pngFile=pngFile,
percentile=90,
type="pvals",
ylab="$-$log$_{10}(p)$",
plotBonferroni=True,
usePylab=False)
def _run_():
if len(sys.argv)==1:
print __doc__
sys.exit(2)
long_options_list=["outputFile=", "delim=", "missingval=", "phenotypeFileType=",
"help", "parallel=", "parallelAll", "addToDB",
"callMethodID=", "comment=", "onlyOriginal192","onlyOriginal96", "subSample=" ,
"subSampleLikePhenotype=", "subsampleTest=", "complement", "onlyBelowLatidue=",
"onlyAboveLatidue=", "srInput=", "sr","srOutput=", "srPar=","srSkipFirstRun",
"permTest=", "savePermutations", "permutationFilter=", "testRobustness",
"memReq=","walltimeReq=",]
try:
opts, args=getopt.getopt(sys.argv[1:], "o:c:d:m:h", long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
phenotypeFileType=1
outputFile=None
delim=","
missingVal="NA"
help=0
parallel=None
parallelAll=False
addToDB=False
callMethodID=None
comment=""
subSample=None
onlyOriginal96=False
onlyOriginal192 = False
subSampleLikePhenotype = None
subsampleTest = False
numSubSamples = None
complement = False
onlyBelowLatidue = None
onlyAboveLatidue = None
sr = False
srOutput = False
srInput = False
srSkipFirstRun = False
srTopQuantile = 0.95
srWindowSize = 30000
permTest = None
savePermutations = False
permutationFilter = 1.0
testRobustness = False
memReq = "5g"
walltimeReq = "100:00:00"
for opt, arg in opts:
if opt in ("-h", "--help"):
help=1
print __doc__
elif opt in ("-o", "--outputFile"):
outputFile=arg
elif opt in ("--phenotypeFileType"):
phenotypeFileType=int(arg)
elif opt in ("--parallel"):
parallel=arg
elif opt in ("--parallelAll"):
parallelAll=True
elif opt in ("--addToDB"):
addToDB=True
elif opt in ("--onlyOriginal96"):
onlyOriginal96=True
elif opt in ("--onlyOriginal192"):
onlyOriginal192=True
elif opt in ("--complement"):
complement=True
elif opt in ("--subSample"):
subSample=int(arg)
elif opt in ("--subsampleTest"):
subsampleTest = True
l = arg.split(",")
subSample=int(l[0])
numSubSamples=int(l[1])
elif opt in ("--onlyBelowLatidue"):
onlyBelowLatidue=float(arg)
elif opt in ("--onlyAboveLatidue"):
onlyAboveLatidue=float(arg)
elif opt in ("--subSampleLikePhenotype"):
subSampleLikePhenotype=int(arg)
elif opt in ("--callMethodID"):
callMethodID=int(arg)
elif opt in ("--comment"):
comment=arg
elif opt in ("-d", "--delim"):
delim=arg
elif opt in ("-m", "--missingval"):
missingVal=arg
elif opt in ("--sr"):
sr = True
elif opt in ("--testRobustness"):
testRobustness = True
elif opt in ("--permTest"):
permTest = int(arg)
elif opt in ("--savePermutations"):
savePermutations = True
elif opt in ("--permutationFilter"):
permutationFilter = float(arg)
elif opt in ("--srSkipFirstRun"):
srSkipFirstRun = True
elif opt in ("--srInput"):
srInput = arg
elif opt in ("--srOutput"):
srOutput = arg
elif opt in ("--srPar"):
vals = arg.split(",")
srTopQuantile = float(vals[0])
srWindowSize = int(vals[1])
elif opt in ("--memReq"):
memReq=arg
elif opt in ("--walltimeReq"):
walltimeReq=arg
else:
if help==0:
print "Unkown option!!\n"
print __doc__
sys.exit(2)
if len(args)<3 and not parallel:
if help==0:
print "Arguments are missing!!\n"
print __doc__
sys.exit(2)
snpsDataFile=args[0]
phenotypeDataFile=args[1]
print "Kruskal-Wallis is being set up with the following parameters:"
print "phenotypeDataFile:",phenotypeDataFile
print "snpsDataFile:",snpsDataFile
print "parallel:",parallel
print "parallelAll:",parallelAll
print "onlyOriginal96:",onlyOriginal96
print "onlyOriginal192:",onlyOriginal192
print "onlyBelowLatidue:",onlyBelowLatidue
print "onlyAboveLatidue:",onlyAboveLatidue
print "complement:",complement
print "subSampleLikePhenotype:",subSampleLikePhenotype
print "subsampleTest:",subsampleTest
print "numSubSamples:",numSubSamples
print "subSample:",subSample
print "sr:",sr
print "srSkipFirstRun:",srSkipFirstRun
print "srInput:",srInput
print "srOutput:",srOutput
print "srTopQuantile:",srTopQuantile
print "srWindowSize:",srWindowSize
print "permTest:",permTest
print "savePermutations:",savePermutations
print "permutationFilter:",permutationFilter
print "testRobustness:",testRobustness
print "walltimeReq:",walltimeReq
print "memReq:",memReq
def runParallel(phenotypeIndex,id=""):
#Cluster specific parameters
phed=phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
phenName=phed.getPhenotypeName(phenotypeIndex)
print phenName
outputFile=resultDir+"KW_"+parallel+"_"+phenName+id
shstr = "#!/bin/csh\n"
shstr += "#PBS -l walltime="+walltimeReq+"\n"
shstr += "#PBS -l mem="+memReq+"\n"
shstr +="#PBS -q cmb\n"
shstr+="#PBS -N K"+phenName+"_"+parallel+"\n"
shstr+="set phenotypeName="+parallel+"\n"
shstr+="set phenotype="+str(phenotypeIndex)+"\n"
shstr+="(python "+scriptDir+"KW.py -o "+outputFile+" "
if subSample:
shstr+=" --subSample="+str(subSample)+" "
elif onlyOriginal96:
shstr+=" --onlyOriginal96 "
elif onlyOriginal192:
shstr+=" --onlyOriginal192 "
if onlyBelowLatidue:
shstr+=" --onlyBelowLatidue="+str(onlyBelowLatidue)+" "
elif onlyAboveLatidue:
shstr+=" --onlyAboveLatidue="+str(onlyAboveLatidue)+" "
if complement:
shstr+=" --complement "
if permTest:
shstr+=" --permTest="+str(permTest)+" "
if savePermutations:
shstr+=" --savePermutations "
shstr+=" --permutationFilter="+str(permutationFilter)+" "
if testRobustness:
shstr+=" --testRobustness "
if sr:
shstr += " --sr "
if not srOutput:
output = resultDir+"KW_"+parallel+"_"+phenName+".sr.pvals"
shstr += " --srOutput="+str(output)+" "
if srSkipFirstRun:
if not srInput:
output = resultDir+"KW_"+parallel+"_"+phenName+".pvals"
shstr += " --srInput="+str(output)+" "
shstr += " --srSkipFirstRun "
shstr += " --srPar="+str(srTopQuantile)+","+str(srWindowSize)+" "
shstr+=snpsDataFile+" "+phenotypeDataFile+" "+str(phenotypeIndex)+" "
shstr+="> "+outputFile+"_job"+".out) >& "+outputFile+"_job"+".err\n"
f=open(parallel+".sh", 'w')
f.write(shstr)
f.close()
#Execute qsub script
os.system("qsub "+parallel+".sh ")
if parallel: #Running on the cluster..
if parallelAll:
phed=phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
for phenotypeIndex in phed.phenIds:
runParallel(phenotypeIndex)
elif subsampleTest:
phenotypeIndex=int(args[2])
for i in range(0,numSubSamples):
runParallel(phenotypeIndex,id="_r"+str(subSample)+"_"+str(i))
else:
phenotypeIndex=int(args[2])
runParallel(phenotypeIndex)
return
else:
phenotypeIndex=int(args[2])
print "phenotypeIndex:",phenotypeIndex
print "output:",outputFile
print "\nStarting program now!\n"
#Load phenotype file
phed=phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
#If onlyOriginal96, then remove all other phenotypes..
if onlyOriginal96:
print "Filtering for the first 96 accessions"
original_96_ecotypes = phenotypeData._getFirst96Ecotypes_()
original_96_ecotypes = map(str,original_96_ecotypes)
keepEcotypes = []
if complement:
for acc in phed.accessions:
if not acc in original_96_ecotypes:
keepEcotypes.append(acc)
else:
keepEcotypes = original_96_ecotypes
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
if onlyOriginal192:
print "Filtering for the first 192 accessions"
original_192_ecotypes = phenotypeData._getFirst192Ecotypes_()
original_192_ecotypes = map(str,original_192_ecotypes)
keepEcotypes = []
if complement:
for acc in phed.accessions:
if not acc in original_192_ecotypes:
keepEcotypes.append(acc)
else:
keepEcotypes = original_192_ecotypes
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
if onlyBelowLatidue:
print "Filtering for the accessions which orginate below latitude",onlyBelowLatidue
eiDict = phenotypeData._getEcotypeIdInfoDict_()
print eiDict
keepEcotypes = []
for acc in phed.accessions:
acc = int(acc)
if eiDict.has_key(acc) and eiDict[acc][2] and eiDict[acc][2]<onlyBelowLatidue:
keepEcotypes.append(str(acc))
elif eiDict.has_key(acc) and eiDict[acc][2]==None:
keepEcotypes.append(str(acc))
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
elif onlyAboveLatidue:
print "Filtering for the accessions which orginate above latitude",onlyAboveLatidue
eiDict = phenotypeData._getEcotypeIdInfoDict_()
print eiDict
keepEcotypes = []
for acc in phed.accessions:
acc = int(acc)
if eiDict.has_key(acc) and eiDict[acc][2] and eiDict[acc][2]>onlyAboveLatidue:
keepEcotypes.append(str(acc))
elif eiDict.has_key(acc) and eiDict[acc][2]==None:
keepEcotypes.append(str(acc))
phed.filterAccessions(keepEcotypes)
print "len(phed.accessions)", len(phed.accessions)
if subSampleLikePhenotype:
p_name = phed.getPhenotypeName(subSampleLikePhenotype)
print "Picking sample as in",p_name
ecotypes = phed.getNonNAEcotypes(subSampleLikePhenotype)
print ecotypes
phed.filterAccessions(ecotypes)
print "len(phed.accessions)", len(phed.accessions)
if subSample:
sample_ecotypes = []
ecotypes = phed.getNonNAEcotypes(phenotypeIndex)
sample_ecotypes = random.sample(ecotypes,subSample)
phed.filterAccessions(sample_ecotypes)
print "len(phed.accessions)", len(phed.accessions)
sys.stdout.write("Finished prefiltering phenotype accessions.\n")
sys.stdout.flush()
#Load genotype file
snpsds=dataParsers.parseCSVData(snpsDataFile, format = 1, deliminator = delim, missingVal = missingVal)
#Checking overlap between phenotype and genotype accessions.
phenotype=phed.getPhenIndex(phenotypeIndex)
accIndicesToKeep=[]
phenAccIndicesToKeep=[]
numAcc=len(snpsds[0].accessions)
sys.stdout.write("Removing accessions which do not have a phenotype value for "+phed.phenotypeNames[phenotype]+".")
sys.stdout.flush()
for i in range(0, len(snpsds[0].accessions)):
acc1=snpsds[0].accessions[i]
for j in range(0, len(phed.accessions)):
acc2=phed.accessions[j]
if acc1==acc2 and phed.phenotypeValues[j][phenotype]!='NA':
accIndicesToKeep.append(i)
phenAccIndicesToKeep.append(j)
break
#Filter accessions which do not have the phenotype value.
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
snpsd.removeAccessionIndices(accIndicesToKeep)
print ""
print numAcc-len(accIndicesToKeep), "accessions removed, leaving", len(accIndicesToKeep), "accessions in all."
print "Filtering phenotype data."
phed.removeAccessions(phenAccIndicesToKeep) #Removing accessions that don't have genotypes or phenotype values
#Ordering accessions according to the order of accessions in the genotype file
accessionMapping=[]
i=0
for acc in snpsds[0].accessions:
if acc in phed.accessions:
accessionMapping.append((phed.accessions.index(acc), i))
i+=1
phed.orderAccessions(accessionMapping)
#Filtering monomorphic
print "Filtering monomorphic SNPs"
for snpsd in snpsds:
print "Removed", str(snpsd.filterMonoMorphicSnps()), "Snps"
#Converting format to 01
newSnpsds=[]
sys.stdout.write("Converting data format")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
newSnpsds.append(snpsd.getSnpsData())
print ""
#Double check genotype file:
problems = 0
for i in range(0,len(newSnpsds)):
snpsd = newSnpsds[i]
for j in range(0,len(snpsd.snps)):
snp = snpsd.snps[j]
sc = snp.count(0)
if sc==0 or sc==len(snp):
print "Problem in file found at chr,pos",(i+1),",",snpsd.positions[i]
problems += 1
if problems >0:
print "Genotype file appears to have potential problems"
else:
print "Genotype file appears to be good"
if permTest:
print "Starting a permutation test"
allSNPs = []
for snpsd in newSnpsds:
allSNPs += snpsd.snps
phenVals = phed.getPhenVals(phenotypeIndex)
test_type = "KW"
if phed.isBinary(phenotypeIndex):
test_type = "Fisher"
permTest = 100
_perm_test_(allSNPs,phenVals,permTest,outputFile, test_type=test_type,savePermutations=savePermutations, filter=permutationFilter)
sys.exit(0)
if testRobustness:
print "Starting a robustness test"
allSNPs = []
for snpsd in newSnpsds:
allSNPs += snpsd.snps
phenVals = phed.getPhenVals(phenotypeIndex)
test_type = "KW"
if phed.isBinary(phenotypeIndex):
test_type = "Fisher"
_robustness_test_(allSNPs,phenVals,outputFile, test_type=test_type, filter=permutationFilter)
sys.exit(0)
sys.stdout.flush()
print "sr:",sr, ", srSkipFirstRun:",srSkipFirstRun
if (not sr) or (sr and not srSkipFirstRun):
#Writing files
#phed and phenotype
sd=snpsdata.SNPsDataSet(newSnpsds, [1, 2, 3, 4, 5])
phenotypeName=phed.getPhenotypeName(phenotypeIndex)
if phed.isBinary(phenotypeIndex):
pvals = run_fet(sd.getSnps(),phed.getPhenVals(phenotypeIndex))
else:
snps = sd.getSnps()
phen_vals = phed.getPhenVals(phenotypeIndex)
try:
kw_res = util.kruskal_wallis(snps,phen_vals)
pvals = kw_res['ps']
except:
print snps
print phen_vals
print len(snps),len(snps[0]),len(phen_vals)
raise Exception
res = gwaResults.Result(scores = pvals,name="KW_"+phenotypeName, snpsds=newSnpsds, load_snps=False)
pvalFile=outputFile+".pvals"
res.writeToFile(pvalFile)
print "Generating a GW plot."
res.negLogTransform()
pngFile = pvalFile+".png"
plotResults.plotResult(res,pngFile=pngFile,percentile=90,type="pvals",ylab="$-$log$_{10}(p)$", plotBonferroni=True,usePylab=False)
srInput = pvalFile
else:
print "Skipping first stage analysis."
sys.stdout.flush()
if sr:
_secondRun_(srOutput,srInput,srTopQuantile,srWindowSize,newSnpsds,phed,phenotypeIndex,binary=binary)
print "Generating second run GW plot."
res = gwaResults.Result(srInput,name="KW_"+phenotypeName, phenotypeID=phenotypeIndex)
res.negLogTransform()
srRes = gwaResults.Result(srOutput,name="KW_SR_"+phenotypeName, phenotypeID=phenotypeIndex)
srRes.negLogTransform()
srPngFile = pvalFile+".sr.png"
plotResults.plotResultWithSecondRun(res,srRes,pngFile=srPngFile,ylab="$-$log$_{10}(p)$", plotBonferroni=True)
def map_phenotype(p_i, phed, mapping_method, trans_method, p_dict):
import copy
phed = copy.deepcopy(phed)
phenotype_name = phed.get_name(p_i)
phen_is_binary = phed.is_binary(p_i)
if trans_method == 'most_normal':
trans_method, shapiro_pval = phed.most_normal_transformation(p_i, perform_trans=False)
file_prefix = _get_file_prefix_(p_dict['run_id'], p_i, phed.get_name(p_i),
mapping_method, trans_method, p_dict['remove_outliers'], p_dict['with_replicates'],
p_dict['call_method_id'])
result_name = "%s_%s_%s" % (phenotype_name, mapping_method, trans_method)
emmax_perm_threshold = None
k = None
res = None
#Check whether result already exists.
if p_dict['use_existing_results']:
if p_dict['region_plots']:
sd = _get_genotype_data_(p_dict)
num_outliers = prepare_data(sd, phed, p_i, trans_method, p_dict['remove_outliers'], p_dict['with_replicates'])
if p_dict['remove_outliers']:
assert num_outliers != 0, "No outliers were removed, so it makes no sense to go on and perform GWA."
snps = sd.getSnps()
else:
snps = None
print "\nChecking for existing results."
result_file = file_prefix + ".pvals"
if os.path.isfile(result_file):
res = gwaResults.Result(result_file=result_file, name=result_name, snps=snps)
pvals = True
else:
result_file = file_prefix + ".scores"
if os.path.isfile(result_file):
res = gwaResults.Result(result_file=result_file, name=result_name, snps=snps)
pvals = False
if res:
print "Found existing results.. (%s)" % (result_file)
sys.stdout.flush()
#Loading candidate genes
cand_genes = None
if p_dict['cand_genes_file']:
cand_genes, tair_ids = gwaResults.load_cand_genes_file(p_dict['cand_genes_file'])
else:
cand_genes = None
tair_ids = None
if not res: #If results weren't found in a file... then do GWA.
#Loading data
sd = _get_genotype_data_(p_dict)
num_outliers, n_filtered_snps = prepare_data(sd, phed, p_i, trans_method, p_dict['remove_outliers'],
p_dict['with_replicates'])
#Do we need to calculate the K-matrix?
if mapping_method in ['emma', 'emmax', 'emmax_anova', 'emmax_step', 'loc_glob_mm']:
#Load genotype file (in binary format)
sys.stdout.write("Retrieving the Kinship matrix K.\n")
sys.stdout.flush()
if p_dict['kinship_file']: #Kinship file was supplied..
print 'Loading supplied kinship file: %s' % p_dict['kinship_file']
k = kinship.load_kinship_from_file(p_dict['kinship_file'], sd.accessions)
else:
print 'Loading kinship file.'
if p_dict['data_file'] != None:
if p_dict['kinship_type'] == 'ibs':
k = sd.get_ibs_kinship_matrix()
elif p_dict['kinship_type'] == 'ibd':
k = sd.get_ibd_kinship_matrix()
else:
k = kinship.get_kinship(call_method_id=p_dict['call_method_id'], data_format=p_dict['data_format'],
method=p_dict['kinship_type'], n_removed_snps=n_filtered_snps,
remain_accessions=sd.accessions)
sys.stdout.flush()
sys.stdout.write("Done!\n")
if p_dict['remove_outliers']:
if num_outliers == 0: print "No outliers were removed!"
phen_vals = phed.get_values(p_i)
if p_dict['local_gwas']: #Filter SNPs, etc..
sd = snpsdata.SNPsDataSet([sd.get_region_snpsd(*p_dict['local_gwas'])],
[p_dict['local_gwas'][0]], data_format=sd.data_format)
snps = sd.getSnps()
sys.stdout.write("Finished loading and handling data!\n")
print "Plotting a histogram"
p_her = None
hist_file_prefix = _get_file_prefix_(p_dict['run_id'], p_i, phenotype_name, trans_method,
p_dict['remove_outliers'], p_dict['with_replicates'],
p_dict['call_method_id'])
hist_png_file = hist_file_prefix + "_hist.png"
if k is not None:
p_her = phed.get_pseudo_heritability(p_i, k)['pseudo_heritability']
p_her_pval = phed.get_pseudo_heritability(p_i, k)['pval']
phed.plot_histogram(p_i, png_file=hist_png_file, p_her=p_her, p_her_pval=p_her_pval)
else:
phed.plot_histogram(p_i, png_file=hist_png_file)
print "Applying %s to data." % (mapping_method)
sys.stdout.flush()
kwargs = {}
additional_columns = []
if "kw" == mapping_method:
if phen_is_binary:
warnings.warn("Warning, applying KW to a binary phenotype")
kw_res = util.kruskal_wallis(snps, phen_vals)
pvals = kw_res['ps']
kwargs['statistics'] = kw_res['ds']
additional_columns.append('statistics')
elif "ft" == mapping_method:
raise NotImplementedError
# pvals, or_est = run_fet(snps, phen_vals)
# kwargs['odds_ratio_est'] = or_est
# additional_columns.append('odds_ratio_est')
else: #Parametric tests below:
if mapping_method in ['emma', 'emmax', 'emmax_perm', 'emmax_step', 'emmax_anova', 'loc_glob_mm']:
r = lm.mm_lrt_test(phen_vals, k)
if r['pval'] > 0.05:
print "Performing EMMA, even though a mixed model does not fit the data significantly better"
print 'p-value: %0.3f' % r['pval']
else:
print 'The mixed model fits the data significantly better than the simple linear model.'
print 'p-value: %f' % r['pval']
if mapping_method in ['loc_glob_mm']:
res_dict = lm.local_vs_global_mm_scan(phen_vals, sd, file_prefix=file_prefix,
global_k=k, window_size=p_dict['loc_glob_ws'],
jump_size=p_dict['loc_glob_ws'] / 2,
kinship_method=p_dict['kinship_type'])
res_file_name = file_prefix + '.csv'
_write_res_dict_to_file_(res_file_name, res_dict)
return
elif mapping_method in ['emma']:
res = lm.emma(snps, phen_vals, k)
elif mapping_method in ['emmax']:
if p_dict['emmax_perm']:
perm_sd = _get_genotype_data_(p_dict)
num_outliers = prepare_data(perm_sd, phed, p_i, 'none', 0, p_dict['with_replicates'])
perm_sd.filter_mac_snps(p_dict['mac_threshold'])
t_snps = perm_sd.getSnps()
t_phen_vals = phed.get_values(p_i)
res = lm.emmax_perm_test(t_snps, t_phen_vals, k, p_dict['emmax_perm'])
emmax_perm_threshold = res['threshold_05'][0]
import pylab
hist_res = pylab.hist(-sp.log10(res['min_ps']), alpha=0.6)
threshold = -sp.log10(emmax_perm_threshold)
b_threshold = -sp.log10(1.0 / (len(t_snps) * 20.0))
pylab.vlines(threshold, 0, max(hist_res[0]), color='g')
pylab.vlines(b_threshold, 0, max(hist_res[0]), color='r')
pylab.savefig(file_prefix + 'perm_%d_min_pval_hist.png' % (p_dict['emmax_perm']),
format='png')
if p_dict['with_replicates']:
#Get values, with ecotypes, construct Z and do GWAM
phen_vals = phed.get_values(p_i)
Z = phed.get_incidence_matrix(p_i)
res = lm.emmax(snps, phen_vals, k, Z=Z, with_betas=p_dict['with_betas'],
emma_num=p_dict['emmax_emma_num'])
else:
res = lm.emmax(snps, phen_vals, k, with_betas=p_dict['with_betas'],
emma_num=p_dict['emmax_emma_num'])
elif mapping_method in ['emmax_step']:
sd.filter_mac_snps(p_dict['mac_threshold'])
local = False
if p_dict['local_gwas']:
local = True
file_prefix += '_' + '_'.join(map(str, p_dict['local_gwas']))
res = lm.emmax_step_wise(phen_vals, k, sd=sd, num_steps=p_dict['num_steps'],
file_prefix=file_prefix, local=local, cand_gene_list=cand_genes,
save_pvals=p_dict['save_stepw_pvals'],
emma_num=p_dict['emmax_emma_num'])
print 'Step-wise EMMAX finished!'
return
elif mapping_method in ['lm_step']:
sd.filter_mac_snps(p_dict['mac_threshold'])
local = False
if p_dict['local_gwas']:
local = True
file_prefix += '_' + '_'.join(map(str, p_dict['local_gwas']))
res = lm.lm_step_wise(phen_vals, sd=sd, num_steps=p_dict['num_steps'],
file_prefix=file_prefix, local=local, cand_gene_list=cand_genes,
save_pvals=p_dict['save_stepw_pvals'])
print 'Step-wise LM finished!'
return
elif mapping_method in ['lm']:
res = lm.linear_model(snps, phen_vals)
elif mapping_method in ['emmax_anova']:
res = lm.emmax_anova(snps, phen_vals, k)
elif mapping_method in ['lm_anova']:
res = lm.anova(snps, phen_vals)
else:
print "Mapping method", mapping_method, 'was not found.'
return
if mapping_method in ['lm', 'emma', 'emmax']:
kwargs['genotype_var_perc'] = res['var_perc']
additional_columns.append('genotype_var_perc')
if p_dict['with_betas'] or mapping_method in ['emma' ]:
betas = map(list, zip(*res['betas']))
kwargs['beta0'] = betas[0]
additional_columns.append('beta0')
if len(betas) > 1:
kwargs['beta1'] = betas[1]
additional_columns.append('beta1')
pvals = res['ps']
sys.stdout.write("Done!\n")
sys.stdout.flush()
if mapping_method in ['lm_anova', 'emmax_anova']:
kwargs['genotype_var_perc'] = res['var_perc']
pvals = res['ps']
sys.stdout.write("Done!\n")
sys.stdout.flush()
# print 'Calculating SNP-phenotype correlations.'
# kwargs['correlations'] = calc_correlations(snps, phen_vals)
# additional_columns.append('correlations')
print 'Writing result to file.'
res = gwaResults.Result(scores=pvals.tolist(), snps_data=sd, name=result_name, **kwargs)
if mapping_method in ["kw", "ft", "emma", 'lm', "emmax", 'emmax_anova', 'lm_anova']:
result_file = file_prefix + ".pvals"
else:
result_file = file_prefix + ".scores"
res.write_to_file(result_file, additional_columns, max_fraction=p_dict['pvalue_filter'])
#add results to DB..
if p_dict['add_to_db']:
print 'Adding results to DB.'
if p_dict['with_db_ids']:
db_pid = p_i
else:
db_pid = phed.get_db_pid(p_i)
import results_2_db as rdb
short_name = 'cm%d_pid%d_%s_%s_%s_%d_%s' % (p_dict['call_method_id'], db_pid, phenotype_name,
mapping_method, trans_method, p_dict['remove_outliers'],
str(p_dict['with_replicates']))
tm_id = transformation_method_dict[trans_method]
try:
rdb.add_results_to_db(result_file, short_name, p_dict['call_method_id'], db_pid,
analysis_methods_dict[mapping_method],
tm_id, remove_outliers=p_dict['remove_outliers'])
except Exception, err_str:
print 'Failed inserting results into DB!'
print err_str