def _run_():
p_dict, args = parse_parameters()
print "GWA runs are being set up with the following parameters:"
for k, v in p_dict.iteritems(): print k + ': ' + str(v)
print ''
#Load phenotype file
if p_dict['phen_file']:
print 'Loading phenotypes from file.'
phed = phenotypeData.readPhenotypeFile(p_dict['phen_file'],
with_db_ids=(not p_dict['no_phenotype_ids'])) #load phenotype file
else:
print 'Retrieving the phenotypes from the DB.'
phed = phenotypeData.getPhenotypes()
#If on the cluster, then set up runs..
if p_dict['parallel']:
if len(p_dict['pids']) == 0: #phenotype index arguement is missing, hence all phenotypes are run/analyzed.
if not p_dict['phen_file']:
raise Exception('Phenotype file or phenotype ID is missing.')
p_dict['pids'] = phed.phenIds
else:
raise Exception('Too many arguments..')
if analysis_plots: #Running on the cluster..
for p_i in p_dict['pids']:
run_parallel(p_i, phed, p_dict)
else:
for mapping_method in p_dict['specific_methods']:
for trans_method in p_dict['specific_transformations']:
for p_i in pids:
run_parallel(p_i, phed, p_dict, mapping_method, trans_method)
return #Exiting the program...
#SNPs data file name
if not p_dict['data_file']:
snps_data_file = '%s250K_t%d.csv' % (env['data_dir'], p_dict['call_method_id'])
else:
snps_data_file = p_dict['data_file']
#Plot analysis plots...
if p_dict['analysis_plots']:
analysis_plots(snps_data_file, phed, p_dict)
else:
#If not analysis plots... then GWAS
for p_i in p_dict['pids']:
print '-' * 120, '\n'
phenotype_name = phed.getPhenotypeName(p_i)
print "Performing GWAS for phenotype: %s, phenotype_id: %s" % (phenotype_name, p_i)
for trans_method in p_dict['specific_transformations']:
print 'Phenotype transformation:', trans_method
for mapping_method in p_dict['specific_methods']:
#DO ANALYSIS
print 'Mapping method:', mapping_method
map_phenotype(p_i, phed, snps_data_file, mapping_method, trans_method, p_dict)
def _runTest_(): import dataParsers import phenotypeData #Get phenotype data phenotypeFile = "/Network/Data/250k/dataFreeze_080608/phenotypes_transformed_publishable_v2.tsv" phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t') #Get Phenotype data #Get SNPs data snpsDataFile = "/Network/Data/250k/dataFreeze_080608/250K_f10_080608.csv" snpsds = dataParsers.parseCSVData(snpsDataFile) #Get SNPs data psFile = env.homedir + "tmp/tree.ps" marg_file = env.homedir + "tmp/test" out_file = env.homedir + "tmp/test_out" rFile = env.homedir + "tmp/tree_test.r" #Run Margarita marg = Margarita(marg_file, out_file) chr = 4 snpsd = snpsds[chr - 1].getSnpsData() marg.gwaWithTrees(marg_file, snpsd, phed, phenotype = 1, numMarkers = 200, chromosome = chr, boundaries = [200000, 350000], numPerm = 1, cutoff = 16, numArg = 100) #(self, id, snpsd, phed, phenotype=0, boundaries = None, numMarkers = 100, numPerm = 500000, cutoff = 16, numArg = 50) #which marginal tree runNum = 1 argNum = 1 markerNum = 1 marg.parseTreeFile(marg_file + ".marg.trees", rFile, psFile, runNum, argNum, markerNum)
def _fakeTransformPhenotypes_(): import os res_dir = "/Network/Data/250k/tmp-bvilhjal/emma_results/" phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_012509.tsv" phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t') for p_i in transformationMap: (trans_type,data_type) = transformationMap[p_i] print trans_type if trans_type==1 or trans_type==6: t_type = "new_logTransform" #m" elif trans_type==9 : t_type = "new_logTransform_f192" #m" elif trans_type==5: t_type = "newDataset_logTransform_noOutliers" elif trans_type==7: t_type = "new_ranks" elif trans_type==8: t_type = "neg_const_logTransform" else: #if trans_type==0: t_type = "new_raw" phenName = phed.getPhenotypeName(p_i) # oldName = res_dir+"Emma_"+t_type+"_"+phenName+".pvals" # newName = res_dir+"Emma_new_trans_"+phenName+".pvals" # cp_command = "sudo cp "+oldName+" "+newName # print cp_command # os.system(cp_command) oldName = res_dir+"Emma_"+t_type+"_"+phenName+".sr.pvals" newName = res_dir+"Emma_new_trans_"+phenName+".sr.pvals" cp_command = "sudo cp "+oldName+" "+newName print cp_command os.system(cp_command)
def _plotKinshipDiffs_():
filterProb = 0.2
p_i = 1
res_dir = "/Users/bjarni/tmp/"
runId = "full_"
snpsDataFile = "/Network/Data/250k/dataFreeze_080608/250K_f10_080608.csv"
snpsds = dataParsers.parseCSVData(snpsDataFile, format=1, deliminator=",") # ,debug=True)
phenotypeFile = "/Network/Data/250k/dataFreeze_080608/phenotypes_all_raw_111008.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter="\t")
snpsdata.coordinateSnpsAndPhenotypeData(phed, p_i, snpsds)
for snpsd in snpsds:
snpsd.filterMinMAF(0.1)
snpsd.filterMonoMorphicSnps()
totalSNPs = []
for i in range(len(snpsds)):
snpsds[i] = snpsds[i].getSnpsData()
totalSNPs += snpsds[i].snps
# For memory, remove random SNPs
snps = []
for snp in totalSNPs:
if random.random() < filterProb:
snps.append(snp)
totalSNPs = snps
print "Calculating the global kinship..."
globalKinship = calcKinship(totalSNPs)
print "done."
normalizedGlobalKinship = globalKinship / mean(globalKinship)
gc.collect() # Calling garbage collector, in an attempt to clean up memory..
for i in range(4, 5): # len(snpsds)):
chr = i + 1
snpsd = snpsds[i]
# pylab.subplot(5,1,chr)
# pylab.figure(figsize=(18,4))
# (kinshipDiffs,binPos,local300Kinships) = getKinshipDiffs(snpsd,normalizedGlobalKinship,windowSize=300000)
# pylab.plot(binPos,kinshipDiffs,"r",label='ws$=300000$')
# (kinshipDiffs,binPos,local500Kinships) = getKinshipDiffs(snpsd,normalizedGlobalKinship,windowSize=500000)
# pylab.plot(binPos,kinshipDiffs,"b",label='ws$=500000$')
# pylab.legend(numpoints=2,handlelen=0.005)
# pylab.title("Kinship diff. chr. "+str(chr))
# pylab.savefig(res_dir+runId+"kinshipDiffs_500_300kb_chr"+str(chr)+".pdf",format="pdf")
# pylab.clf()
pylab.figure(figsize=(18, 4))
(emmaDiffs, binPos) = getEmmaDiffs(snpsd, phed, p_i, globalKinship, windowSize=300000)
pylab.plot(binPos, emmaDiffs, "r", label="ws$=300000$")
pylab.title("Emma avg. p-value diff. 500kb on chr. " + str(chr))
(emmaDiffs, binPos) = getEmmaDiffs(snpsd, phed, p_i, globalKinship, windowSize=500000)
pylab.plot(binPos, emmaDiffs, "b", label="ws$=500000$")
pylab.title("Emma avg. p-value diff. on chr. " + str(chr))
pylab.legend(numpoints=2, handlelen=0.005)
pylab.savefig(res_dir + runId + "EmmaPvalDiffs_500_300kb_chr" + str(chr) + ".pdf", format="pdf")
pylab.clf()
gc.collect() # Calling garbage collector, in an attempt to clean up memory..
def runParallel(phenotypeIndex):
#Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter='\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/","_div_")
phenName = phenName.replace("*","_star_")
impFileName = resultDir+"RF_"+parallel+"_"+phenName
outFileName = impFileName
shstr = """#!/bin/csh
#PBS -l walltime=120:00:00
"""
shstr += "#PBS -l mem="+mem+"\n"
shstr +="""
#PBS -q cmb
"""
shstr += "#PBS -N RF"+phenName+"_"+parallel+"\n"
shstr += "(python "+programDir+"RandomForest.py -o "+impFileName+" --chunkSize "+str(chunkSize)+" --nTrees "+str(nTrees)+" --mem "+str(mem)+" --round2Size "+str(round2Size)+""
if nodeSize:
shstr += " --nodeSize "+str(nodeSize)+" "
if logTransform:
shstr += " --logTransform "
if not skipSecondRound:
shstr += " --secondRound "
shstr += " -a "+str(withArrayIds)+" "
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 ")
def runParallel(phenotypeIndex, id=""):
#Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outputFile = resultDir + "CAMP_" + parallel + "_" + phenName + id
shstr = """#!/bin/csh
#PBS -l walltime=24:00:00
#PBS -l mem=6g
#PBS -q cmb
"""
shstr += "#PBS -N C" + phenName + "_" + parallel + "\n"
shstr += "set phenotypeName=" + parallel + "\n"
shstr += "set phenotype=" + str(phenotypeIndex) + "\n"
shstr += "(python " + scriptDir + "Camp.py -o " + outputFile + " "
if sampleNum:
shstr += " -n " + str(sampleNum) + " "
if useFloats:
shstr += " --useFloats "
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 ")
def _fakeTransformPhenotypes_():
import os
res_dir = "/Network/Data/250k/tmp-bvilhjal/emma_results/"
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_042109.tsv"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter='\t')
for p_i in transformationMap:
(trans_type, data_type) = transformationMap[p_i]
print trans_type
if trans_type == 1 or trans_type == 6:
t_type = "logTransform" #m"
elif trans_type == 9:
t_type = "logTransform_f192" #m"
elif trans_type == 5:
t_type = "logTransform_noOutliers"
elif trans_type == 7:
t_type = "ranks"
elif trans_type == 8:
t_type = "neg_const_logTransform"
else: #if trans_type==0:
t_type = "raw"
phenName = phed.getPhenotypeName(p_i)
# oldName = res_dir+"Emma_"+t_type+"_"+phenName+".pvals"
# newName = res_dir+"Emma_trans_"+phenName+".pvals"
# cp_command = "sudo cp "+oldName+" "+newName
# print cp_command
# os.system(cp_command)
oldName = res_dir + "Emma_" + t_type + "_" + phenName + ".pvals"
newName = res_dir + "Emma_trans_" + phenName + ".pvals"
cp_command = "sudo cp " + oldName + " " + newName
print cp_command
os.system(cp_command)
def runParallel(phenotypeIndex, id=""):
# Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter="\t") # Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outputFile = resultDir + "CAMP_" + parallel + "_" + phenName + id
shstr = """#!/bin/csh
#PBS -l walltime=24:00:00
#PBS -l mem=6g
#PBS -q cmb
"""
shstr += "#PBS -N C" + phenName + "_" + parallel + "\n"
shstr += "set phenotypeName=" + parallel + "\n"
shstr += "set phenotype=" + str(phenotypeIndex) + "\n"
shstr += "(python " + scriptDir + "Camp.py -o " + outputFile + " "
if sampleNum:
shstr += " -n " + str(sampleNum) + " "
if useFloats:
shstr += " --useFloats "
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 ")
def runParallel(phenotypeIndex):
#Cluster specific parameters
#margdir = '/home/cmb-01/bvilhjal/Projects/Python-snps/'
resultDir = env.results_dir #'/home/cmb-01/bvilhjal/results/'
import phenotypeData
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outFileName = resultDir + "Marg_" + parallel + "_" + phenName
scoreFile = outFileName + ".score"
shstr = """#!/bin/csh
#PBS -l walltime=120:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
shstr += "#PBS -N M" + phenName + "_" + parallel + "\n"
#shstr += "(python " + margdir + "margarita.py "
shstr += "(python " + env.script_dir + "margarita.py "
if phed.isBinary(phenotypeIndex):
shstr += " --binary "
shstr += " -s " + scoreFile
shstr += " -a " + str(withArrayId) + " "
shstr += snpsDataFile + " " + phenotypeDataFile + " " + str(phenotypeIndex) + " "
shstr += "> " + outFileName + ".out) >& " + outFileName + ".err\n"
f = open(parallel + ".sh", 'w')
f.write(shstr)
f.close()
#Execute qsub script
os.system("qsub " + parallel + ".sh ")
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 ")
def _test_(): phenotypeFile = "/Network/Data/250k/dataFreeze_080608/phenotypes_all_raw_102208.tsv" print "Loading phenotype data" phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t') for p_i in range(215, 216): phenName = phed.getPhenotypeName(p_i) drawHistogram(phed, p_i, title = phenName) phed.logTransform(p_i) drawHistogram(phed, p_i, title = phenName)
def _test1_():
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_042109.tsv"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter='\t')
print "phenName, data_type, transformation_type"
for ctg in [1, 2, 3, 4]:
phenIds = phenotypeData.categories_2_phenotypes[ctg]
for pi in phenIds:
phenName = phed.getPhenotypeName(pi)
(trans_type, data_type) = transformationMap[pi]
print str(phenName) + ", " + str(
datatypeDict[data_type]) + ", " + str(
transformationTypes[trans_type])
def _drawPowerQQPlots_(phenotypeIndices=None,res_path="/Network/Data/250k/tmp-bvilhjal/power_analysis/results/",runId="gwPlot"): """ Draws all the GWA plots for 6 methods. """ import plotResults phenotypeFile = "/Network/Data/250k/dataFreeze_080608/phenotypes_all_raw_120308.tsv" phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter='\t') if not phenotypeIndices: phenotypeIndices = phed.phenIds #mainRTs = ["","_original192", "_original192_inverse","_original96","_original96_inverse"] #FIXME: add full result #mainLabels = ["Full data", "192 acc. overlap", "192 acc. complement", "96 acc. overlap", "96 acc. complement"] mainRTs = ["","_original192","_original96", "_latitude60","_latitude55", "_original192_latitude60", "_original192_latitude55"] #FIXME: add full result mainLabels = ["Full data", "192 acc. overlap", "96 acc. overlap", "latitude < 60", "Latitude < 55", "192 acc. overl. and lat. < 60", "192 acc. overl. and lat. < 55"] permRTs = []#["permTest","permTest"] colors = []#[[0.6,0.8,0.6],[0.6,0.6,0.8]] perm_counts = []#[10,10] perm_sample_sizes = []#[65 ,112] #[170,96] # permLabels = []#["random 65","random 112"] for p_i in phenotypeIndices: mainResults = [] phenName = phed.getPhenotypeName(p_i) pdfFile = res_path+phenName+"_log_QQplot.pdf" pngFile = res_path+phenName+"_log_QQplot.png" for i in range(0,len(mainRTs)): mainRT = mainRTs[i] name = mainLabels[i] filename = res_path+"KW_raw"+mainRT+"_"+phenName+".pvals" rt = gwaResults.ResultType(resultType="KW",name=name) print "Loading",filename result = gwaResults.Result(filename, name=name, resultType=rt) mainResults.append(result) permResultsList = [] for i in range(0,len(permRTs)): permResults = [] permRT = permRTs[i] for j in range(0,perm_counts[i]): filename = res_path+"KW_raw_"+permRT+"_"+phenName+"_r"+str(perm_sample_sizes[i])+"_"+str(j)+".pvals" rt = gwaResults.ResultType(resultType="KW",name=permRT) print "Loading",filename result = gwaResults.Result(filename, name=permRT, resultType=rt) permResults.append(result) permResultsList.append((permResults,permLabels[i],colors[i])) drawPermLogQQPlot(mainResults, permResultsList, phenName = phenName,pdfFile=pdfFile,pngFile=pngFile) gc.collect() #Calling garbage collector, in an attempt to clean up memory..
def _impute_FLC_192_():
phed = pd.readPhenotypeFile(
"/Users/bjarnivilhjalmsson/Projects/Data/phenotypes/FLC_phenotypes_011710.tsv"
)
d250k_file = env.home_dir + "Projects/Data/250k/250K_192_043009.csv"
d250k_sd = dataParsers.parse_snp_data(d250k_file)
d250k_sd.filter_accessions(phed.accessions)
d250k_sd.filter_maf_snps(0.05)
seq_snpsd = dataParsers.parseCSVData(
data_dir + "/flc_seqs_aln_imputed_snps_012710.csv")
seq_snpsd.onlyBinarySnps()
d250k_sd.snpsDataList[4].compareWith(seq_snpsd)
d250k_sd.snpsDataList[4].merge_data(seq_snpsd)
def _countVals_():
resdir = "/Network/Data/250k/tmp-bvilhjal/phenotype_analyzis/"
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_012509.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t')
phenotypeIndices = phenotypeData.categories_2_phenotypes[1]+phenotypeData.categories_2_phenotypes[2]+phenotypeData.categories_2_phenotypes[3]+phenotypeData.categories_2_phenotypes[4]
print "total # of phenotypes:", phed.countPhenotypes()
print "# of phenotypes analyzed:", len(phenotypeIndices)
totalCounts = []
for p_i in phenotypeIndices:
valCount = phed.countValues(p_i)
totalCounts.append(valCount)
snpsDataFile="/Network/Data/250k/dataFreeze_011209/250K_f13_012509.csv"
import dataParsers,snpsdata
snpsds = dataParsers.parseCSVData(snpsDataFile, format=1, deliminator=",")#,debug=True)
snpsd = snpsdata.SNPsDataSet(snpsds,[1,2,3,4,5])
phed.removeAccessionsNotInSNPsData(snpsd)
overlappingCounts = []
for p_i in phenotypeIndices:
valCount = phed.countValues(p_i)
overlappingCounts.append(valCount)
#ecotypes_192 = phenotypeData._getFirst192Ecotypes_()
ecotypes_192 = _get192Ecotypes_()
ecotypes_192 = [str(e) for e in ecotypes_192]
print "len(ecotypes_192):",len(ecotypes_192)
print ecotypes_192
phed.filterAccessions(ecotypes_192)
filename = resdir+"phen_value_count_new_data_012509_v2.txt"
f = open(filename,"w")
f.write("Phenotype, total_count, overlapping_count, 192_overlap_count\n")
for i in range(0,len(phenotypeIndices)):
p_i = phenotypeIndices[i]
try:
phenName = phed.getPhenotypeName(p_i)
valCount = phed.countValues(p_i)
f.write(str(phenName)+", "+str(totalCounts[i])+", "+str(overlappingCounts[i])+", "+str(valCount)+"\n")
except Exception:
print "\nPhenotype index", p_i, "failed."
f.close()
def _plot_local_FLC_haplotype_():
data_dir = "/Users/bjarnivilhjalmsson/Projects/FLC_analysis/"
ref_seq_name = "raw_ref_col-0"
ref_start = 3170501
ref_chr = 5
ad = sequences.readFastaAlignment(data_dir +
"flc_seqs_aln_merged_011810.fasta",
ref_seq_name=ref_seq_name,
ref_start=ref_start,
ref_chr=ref_chr,
alignment_type="muscle",
ref_direction=1)
r = ad.get_snps(type=1)
seq_snpsd = r['snpsd']
seq_snpsd.filter_na_snps(max_na_rate=0.05)
# seq_snpsd = dataParsers.parseCSVData(data_dir+"flc_seqs_aln_imputed_snps_012710.csv")[0]
# seq_snpsd = seq_snpsd.getSnpsData(missingVal='NA')
# seq_snpsd.remove_accessions(map(str,ad.ecotypes))
import phenotypeData as pd
phend = pd.readPhenotypeFile(
"/Users/bjarnivilhjalmsson/Projects/Data/phenotypes/FLC_phenotypes_011710.tsv"
) #phenotype_parsers.load_phentoype_file("/Users/bjarnivilhjalmsson/Projects/FLC_analysis/data_102509/FLC_soil_data_102509.csv")
for i in phend.phenIds:
phen_name = phend.getPhenotypeName(i)
import analyzeHaplotype as ah
ah.plot_haplotypes(
seq_snpsd.snps,
seq_snpsd.accessions,
haplotypeFile=
"/Users/bjarnivilhjalmsson/tmp/flc_seq_haplotypes_old.pdf")
for start, stop in [(3175500, 3176000), (3176000, 3176500),
(3176500, 3177000), (3177000, 3177500),
(3177500, 3178000), (3178000, 3178500),
(3178500, 3179000)]:
ah.plot_local_haplotypes(
"/Users/bjarnivilhjalmsson/tmp/flc_seq_haplotypes_" +
phen_name + "_" + str(start) + "_" + str(stop) + ".pdf",
seq_snpsd,
start,
stop,
phenotypeData=phend,
phen_id=i)
def __init__(self,phenotypeIndices=None,snpsds=None,results=None,results_map=None):
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_012509.tsv"
self.phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter='\t')
#if snpsds:
self.snpsds = snpsds
#else:
# snpsDataFile="/Network/Data/250k/dataFreeze_011209/250K_f13_012509.csv"
# self.snpsds = dataParsers.parseCSVData(snpsDataFile, format=1, deliminator=",")
self.results_map = {}
if results_map:
self.results_map=results_map
elif results:
for result in results:
if self.results_map.has_key(result.phenotypeID):
self.results_map[result.phenotypeID].append(result)
else:
self.results_map[result.phenotypeID] = [result]
elif phenotypeIndices:
self.loadData(phenotypeIndices)
def _get192Ecotypes_():
resdir = "/Network/Data/250k/tmp-bvilhjal/phenotype_analyzis/"
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_012509.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t')
phenotypeIndices = phenotypeData.categories_2_phenotypes[1]+phenotypeData.categories_2_phenotypes[2]+phenotypeData.categories_2_phenotypes[3]+phenotypeData.categories_2_phenotypes[4]
total_accessions = set()
for p_i in phenotypeIndices:
if not p_i in [5,6,7]:
accessions = phed.getAccessionsWithValues(p_i)
total_accessions = total_accessions.union(accessions)
ecotypes_192 = phenotypeData._getFirst192Ecotypes_()
ecotypes_192 = [str(e) for e in ecotypes_192]
print "len(ecotypes_192):",len(ecotypes_192)
#print ecotypes_192
phed.filterAccessions(ecotypes_192)
for p_i in [5,6,7]:
accessions = phed.getAccessionsWithValues(p_i)
total_accessions = total_accessions.union(accessions)
total_accessions = list(total_accessions)
print len(total_accessions)
total_accessions.sort()
print total_accessions
ecotype_info_dict = phenotypeData._getEcotypeIdInfoDict_()
ets = []
i = 0
for et in total_accessions:
et = int(et)
if ecotype_info_dict.has_key(et):
print str(et)+", "+str(ecotype_info_dict[et][0])+", "+str(ecotype_info_dict[et][1])
i += 1
ets.append(et)
else:
print et,"is missing in genotype data."
print i
return ets
def runParallel(phenotypeIndex):
# Cluster specific parameters
scriptDir = env.scriptDir
resultDir = env.resultDir
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter="\t") # Get Phenotype data
phed.onlyBiologyCategory(phenotypeCategory, host=host, user=user, passwd=passwd)
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outFile = resultDir + "CS_" + parallel + "_" + phenName
shstr = """#!/bin/csh
#PBS -l walltime=72:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
shstr += "#PBS -N CS" + phenName + "_" + parallel + "\n"
shstr += "(python " + scriptDir + "compositeScore.py -o" + outFile + " "
shstr += (
"--candGeneListID="
+ str(candGeneListID)
+ " --testDataFraction="
+ str(testDataFraction)
+ " --gridSize="
+ str(gridSize)
+ " --windowSize="
+ str(windowSize)
+ " --phenotypeCategory="
+ str(phenotypeCategory)
+ " "
+ str(phenotypeIndex)
+ " "
)
shstr += "> " + outFile + "_job" + ".out) >& " + outFile + "_job" + ".err\n"
f = open(parallel + ".sh", "w")
f.write(shstr)
f.close()
# Execute qsub script
os.system("qsub " + parallel + ".sh ")
def runParallel(phenotypeIndex):
#Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
impFileName = resultDir + "RF_" + parallel + "_" + phenName
outFileName = impFileName
shstr = """#!/bin/csh
#PBS -l walltime=50:00:00
"""
shstr += "#PBS -l mem=" + mem + "\n"
shstr += """
#PBS -q cmb
"""
shstr += "#PBS -N RF" + phenName + "_" + parallel + "\n"
shstr += "(python " + programDir + "RandomForest.py -o " + impFileName + " --chunkSize " + str(
chunkSize) + " --nTrees " + str(nTrees) + " --mem " + str(
mem) + " --round2Size " + str(round2Size) + ""
if nodeSize:
shstr += " --nodeSize " + str(nodeSize) + " "
if logTransform:
shstr += " --logTransform "
if not skipSecondRound:
shstr += " --secondRound "
shstr += " -a " + str(withArrayIds) + " "
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 ")
def runParallel(phenotypeIndex):
#Cluster specific parameters
scriptDir = env.scriptDir
resultDir = env.resultDir
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
phed.onlyBiologyCategory(phenotypeCategory,
host=host,
user=user,
passwd=passwd)
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outFile = resultDir + "CS_" + parallel + "_" + phenName
shstr = """#!/bin/csh
#PBS -l walltime=72:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
shstr += "#PBS -N CS" + phenName + "_" + parallel + "\n"
shstr += "(python " + scriptDir + "compositeScore.py -o" + outFile + " "
shstr += "--candGeneListID=" + str(
candGeneListID) + " --testDataFraction=" + str(
testDataFraction
) + " --gridSize=" + str(gridSize) + " --windowSize=" + str(
windowSize) + " --phenotypeCategory=" + str(
phenotypeCategory) + " " + str(phenotypeIndex) + " "
shstr += "> " + outFile + "_job" + ".out) >& " + outFile + "_job" + ".err\n"
f = open(parallel + ".sh", 'w')
f.write(shstr)
f.close()
#Execute qsub script
os.system("qsub " + parallel + ".sh ")
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 _plotKW_():
"""
Analyze how population structure affects KW.
"""
filterProb = 0.1
p_i = 1
res_dir = "/Users/bjarni/tmp/"
runId = "_full_quick_"
snpsDataFile = "/Network/Data/250k/dataFreeze_080608/250K_f10_080608.csv"
snpsds = dataParsers.parseCSVData(snpsDataFile, format=1,
deliminator=",") #,debug=True)
phenotypeFile = "/Network/Data/250k/dataFreeze_080608/phenotypes_all_raw_111008.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter='\t')
snpsdata.coordinateSnpsAndPhenotypeData(phed, p_i, snpsds)
totalSNPs = []
for i in range(len(snpsds)):
snpsds[i] = snpsds[i].getSnpsData()
totalSNPs += snpsds[i].snps
#For memory, remove random SNPs
snps = []
for snp in totalSNPs:
if random.random() < filterProb:
snps.append(snp)
totalSNPs = snps
#globalKinship = calcKinship(totalSNPs)
gc.collect(
) #Calling garbage collector, in an attempt to clean up memory..
#chr = 1
#for snpsd in snpsds:
snpsd = snpsds[3]
k = calcKinship(snpsd.snps[200:1400])
res = runEmma(phed, p_i, k,
snpsd.snps[200:1400]) #runEmma(phed,p_i,k,snps):
pvals = res["ps"]
log_pvals = []
for pval in pvals:
#print pval
log_pvals.append(-math.log10(pval))
pylab.plot(snpsd.positions[200:1400],
log_pvals,
"c.",
label="Emma (local)")
k = calcKinship(totalSNPs)
res = runEmma(phed, p_i, k,
snpsd.snps[200:1400]) #runEmma(phed,p_i,k,snps):
pvals = res["ps"]
log_pvals = []
for pval in pvals:
#print pval
log_pvals.append(-math.log10(pval))
pylab.plot(snpsd.positions[200:1400],
log_pvals,
"g.",
label="Emma (global)")
phenVals = phed.getPhenVals(p_i)
pvals = _run_kw_(snpsd.snps[200:1400], phenVals)
log_pvals = []
for pval in pvals:
#print pval
log_pvals.append(-math.log10(pval))
pylab.plot(snpsd.positions[200:1400],
log_pvals,
"r.",
label="KW (full data)")
(pvals, new_positions,
acc_groups) = get_KW_pvals(snpsd.snps[200:1400],
snpsd.positions[200:1400],
phed,
p_i,
kinshipThreshold=0.95,
method="KW")
ecot_map = phenotypeData._getEcotypeIdToStockParentDict_()
for i in range(0, len(acc_groups)):
acc_list = []
for a_i in acc_groups[i]:
e_i = snpsd.accessions[a_i]
#print e_i
acc_list.append(ecot_map[int(e_i)][0])
print "group", i, ":", acc_list
log_pvals = []
for pval in pvals:
#print pval
log_pvals.append(-math.log10(pval))
pylab.plot(new_positions, log_pvals, "b.", label="KW (merged data)")
pylab.legend(numpoints=2, handlelen=0.005)
pylab.show()
def _plotRobustnessTests_():
import csv
resdir = "/Network/Data/250k/tmp-bvilhjal/robustness_test/"
fig_dir = "/Users/bjarni/tmp/"
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_012509.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t')
phenotypeIndices = phenotypeData.categories_2_phenotypes[1]+phenotypeData.categories_2_phenotypes[2]+phenotypeData.categories_2_phenotypes[3]+phenotypeData.categories_2_phenotypes[4]
#First Emma
emma_sd_dict = {}
emma_sd_list = []
emma_log_pvalues = []
found_phenotypes = []
for p_i in phenotypeIndices:
try:
phenName = phed.getPhenotypeName(p_i)
filename = resdir+"KW_rob_f1_"+phenName+".rob.log_pvals_sd"
print "Loading", filename, "..."
reader = csv.reader(open(filename, "rb"))
reader.next()
for row in reader:
emma_log_pvalues.append(float(row[0]))
emma_sd_list.append(float(row[1]))
found_phenotypes.append(p_i)
except Exception:
print p_i,"failed."
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
xs = np.array(emma_log_pvalues)
ys = np.array(emma_sd_list)
print len(emma_sd_list),len(emma_log_pvalues)
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
#plt.subplots_adjust(hspace=0.5)
#plt.subplot(121)
plt.hexbin(xs,ys,bins='log',cmap=cm.jet)
plt.axis([xmin, xmax, ymin, ymax])
cb = plt.colorbar()
cb.set_label('$log_{10}(N)$')
plt.ylabel("SD$(\Delta log(p)))$")
plt.xlabel("$log(p)$")
plt.savefig(fig_dir+"KW_overall_robustness.png", format = "png")
plt.clf()
emma_sd_dict = {}
emma_sd_list = []
emma_log_pvalues = []
found_phenotypes = []
for p_i in phenotypeIndices:
try:
phenName = phed.getPhenotypeName(p_i)
filename = resdir+"Emma_rob_f1_"+phenName+".rob.log_pvals_sd"
print "Loading", filename, "..."
reader = csv.reader(open(filename, "rb"))
reader.next()
for row in reader:
emma_log_pvalues.append(float(row[0]))
emma_sd_list.append(float(row[1]))
found_phenotypes.append(p_i)
except Exception:
print p_i,"failed."
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
xs = np.array(emma_log_pvalues)
ys = np.array(emma_sd_list)
print len(emma_sd_list),len(emma_log_pvalues)
xmin = xs.min()
xmax = xs.max()
ymin = ys.min()
ymax = ys.max()
#plt.subplots_adjust(hspace=0.5)
#plt.subplot(121)
plt.hexbin(xs,ys,bins='log',cmap=cm.jet)
plt.axis([xmin, xmax, ymin, ymax])
cb = plt.colorbar()
cb.set_label('$log_{10}(N)$')
plt.ylabel("SD$(\Delta log(p)))$")
plt.xlabel("$log(p)$")
plt.savefig(fig_dir+"Emma_overall_robustness.png", format = "png")
plt.clf()
def _run_():
import os
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = ["id=", "chr=", "numARG=", "numMarkers=", "numPerm=", "smartCutoff=", "BoundaryStart=", "BoundaryEnd=", "binary", "delim=", "missingval=", "withArrayId=", "phenotypeFileType=", "debug", "parallel=", "parallelAll", "help", "scoreFile="]
try:
opts, args = getopt.getopt(sys.argv[1:], "i:s:c:d:m:a:bh", long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
import tempfile
tempfile.tempdir = '/tmp'
(fId, id) = tempfile.mkstemp()
os.close(fId)
scoreFile = None
chr = None
numARG = 30
numMarkers = 100
numPerm = 0
smartCutoff = 10
binary = False
delim = ","
missingVal = "NA"
debug = None
report = None
help = 0
withArrayId = 0
boundaries = [ - 1, - 1]
phenotypeFileType = 1
parallel = None
parallelAll = False
snpsDataFile = None
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-i", "--id"):
id = '/tmp/' + arg
elif opt in ("-s", "--scoreFile"):
scoreFile = arg
elif opt in ("-c", "--chr"):
chr = int(arg)
elif opt in ("--numARG"):
numARG = int(arg)
elif opt in ("--numMarkers"):
numMarkers = int(arg)
elif opt in ("--numPerm"):
numPerm = int(arg)
elif opt in ("--BoundaryStart"):
boundaries[0] = int(arg)
elif opt in ("--BoundaryEnd"):
boundaries[1] = int(arg)
elif opt in ("--smartCutoff"):
smartCutoff = int(arg)
elif opt in ("--phenotypeFileType"):
phenotypeFileType = int(arg)
elif opt in ("--binary"):
binary = True
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
elif opt in ("-a", "--withArrayId"):
withArrayId = int(arg)
elif opt in ("-b", "--debug"):
debug = 1
if len(args) < 3 and not parallel:
if help == 0:
print "Arguments are missing!!\n"
print __doc__
sys.exit(2)
if boundaries[0] == boundaries[1] and boundaries[0] == - 1:
boundaries = None
margFile = id + ".marg"
outFile = margFile + ".out"
def runParallel(phenotypeIndex):
#Cluster specific parameters
#margdir = '/home/cmb-01/bvilhjal/Projects/Python-snps/'
resultDir = env.results_dir #'/home/cmb-01/bvilhjal/results/'
import phenotypeData
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outFileName = resultDir + "Marg_" + parallel + "_" + phenName
scoreFile = outFileName + ".score"
shstr = """#!/bin/csh
#PBS -l walltime=120:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
shstr += "#PBS -N M" + phenName + "_" + parallel + "\n"
#shstr += "(python " + margdir + "margarita.py "
shstr += "(python " + env.script_dir + "margarita.py "
if phed.isBinary(phenotypeIndex):
shstr += " --binary "
shstr += " -s " + scoreFile
shstr += " -a " + str(withArrayId) + " "
shstr += snpsDataFile + " " + phenotypeDataFile + " " + str(phenotypeIndex) + " "
shstr += "> " + outFileName + ".out) >& " + outFileName + ".err\n"
f = open(parallel + ".sh", 'w')
f.write(shstr)
f.close()
#Execute qsub script
os.system("qsub " + parallel + ".sh ")
#Nested function ends
snpsDataFile = args[0]
phenotypeDataFile = args[1]
if parallel: #Running on the cluster..
if len(args) > 2:
phenotypeIndex = int(args[2])
runParallel(phenotypeIndex)
return
else:
snpsDataFile = args[0]
if not parallelAll:
phenotypeIndex = int(args[1])
runParallel(phenotypeIndex)
return
import phenotypeData
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
for phenotypeIndex in phed.phenIds:
runParallel(phenotypeIndex)
return
phenotypeIndex = int(args[2])
#Print out information about this run...
print "Preparing a blended margarita...."
print "Num ARG:", numARG
print "Num Markers:", numMarkers
print "Num Permutations:", numPerm
print "Smart cutoff:", smartCutoff
print "Binary:", binary
print "ScoreFile:", scoreFile
import dataParsers, snpsdata, phenotypeData
#phenotypeFile = "/Users/bjarni/Projects/Python-snps/tinaPhenos_041808.csv"
if phenotypeFileType == 1:
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
elif phenotypeFileType == 2:
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, accessionDecoder = dataParsers.accessionName2EcotypeId, type = 2)
snpsds = dataParsers.parseCSVData(snpsDataFile, deliminator = delim, missingVal = missingVal, withArrayIds = bool(withArrayId)) #Get SNPs data
marg = Margarita(margFile, outFile, numARG, numMarkers, numPerm, smartCutoff)
if chr:
snpsd = snpsds[chr - 1].getSnpsData()
marg.gwa(snpsd, phed, phenotype = phenotypeIndex, boundaries = boundaries, chromosome = chr, binary = binary)
else:
scoreStr = ""
for chr in [0, 1, 2, 3, 4]:
snpsd = snpsds[chr].getSnpsData()
(newRStr, newScoreStr, permPvals) = marg.gwa(snpsd, phed, phenotype = phenotypeIndex, boundaries = boundaries, chromosome = chr + 1, binary = binary)
scoreStr += newScoreStr
f = open(scoreFile, 'w')
f.write(scoreStr)
f.close()
def _generate_250K_2010_FLC_data_(impute=True):
"""
Create a combined version of
250K, overlapping with the FLC phenotypes.
Then merge with 2010 data (including indels).
Then merge with FLC sequences.
Impute missing SNPs.
write to file.
"""
import phenotypeData as pd
import env
phed = pd.readPhenotypeFile(
"/Users/bjarnivilhjalmsson/Projects/Data/phenotypes/FLC_phenotypes_011710.tsv"
)
d2010_file = env.home_dir + "Projects/Data/2010/2010_imputed_012610.csv"
d2010_sd = dataParsers.parse_snp_data(d2010_file, id="2010_data")
d2010_sd.filter_accessions(phed.accessions)
d2010_sd.filter_na_snps()
d2010_sd.filter_maf_snps(0.05)
#d250k_file = env.home_dir+"Projects/Data/250k/250K_t54.csv"
d250k_file = env.home_dir + "Projects/Data/250k/250K_192_043009.csv"
d250k_sd = dataParsers.parse_snp_data(d250k_file)
d250k_sd.filter_accessions(phed.accessions)
d250k_sd.filter_maf_snps(0.05)
d250k_sd.merge_snps_data(d2010_sd)
d250k_sd.filter_na_accessions()
d250k_sd.filter_na_snps(0.7)
d250k_sd.filter_monomorphic_snps()
ref_seq_name = "raw_ref_col-0"
ref_start = 3170501
ref_chr = 5
seq_file = env.home_dir + "Projects/FLC_analysis/flc_seqs_aln_merged_050410.fasta"
ad = sequences.readFastaAlignment(seq_file,
ref_seq_name=ref_seq_name,
ref_start=ref_start,
ref_chr=ref_chr,
alignment_type="muscle",
ref_direction=1)
# ref_start = 3170500
# ad2 = sequences.readFastaAlignment(seq_file,ref_seq_name=ref_seq_name,ref_start=ref_start,
# ref_chr=ref_chr,alignment_type="muscle",ref_direction=1)
# ref_start = 3170502
# ad3 = sequences.readFastaAlignment(seq_file,ref_seq_name=ref_seq_name,ref_start=ref_start,
# ref_chr=ref_chr,alignment_type="muscle",ref_direction=1)
pdb.set_trace()
r = ad.get_snps(type=0)
seq_snpsd1 = r['snpsd']
seq_snpsd1.merge_data(r['indels'], error_threshold=0.0)
# r2 = ad2.get_snps(type=0)
# seq_snpsd2 = r2['snpsd']
# seq_snpsd2.merge_data(r2['indels'],error_threshold=0.0)
#
# r3 = ad3.get_snps(type=0)
# seq_snpsd3 = r3['snpsd']
# seq_snpsd3.merge_data(r3['indels'],error_threshold=0.0)
print "Now merging data.."
d250k_sd.snpsDataList[4].compareWith(seq_snpsd1)
# d250k_sd.snpsDataList[4].compareWith(seq_snpsd2)
# d250k_sd.snpsDataList[4].compareWith(seq_snpsd3)
d250k_sd.snpsDataList[4].merge_data(seq_snpsd1, union_accessions=False)
d250k_sd.filter_na_accessions()
d250k_sd.filter_na_snps(0.7)
d250k_sd.filter_monomorphic_snps()
d250k_sd.snpsDataList[4].impute_data()
d250k_sd.writeToFile("/tmp/test.csv")
print "YEAH!"
def _run_():
if len(sys.argv)==1:
print __doc__
sys.exit(2)
long_options_list=["outputFile=", "delim=", "missingval=", "withArrayId=", "phenotypeFileType=",
"help", "parallel=", "parallelAll", "addToDB",
"callMethodID=", "comment=", "onlyOriginal192","onlyOriginal96", "subSample=" ,
"subSampleLikePhenotype=", "subsampleTest=", "complement", "onlyBelowLatidue=",
"onlyAboveLatidue=", "srInput=", "sr","srOutput=", "srPar=","srSkipFirstRun",
"permTest=", "savePermutations", "permutationFilter=", "testRobustness"]
try:
opts, args=getopt.getopt(sys.argv[1:], "o:c:d:m:a: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
withArrayIds=1
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
for opt, arg in opts:
if opt in ("-h", "--help"):
help=1
print __doc__
elif opt in ("-a", "--withArrayId"):
withArrayIds=int(arg)
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])
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 "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
def runParallel(phenotypeIndex,id=""):
#Cluster specific parameters
phed=phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
phenName=phed.getPhenotypeName(phenotypeIndex)
phenName=phenName.replace("/", "_div_")
phenName=phenName.replace("*", "_star_")
outputFile=resultDir+"KW_"+parallel+"_"+phenName+id
shstr="""#!/bin/csh
#PBS -l walltime=100:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
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+" "
shstr+=" -a "+str(withArrayIds)+" "
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, withArrayIds = withArrayIds)
#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
if env.user=="bjarni":
tempfile.tempdir='/tmp'
(fId, phenotypeTempFile)=tempfile.mkstemp()
os.close(fId)
(fId, genotypeTempFile)=tempfile.mkstemp()
os.close(fId)
phed.writeToFile(phenotypeTempFile, [phenotype])
sys.stdout.write("Phenotype file written\n")
sys.stdout.flush()
snpsDataset=snpsdata.SNPsDataSet(newSnpsds, [1, 2, 3, 4, 5])
decoder={1:1, 0:0,-1:'NA'}
snpsDataset.writeToFile(genotypeTempFile, deliminator = delim, missingVal = missingVal, withArrayIds = 0, decoder = decoder)
sys.stdout.write("Genotype file written\n")
sys.stdout.flush()
phenotypeName=phed.getPhenotypeName(phenotypeIndex)
rDataFile=outputFile+".rData"
pvalFile=outputFile+".pvals"
#Is the phenotype binary?
binary=phed.isBinary(phenotypeIndex)
rstr=_generateRScript_(genotypeTempFile, phenotypeTempFile, rDataFile, pvalFile, name = phenotypeName, binary = binary)
rFileName=outputFile+".r"
f=open(rFileName, 'w')
f.write(rstr)
f.close()
outRfile=rFileName+".out"
errRfile=rFileName+".err"
print "Running R file:"
cmdStr="(R --vanilla < "+rFileName+" > "+outRfile+") >& "+errRfile
sys.stdout.write(cmdStr+"\n")
sys.stdout.flush()
gc.collect()
os.system(cmdStr)
#print "Emma output saved in R format in", rDataFile
print "Generating a GW plot."
res = gwaResults.Result(pvalFile,name="KW_"+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)
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 _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = ["chunkSize=", "nTrees=", "impFile=", "delim=", "missingval=", "withArrayId=", "logTransform", "phenotypeFileType=", "help", "parallel=", "parallelAll", "nodeSize=", "mem=", "round2Size=", "secondRound", "minMAF="]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:d:m:a:h", long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
phenotypeFileType = 1
impFile = None
delim = ","
missingVal = "NA"
help = 0
withArrayIds = 1
parallel = None
logTransform = False
parallelAll = False
chunkSize = 250000
round2Size = 5000
nTrees = 15000
nodeSize = None
mem = "8g"
skipSecondRound = True
minMAF = 0.0
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-a","--withArrayId"):
withArrayIds = int(arg)
elif opt in ("-o","--rFile"):
impFile = arg
elif opt in ("--phenotypeFileType"):
phenotypeFileType = int(arg)
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("--logTransform"):
logTransform = True
elif opt in ("--secondRound"):
skipSecondRound = False
elif opt in ("-d","--delim"):
delim = arg
elif opt in ("--chunkSize"):
chunkSize = int(arg)
elif opt in ("--round2Size"):
round2Size = int(arg)
elif opt in ("--nTrees"):
nTrees = int(arg)
elif opt in ("--nodeSize"):
nodeSize = int(arg)
elif opt in ("--mem"):
mem = arg
elif opt in ("-m","--missingval"):
missingVal = arg
elif opt in ("-m","--minMAF"):
minMAF = float(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)
def runParallel(phenotypeIndex):
#Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter='\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/","_div_")
phenName = phenName.replace("*","_star_")
impFileName = resultDir+"RF_"+parallel+"_"+phenName
outFileName = impFileName
shstr = """#!/bin/csh
#PBS -l walltime=120:00:00
"""
shstr += "#PBS -l mem="+mem+"\n"
shstr +="""
#PBS -q cmb
"""
shstr += "#PBS -N RF"+phenName+"_"+parallel+"\n"
shstr += "(python "+programDir+"RandomForest.py -o "+impFileName+" --chunkSize "+str(chunkSize)+" --nTrees "+str(nTrees)+" --mem "+str(mem)+" --round2Size "+str(round2Size)+""
if nodeSize:
shstr += " --nodeSize "+str(nodeSize)+" "
if logTransform:
shstr += " --logTransform "
if not skipSecondRound:
shstr += " --secondRound "
shstr += " -a "+str(withArrayIds)+" "
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 ")
#Nested function ends
snpsDataFile = args[0]
phenotypeDataFile = args[1]
if parallel: #Running on the cluster..
if parallelAll:
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter='\t') #Get Phenotype data
for phenotypeIndex in phed.phenIds:
runParallel(phenotypeIndex)
else:
phenotypeIndex = int(args[2])
runParallel(phenotypeIndex)
return
else:
phenotypeIndex = int(args[2])
print "chunkSize:",chunkSize
print "nTrees:",nTrees
print "nodeSize:",nodeSize
print "mem:",mem
print "logTransform:",logTransform
print "round2Size:",round2Size
print "skipSecondRound:",skipSecondRound
#Loading genotype data
import dataParsers
snpsds = dataParsers.parseCSVData(snpsDataFile, format=1, deliminator=delim, missingVal=missingVal, withArrayIds=withArrayIds)
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter='\t') #Get Phenotype data
phenotype = phed.getPhenIndex(phenotypeIndex)
accIndicesToKeep = []
phenAccIndicesToKeep = []
numAcc = len(snpsds[0].accessions)
#Load phenotype file
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)
#Log-transforming
if logTransform:
print "Log transforming phenotype"
phed.logTransform(phenotype)
#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)
#Converting format to 01
import snpsdata
newSnpsds = []
sys.stdout.write("Converting data format")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
newSnpsds.append(snpsd.getSnpsData())
print ""
snpsds = newSnpsds
#Writing files
import tempfile
if env.user=="bjarni":
tempfile.tempdir='/tmp'
(fId, phenotypeTempFile) = tempfile.mkstemp()
os.close(fId)
(fId, genotypeTempFile) = tempfile.mkstemp()
os.close(fId)
phed.writeToFile(phenotypeTempFile, [phenotype])
sys.stdout.write( "Phenotype file written\n")
sys.stdout.flush()
#Retain only the correct runchunk of data.
chromasomes = []
positions = []
snps = []
for i in range(0,len(snpsds)):
snpsd = snpsds[i]
positions += snpsd.positions
snps += snpsd.snps
chrList = [i+1]*len(snpsd.positions)
chromasomes += chrList
#Is the phenotype binary?
binary = phed.isBinary(phenotypeIndex)
import util
impFile = impFile+".imp"
rDataFile = impFile+".rData"
rFile = impFile+".r"
outRfile = rFile+".out"
errRfile = rFile+".err"
topImpFile = impFile+"_top"+str(chunkSize)+".imp"
topRDataFile = impFile+"_top.rData"
try:
os.remove(impFile) #Removing file if it already exits.
except Exception:
print "Couldn't remove",impFile
try:
os.remove(topImpFile) #Removing file if it already exits.
except Exception:
print "Couldn't remove",topImpFile
for startIndex in range(0,len(positions),chunkSize):
if startIndex+chunkSize>=len(positions):
endIndex = len(positions)
else:
endIndex = startIndex+chunkSize
#Writing genotype data to file.
tmpFile = open(genotypeTempFile,"w")
for i in range(startIndex,endIndex):
outStr =""
snp = util.valListToStrList(snps[i])
outStr += str(chromasomes[i])+","+str(positions[i])+","
outStr += ",".join(snp)
outStr += "\n"
tmpFile.write(outStr)
tmpFile.close()
rstr = _generateRScript_(genotypeTempFile, phenotypeTempFile, impFile, rDataFile, cluster=True, binary=binary, nTrees=nTrees, nodeSize=nodeSize)
f = open(rFile,'w')
f.write(rstr)
f.close()
#outRfile = rFile+"_"+str(startIndex/chunkSize)+".out"
#errRfile = rFile+"_"+str(startIndex/chunkSize)+".err"
print "Running model nr",startIndex/chunkSize,":"
cmdStr = "(R --vanilla < "+rFile+" > "+outRfile+") >& "+errRfile
sys.stdout.write(cmdStr+"\n")
sys.stdout.flush()
os.system(cmdStr)
print "Random forest output saved in", impFile
if not skipSecondRound:
#Run on the top 'chunkSize' number of hits.
#loading the R output file.
impF = open(impFile,"r")
lines=impF.readlines()
impF.close()
impList = list()
for i in range(1,len(lines)):
line = lines[i]
line.strip()
l = line.split(",")
impList.append( (float(l[2]),l[0],l[1],snps[i]) )
impList.sort()
impList.reverse()
#Writing genotype data to file.
tmpFile = open(genotypeTempFile,"w")
for i in range(0,round2Size):
outStr = ""
snp = util.valListToStrList(impList[i][3])
outStr += str(impList[i][1])+","+str(impList[i][2])+","
outStr += ",".join(snp)
outStr += "\n"
tmpFile.write(outStr)
tmpFile.close()
rstr = _generateRScript_(genotypeTempFile, phenotypeTempFile, topImpFile, topRDataFile, cluster=True, binary=binary, nTrees=nTrees, nodeSize=nodeSize)
f = open(rFile,'w')
f.write(rstr)
f.close()
print "Running randomForest on the top importance scores:"
cmdStr = "(R --vanilla < "+rFile+" > "+outRfile+") >& "+errRfile
sys.stdout.write(cmdStr+"\n")
sys.stdout.flush()
os.system(cmdStr)
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = [
"outputSNPsFile=",
"outputPhenotFile=",
"filterMonomorphic",
"rawDataFormat",
"delim=",
"missingval=",
"withArrayId=",
"phenotype=",
"phenotypeFile=",
"phenotypeName=",
"calcKinshipMatrix=",
"orderAccessions",
"help",
]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:u:d:m:a:f:p:h", long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
inputFile = args[0]
output_fname = None
outputPhenotFile = None
delim = ","
missingVal = "NA"
phenotypeFile = None
kinshipMatrixFile = None
phenotype = None
phenotypeName = None
rawDataFormat = False
monomorphic = False
help = 0
withArrayIds = 1
orderAccessions = False
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-a", "--withArrayId"):
withArrayIds = int(arg)
elif opt in ("-f", "--phenotypeFile"):
phenotypeFile = arg
elif opt in ("calcKinshipMatrix"):
kinshipMatrixFile = arg
elif opt in ("--filterMonomorphic"):
monomorphic = True
elif opt in ("--rawDataFormat"):
rawDataFormat = True
elif opt in ("--minCallProb"):
minCallProb = float(arg)
elif opt in ("-p", "--phenotype"):
phenotype = int(arg)
elif opt in ("-o", "--outputSNPsFile"):
output_fname = arg
elif opt in ("--orderAccessions"):
orderAccessions = True
elif opt in ("-u", "--phenotypeFile"):
outputPhenotFile = arg
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
else:
if help == 0:
print "Unkown option!!\n"
print __doc__
sys.exit(2)
if not output_fname:
print output_fname
if help == 0:
print "Output file missing!!\n"
print __doc__
sys.exit(2)
waid1 = withArrayIds == 1 or withArrayIds == 2
waid2 = withArrayIds == 2
import dataParsers
snpsds = dataParsers.parseCSVData(inputFile, format=1, deliminator=delim, missingVal=missingVal, withArrayIds=waid1)
if phenotypeFile:
import phenotypeData
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter="\t") # Get Phenotype data
accIndicesToKeep = []
phenAccIndicesToKeep = []
numAcc = len(snpsds[0].accessions)
if phenotype >= 0:
# Load phenotype file
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
elif phenotype == None:
sys.stdout.write("Removing accessions which do not have any phenotype values.")
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:
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."
if outputPhenotFile:
print "Filtering phenotype data."
phed.removeAccessions(phenAccIndicesToKeep)
if orderAccessions:
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)
if phenotype >= 0:
phed.writeToFile(outputPhenotFile, [phenotype])
else:
phed.writeToFile(outputPhenotFile)
# Filtering monomorphic
if monomorphic:
print "Filtering monomorphic SNPs"
for snpsd in snpsds:
print "Removed", str(snpsd.filterMonoMorphicSnps()), "Snps"
import snpsdata
newSnpsds = []
if not rawDataFormat:
sys.stdout.write("Converting data format")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
newSnpsds.append(snpsd.getSnpsData())
print ""
waid1 = 0
snpsDataset = snpsdata.SnpsDataSet(newSnpsds, [1, 2, 3, 4, 5])
decoder = {1: 1, 0: 0, -1: "NA"}
else:
snpsDataset = snpsdata.SnpsDataSet(snpsds, [1, 2, 3, 4, 5])
decoder = None
snpsDataset.writeToFile(output_fname, deliminator=delim, missingVal=missingVal, withArrayIds=waid1, decoder=decoder)
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = ["outputFile=", "delim=", "missingval=", "sampleNum=", "parallel=", "parallelAll", "useFloats"]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:d:m:n: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
withArrayIds = 1
parallel = None
parallelAll = False
sampleNum = None
chromosomes = [1, 2, 3, 4, 5]
useFloats = False
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-o", "--outputFile"):
outputFile = arg
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
elif opt in ("n", "--sampleNum"):
sampleNum = int(arg)
elif opt in ("--useFloats"):
useFloats = True
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 "CAMP is being set up with the following parameters:"
print "phenotypeDataFile:", phenotypeDataFile
if len(args) > 2:
print "Phenotype_id:", args[2]
print "snpsDataFile:", snpsDataFile
print "parallel:", parallel
print "parallelAll:", parallelAll
print "sampleNum:", sampleNum
def runParallel(phenotypeIndex, id=""):
# Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter="\t") # Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outputFile = resultDir + "CAMP_" + parallel + "_" + phenName + id
shstr = """#!/bin/csh
#PBS -l walltime=24:00:00
#PBS -l mem=6g
#PBS -q cmb
"""
shstr += "#PBS -N C" + phenName + "_" + parallel + "\n"
shstr += "set phenotypeName=" + parallel + "\n"
shstr += "set phenotype=" + str(phenotypeIndex) + "\n"
shstr += "(python " + scriptDir + "Camp.py -o " + outputFile + " "
if sampleNum:
shstr += " -n " + str(sampleNum) + " "
if useFloats:
shstr += " --useFloats "
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)
else:
phenotypeIndex = int(args[2])
runParallel(phenotypeIndex)
return
else:
phenotypeIndex = int(args[2])
# Load phenotype file
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter="\t") # Get Phenotype data
# Load genotype file
snpsds = dataParsers.parseCSVData(
snpsDataFile, format=1, deliminator=delim, missingVal=missingVal, withArrayIds=withArrayIds
)
# 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 ""
# Writing phenotype data to CAMP format.
(fId, phenotypeFile) = tempfile.mkstemp()
os.close(fId)
phenVals = phed.getPhenVals(phenotypeIndex, asString=False)
if not useFloats:
phenVals = map(int, phenVals)
phenFile = open(phenotypeFile, "w")
for value in phenVals:
phenFile.write(str(value) + "\n")
phenFile.close()
chromosome_list = []
positions_list = []
scores_list = []
interaction_positions_list = []
mafs = []
marfs = []
# Writing SNP data to CAMP format.
for chromosome in chromosomes:
(fId, snpsFile) = tempfile.mkstemp()
os.close(fId)
(fId, posFile) = tempfile.mkstemp()
os.close(fId)
sf = open(snpsFile, "w")
pf = open(posFile, "w")
snpsd = newSnpsds[chromosome - 1]
for i in range(0, len(snpsd.snps)):
snp = snpsd.snps[i]
(marf, maf) = snpsdata.getMAF(snp)
marfs.append(marf)
mafs.append(maf)
str_snp = map(str, snp)
double_snp = []
for nt in str_snp:
double_snp.append(nt)
double_snp.append(nt)
sf.write("".join(double_snp) + "\n")
pf.write(str(snpsd.positions[i]) + "\n")
sf.close()
pf.close()
outFile = outputFile + "_job_" + str(chromosome) + ".out"
errFile = outputFile + "_job_" + str(chromosome) + ".err"
resFile = outputFile + "_" + str(chromosome) + ".out"
print "resFile,outFile,errFile,snpsFile,posFile,phenotypeFile:", resFile, outFile, errFile, snpsFile, posFile, phenotypeFile
results = _runCAMP_(resFile, outFile, errFile, snpsFile, posFile, phenotypeFile, sampleNum)
positions_list += results["positions"]
scores_list += results["scores"]
for (i, j) in results["snpIndices"]:
if not (j < 0 or i < 0):
marfs.append(0.5) # An ugly hack!!!
mafs.append(0.5)
chromosome_list.append(chromosome)
scoreFile = outputFile + ".scores"
f = open(scoreFile, "w")
f.write("Chromosome,Position,Score,MARF,MAF,Second_Position\n")
for i in range(0, len(positions_list)):
chromosome = chromosome_list[i]
(pos1, pos2) = positions_list[i]
score = scores_list[i]
marf = marfs[i]
maf = mafs[i]
l = map(str, [chromosome, pos1, score, marf, maf, pos2])
f.write(",".join(l) + "\n")
f.close()
def _plotKW_():
"""
Analyze how population structure affects KW.
"""
filterProb = 0.1
p_i = 1
res_dir = "/Users/bjarni/tmp/"
runId = "_full_quick_"
snpsDataFile = "/Network/Data/250k/dataFreeze_080608/250K_f10_080608.csv"
snpsds = dataParsers.parseCSVData(snpsDataFile, format=1, deliminator=",") # ,debug=True)
phenotypeFile = "/Network/Data/250k/dataFreeze_080608/phenotypes_all_raw_111008.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter="\t")
snpsdata.coordinateSnpsAndPhenotypeData(phed, p_i, snpsds)
totalSNPs = []
for i in range(len(snpsds)):
snpsds[i] = snpsds[i].getSnpsData()
totalSNPs += snpsds[i].snps
# For memory, remove random SNPs
snps = []
for snp in totalSNPs:
if random.random() < filterProb:
snps.append(snp)
totalSNPs = snps
# globalKinship = calcKinship(totalSNPs)
gc.collect() # Calling garbage collector, in an attempt to clean up memory..
# chr = 1
# for snpsd in snpsds:
snpsd = snpsds[3]
k = calcKinship(snpsd.snps[200:1400])
res = runEmma(phed, p_i, k, snpsd.snps[200:1400]) # runEmma(phed,p_i,k,snps):
pvals = res["ps"]
log_pvals = []
for pval in pvals:
# print pval
log_pvals.append(-math.log10(pval))
pylab.plot(snpsd.positions[200:1400], log_pvals, "c.", label="Emma (local)")
k = calcKinship(totalSNPs)
res = runEmma(phed, p_i, k, snpsd.snps[200:1400]) # runEmma(phed,p_i,k,snps):
pvals = res["ps"]
log_pvals = []
for pval in pvals:
# print pval
log_pvals.append(-math.log10(pval))
pylab.plot(snpsd.positions[200:1400], log_pvals, "g.", label="Emma (global)")
phenVals = phed.getPhenVals(p_i)
pvals = _run_kw_(snpsd.snps[200:1400], phenVals)
log_pvals = []
for pval in pvals:
# print pval
log_pvals.append(-math.log10(pval))
pylab.plot(snpsd.positions[200:1400], log_pvals, "r.", label="KW (full data)")
(pvals, new_positions, acc_groups) = get_KW_pvals(
snpsd.snps[200:1400], snpsd.positions[200:1400], phed, p_i, kinshipThreshold=0.95, method="KW"
)
ecot_map = phenotypeData._getEcotypeIdToStockParentDict_()
for i in range(0, len(acc_groups)):
acc_list = []
for a_i in acc_groups[i]:
e_i = snpsd.accessions[a_i]
# print e_i
acc_list.append(ecot_map[int(e_i)][0])
print "group", i, ":", acc_list
log_pvals = []
for pval in pvals:
# print pval
log_pvals.append(-math.log10(pval))
pylab.plot(new_positions, log_pvals, "b.", label="KW (merged data)")
pylab.legend(numpoints=2, handlelen=0.005)
pylab.show()
def _run_():
import sys
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = [
"delim=",
"missingval=",
"phenotypeFileType=",
"help",
"parallel=",
"parallelAll",
"candGeneListID=",
"windowSize=",
"testDataFraction=",
"gridSize=",
"phenotypeCategory=",
]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:c:d:m:a:h", long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
phenotypeCategory = 1
phenotypeFileType = 1
testDataFraction = 1.0 / 3.0
gridSize = 6
outFile = None
delim = ","
missingVal = "NA"
help = 0
parallel = None
parallelAll = False
candGeneListID = 129
windowSize = 10000
host = "papaya.usc.edu"
user = "******"
passwd = "bamboo123"
db = "T8_annotation_TH"
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-o"):
outFile = arg
elif opt in ("--gridSize"):
gridSize = int(arg)
elif opt in ("--phenotypeFileType"):
phenotypeFileType = int(arg)
elif opt in ("--phenotypeCategory"):
phenotypeCategory = int(arg)
elif opt in ("--testDataFraction"):
testDataFraction = float(arg)
elif opt in ("--candGeneListID"):
candGeneListID = int(arg)
elif opt in ("--windowSize"):
windowSize = int(arg)
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
else:
if help == 0:
print "Unkown option!!\n"
print __doc__
sys.exit(2)
if len(args) < 1 and not parallel:
if help == 0:
print "Arguments are missing!!\n"
print __doc__
sys.exit(2)
def runParallel(phenotypeIndex):
# Cluster specific parameters
scriptDir = env.scriptDir
resultDir = env.resultDir
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter="\t") # Get Phenotype data
phed.onlyBiologyCategory(phenotypeCategory, host=host, user=user, passwd=passwd)
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outFile = resultDir + "CS_" + parallel + "_" + phenName
shstr = """#!/bin/csh
#PBS -l walltime=72:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
shstr += "#PBS -N CS" + phenName + "_" + parallel + "\n"
shstr += "(python " + scriptDir + "compositeScore.py -o" + outFile + " "
shstr += (
"--candGeneListID="
+ str(candGeneListID)
+ " --testDataFraction="
+ str(testDataFraction)
+ " --gridSize="
+ str(gridSize)
+ " --windowSize="
+ str(windowSize)
+ " --phenotypeCategory="
+ str(phenotypeCategory)
+ " "
+ str(phenotypeIndex)
+ " "
)
shstr += "> " + outFile + "_job" + ".out) >& " + outFile + "_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
phed.onlyBiologyCategory(phenotypeCategory, host=host, user=user, passwd=passwd)
for phenotypeIndex in phed.phenIds:
runParallel(phenotypeIndex)
else:
for arg in args:
runParallel(int(arg))
return
else:
phenotype = int(args[0])
if len(args) > 1:
print "Warning multiple phenotype_id arguments were ignored (use --parallel)."
print "compositeScore is being set up with the following parameters:"
print "candGeneListID:", candGeneListID
print "phenotypeCategory:", phenotypeCategory
print "phenotype:", phenotype
print "gridSize:", gridSize
print "testDataFraction:", testDataFraction
print "phenotypeFileType:", phenotypeFileType
print "parallel:", parallel
print "parallelAll:", parallelAll
print "delim:", delim
print "missingval:", missingVal
print ""
# Now the algorithm!!!
# Load phenotype file
categoricalNames = [
"158_Sil_length_16",
"159_Sil_length_22",
"161_Germ_10",
"163_Germ_22",
"173_Leaf_serr_10",
"174_Leaf_serr_16",
"175_Leaf_serr_22",
"179_Roset_erect_22",
"180_Chlor_16",
"181_Chlor_22",
]
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter="\t") # Get Phenotype data
phed.onlyBiologyCategory(phenotypeCategory, host=host, user=user, passwd=passwd)
# Check whether phenotype is quantitative..
isQuantitative = not (phed.isBinary(phenotype) or phed.getPhenotypeName(phenotype) in categoricalNames)
if isQuantitative:
print "Phenotype", phed.getPhenotypeName(phenotype), "is quantitaive."
phenName = phed.getPhenotypeName(phenotype)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
# Load the result files:
results = []
resultFiles = []
mafCutoff = max(mafCutoffs)
for j in range(0, len(methods)):
if isQuantitative or not onlyQuantitative[j]:
resultFile = resultsDirs[j] + methods[j] + "_" + datasetNames[j] + "_" + phenName + fileTypes[j]
resultFiles.append(resultFile)
print "Loading result file", resultFile
result = gwaResults.Result(resultFile)
if logTransform[j]:
print "Log transformed the p-values"
result.negLogTransform()
result.filterMAF(minMaf=mafCutoff)
results.append(result)
# Write the results to a file.
import tempfile
# if os.getenv("USER")=="bjarni"
# tempfile.tempdir='/tmp'
# tempfile.tempdir='/home/cmb-01/bvilhjal/tmp'
(fId, resultsTempFile) = tempfile.mkstemp()
os.close(fId)
f = open(resultsTempFile, "w")
for i in range(0, len(results[0].scores)):
out_str = str(results[0].chromosomes[i]) + "_" + str(results[0].positions[i])
for result in results:
out_str += "," + str(result.scores[i])
out_str += "\n"
f.write(out_str)
f.close()
# Load cand. gene list.
print "Connecting to db, host=" + host
if not user:
import sys
sys.stdout.write("Username: "******"Error %d: %s" % (e.args[0], e.args[1])
sys.exit(1)
def _run_():
import sys
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = [
"delim=", "missingval=", "phenotypeFileType=", "help", "parallel=",
"parallelAll", "candGeneListID=", "windowSize=", "testDataFraction=",
"gridSize=", "phenotypeCategory="
]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:c:d:m:a:h",
long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
phenotypeCategory = 1
phenotypeFileType = 1
testDataFraction = 1.0 / 3.0
gridSize = 6
outFile = None
delim = ","
missingVal = "NA"
help = 0
parallel = None
parallelAll = False
candGeneListID = 129
windowSize = 10000
host = "papaya.usc.edu"
user = "******"
passwd = "bamboo123"
db = "T8_annotation_TH"
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-o"):
outFile = arg
elif opt in ("--gridSize"):
gridSize = int(arg)
elif opt in ("--phenotypeFileType"):
phenotypeFileType = int(arg)
elif opt in ("--phenotypeCategory"):
phenotypeCategory = int(arg)
elif opt in ("--testDataFraction"):
testDataFraction = float(arg)
elif opt in ("--candGeneListID"):
candGeneListID = int(arg)
elif opt in ("--windowSize"):
windowSize = int(arg)
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
else:
if help == 0:
print "Unkown option!!\n"
print __doc__
sys.exit(2)
if len(args) < 1 and not parallel:
if help == 0:
print "Arguments are missing!!\n"
print __doc__
sys.exit(2)
def runParallel(phenotypeIndex):
#Cluster specific parameters
scriptDir = env.scriptDir
resultDir = env.resultDir
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
phed.onlyBiologyCategory(phenotypeCategory,
host=host,
user=user,
passwd=passwd)
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outFile = resultDir + "CS_" + parallel + "_" + phenName
shstr = """#!/bin/csh
#PBS -l walltime=72:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
shstr += "#PBS -N CS" + phenName + "_" + parallel + "\n"
shstr += "(python " + scriptDir + "compositeScore.py -o" + outFile + " "
shstr += "--candGeneListID=" + str(
candGeneListID) + " --testDataFraction=" + str(
testDataFraction
) + " --gridSize=" + str(gridSize) + " --windowSize=" + str(
windowSize) + " --phenotypeCategory=" + str(
phenotypeCategory) + " " + str(phenotypeIndex) + " "
shstr += "> " + outFile + "_job" + ".out) >& " + outFile + "_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
phed.onlyBiologyCategory(phenotypeCategory,
host=host,
user=user,
passwd=passwd)
for phenotypeIndex in phed.phenIds:
runParallel(phenotypeIndex)
else:
for arg in args:
runParallel(int(arg))
return
else:
phenotype = int(args[0])
if len(args) > 1:
print "Warning multiple phenotype_id arguments were ignored (use --parallel)."
print "compositeScore is being set up with the following parameters:"
print "candGeneListID:", candGeneListID
print "phenotypeCategory:", phenotypeCategory
print "phenotype:", phenotype
print "gridSize:", gridSize
print "testDataFraction:", testDataFraction
print "phenotypeFileType:", phenotypeFileType
print "parallel:", parallel
print "parallelAll:", parallelAll
print "delim:", delim
print "missingval:", missingVal
print ""
#Now the algorithm!!!
#Load phenotype file
categoricalNames = [
"158_Sil_length_16", "159_Sil_length_22", "161_Germ_10", "163_Germ_22",
"173_Leaf_serr_10", "174_Leaf_serr_16", "175_Leaf_serr_22",
"179_Roset_erect_22", "180_Chlor_16", "181_Chlor_22"
]
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile,
delimiter='\t') #Get Phenotype data
phed.onlyBiologyCategory(phenotypeCategory,
host=host,
user=user,
passwd=passwd)
#Check whether phenotype is quantitative..
isQuantitative = not (phed.isBinary(phenotype) or
phed.getPhenotypeName(phenotype) in categoricalNames)
if isQuantitative:
print "Phenotype", phed.getPhenotypeName(phenotype), "is quantitaive."
phenName = phed.getPhenotypeName(phenotype)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
#Load the result files:
results = []
resultFiles = []
mafCutoff = max(mafCutoffs)
for j in range(0, len(methods)):
if isQuantitative or not onlyQuantitative[j]:
resultFile = resultsDirs[j] + methods[j] + "_" + datasetNames[
j] + "_" + phenName + fileTypes[j]
resultFiles.append(resultFile)
print "Loading result file", resultFile
result = gwaResults.Result(resultFile, )
if logTransform[j]:
print "Log transformed the p-values"
result.negLogTransform()
result.filterMAF(minMaf=mafCutoff)
results.append(result)
#Write the results to a file.
import tempfile
#if os.getenv("USER")=="bjarni"
# tempfile.tempdir='/tmp'
#tempfile.tempdir='/home/cmb-01/bvilhjal/tmp'
(fId, resultsTempFile) = tempfile.mkstemp()
os.close(fId)
f = open(resultsTempFile, 'w')
for i in range(0, len(results[0].scores)):
out_str = str(results[0].chromosomes[i]) + "_" + str(
results[0].positions[i])
for result in results:
out_str += "," + str(result.scores[i])
out_str += "\n"
f.write(out_str)
f.close()
#Load cand. gene list.
print "Connecting to db, host=" + host
if not user:
import sys
sys.stdout.write("Username: "******"Error %d: %s" % (e.args[0], e.args[1])
sys.exit(1)
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = [
"chunkSize=", "nTrees=", "impFile=", "delim=", "missingval=",
"withArrayId=", "logTransform", "phenotypeFileType=", "help",
"parallel=", "parallelAll", "nodeSize=", "mem=", "round2Size=",
"secondRound", "minMAF="
]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:d:m:a:h",
long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
phenotypeFileType = 1
impFile = None
delim = ","
missingVal = "NA"
help = 0
withArrayIds = 1
parallel = None
logTransform = False
parallelAll = False
chunkSize = 250000
round2Size = 5000
nTrees = 15000
nodeSize = None
mem = "8g"
skipSecondRound = True
minMAF = 0.0
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-a", "--withArrayId"):
withArrayIds = int(arg)
elif opt in ("-o", "--rFile"):
impFile = arg
elif opt in ("--phenotypeFileType"):
phenotypeFileType = int(arg)
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("--logTransform"):
logTransform = True
elif opt in ("--secondRound"):
skipSecondRound = False
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("--chunkSize"):
chunkSize = int(arg)
elif opt in ("--round2Size"):
round2Size = int(arg)
elif opt in ("--nTrees"):
nTrees = int(arg)
elif opt in ("--nodeSize"):
nodeSize = int(arg)
elif opt in ("--mem"):
mem = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
elif opt in ("-m", "--minMAF"):
minMAF = float(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)
def runParallel(phenotypeIndex):
#Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
impFileName = resultDir + "RF_" + parallel + "_" + phenName
outFileName = impFileName
shstr = """#!/bin/csh
#PBS -l walltime=50:00:00
"""
shstr += "#PBS -l mem=" + mem + "\n"
shstr += """
#PBS -q cmb
"""
shstr += "#PBS -N RF" + phenName + "_" + parallel + "\n"
shstr += "(python " + programDir + "RandomForest.py -o " + impFileName + " --chunkSize " + str(
chunkSize) + " --nTrees " + str(nTrees) + " --mem " + str(
mem) + " --round2Size " + str(round2Size) + ""
if nodeSize:
shstr += " --nodeSize " + str(nodeSize) + " "
if logTransform:
shstr += " --logTransform "
if not skipSecondRound:
shstr += " --secondRound "
shstr += " -a " + str(withArrayIds) + " "
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 ")
#Nested function ends
snpsDataFile = args[0]
phenotypeDataFile = args[1]
if parallel: #Running on the cluster..
if parallelAll:
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
for phenotypeIndex in phed.phenIds:
runParallel(phenotypeIndex)
else:
phenotypeIndex = int(args[2])
runParallel(phenotypeIndex)
return
else:
phenotypeIndex = int(args[2])
print "chunkSize:", chunkSize
print "nTrees:", nTrees
print "nodeSize:", nodeSize
print "mem:", mem
print "logTransform:", logTransform
print "round2Size:", round2Size
print "skipSecondRound:", skipSecondRound
#Loading genotype data
import dataParsers
snpsds = dataParsers.parseCSVData(snpsDataFile,
format=1,
deliminator=delim,
missingVal=missingVal,
withArrayIds=withArrayIds)
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile,
delimiter='\t') #Get Phenotype data
phenotype = phed.getPhenIndex(phenotypeIndex)
accIndicesToKeep = []
phenAccIndicesToKeep = []
numAcc = len(snpsds[0].accessions)
#Load phenotype file
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)
#Log-transforming
if logTransform:
print "Log transforming phenotype"
phed.logTransform(phenotype)
#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)
#Converting format to 01
import snpsdata
newSnpsds = []
sys.stdout.write("Converting data format")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
newSnpsds.append(snpsd.getSnpsData())
print ""
snpsds = newSnpsds
#Writing files
import tempfile
if env.user == "bjarni":
tempfile.tempdir = '/tmp'
(fId, phenotypeTempFile) = tempfile.mkstemp()
os.close(fId)
(fId, genotypeTempFile) = tempfile.mkstemp()
os.close(fId)
phed.writeToFile(phenotypeTempFile, [phenotype])
sys.stdout.write("Phenotype file written\n")
sys.stdout.flush()
#Retain only the correct runchunk of data.
chromasomes = []
positions = []
snps = []
for i in range(0, len(snpsds)):
snpsd = snpsds[i]
positions += snpsd.positions
snps += snpsd.snps
chrList = [i + 1] * len(snpsd.positions)
chromasomes += chrList
#Is the phenotype binary?
binary = phed.isBinary(phenotypeIndex)
import util
impFile = impFile + ".imp"
rDataFile = impFile + ".rData"
rFile = impFile + ".r"
outRfile = rFile + ".out"
errRfile = rFile + ".err"
topImpFile = impFile + "_top" + str(chunkSize) + ".imp"
topRDataFile = impFile + "_top.rData"
try:
os.remove(impFile) #Removing file if it already exits.
except Exception:
print "Couldn't remove", impFile
try:
os.remove(topImpFile) #Removing file if it already exits.
except Exception:
print "Couldn't remove", topImpFile
for startIndex in range(0, len(positions), chunkSize):
if startIndex + chunkSize >= len(positions):
endIndex = len(positions)
else:
endIndex = startIndex + chunkSize
#Writing genotype data to file.
tmpFile = open(genotypeTempFile, "w")
for i in range(startIndex, endIndex):
outStr = ""
snp = util.valListToStrList(snps[i])
outStr += str(chromasomes[i]) + "," + str(positions[i]) + ","
outStr += ",".join(snp)
outStr += "\n"
tmpFile.write(outStr)
tmpFile.close()
rstr = _generateRScript_(genotypeTempFile,
phenotypeTempFile,
impFile,
rDataFile,
binary=binary,
nTrees=nTrees,
nodeSize=nodeSize)
f = open(rFile, 'w')
f.write(rstr)
f.close()
#outRfile = rFile+"_"+str(startIndex/chunkSize)+".out"
#errRfile = rFile+"_"+str(startIndex/chunkSize)+".err"
print "Running model nr", startIndex / chunkSize, ":"
cmdStr = "(R --vanilla < " + rFile + " > " + outRfile + ") >& " + errRfile
sys.stdout.write(cmdStr + "\n")
sys.stdout.flush()
os.system(cmdStr)
print "Random forest output saved in", impFile
if not skipSecondRound:
#Run on the top 'chunkSize' number of hits.
#loading the R output file.
impF = open(impFile, "r")
lines = impF.readlines()
impF.close()
impList = list()
for i in range(1, len(lines)):
line = lines[i]
line.strip()
l = line.split(",")
impList.append((float(l[2]), l[0], l[1], snps[i]))
impList.sort()
impList.reverse()
#Writing genotype data to file.
tmpFile = open(genotypeTempFile, "w")
for i in range(0, round2Size):
outStr = ""
snp = util.valListToStrList(impList[i][3])
outStr += str(impList[i][1]) + "," + str(impList[i][2]) + ","
outStr += ",".join(snp)
outStr += "\n"
tmpFile.write(outStr)
tmpFile.close()
rstr = _generateRScript_(genotypeTempFile,
phenotypeTempFile,
topImpFile,
topRDataFile,
binary=binary,
nTrees=nTrees,
nodeSize=nodeSize)
f = open(rFile, 'w')
f.write(rstr)
f.close()
print "Running randomForest on the top importance scores:"
cmdStr = "(R --vanilla < " + rFile + " > " + outRfile + ") >& " + errRfile
sys.stdout.write(cmdStr + "\n")
sys.stdout.flush()
os.system(cmdStr)
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 _testQQplot_(includeEmmaInBinary=False,usePvalueFiles=True):
resdir = "/Users/bjarni/tmp/"
#resdir = "/Network/Data/250k/tmp-bvilhjal/phenotype_analyzis/"
#resdir = "/Network/Data/250k/tmp-bvilhjal/qq_plots/"
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_012509.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t')
phed2 = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t')
phenotypeIndices = phenotypeData.categories_2_phenotypes[4]#+phenotypeData.categories_2_phenotypes[2]+phenotypeData.categories_2_phenotypes[3]+phenotypeData.categories_2_phenotypes[4]
#(results_map, resultTypes_map) = _loadData_(phed, phenotypeIndices)
q_pvalues = None
stat_dict = {}
for p_i in phenotypeIndices:
(results_map, resultTypes_map) = _loadData_(phed, [p_i])
#try:
phenName = phed.getPhenotypeName(p_i)
phenNamePrint = " ".join(phenName.split("_")[1:])
print "\nWorking on phenotype",phenName
if usePvalueFiles:
q_pvalues = _getPermPvalues_(phenName)
print len(q_pvalues),"permuted pvalues found"
valCount = phed.countValues(p_i)
print valCount,"values found."
if (not phed.isBinary(p_i)) or includeEmmaInBinary:
histogramFile = resdir + phenName +"_hist.pdf"
histogramFile_png = resdir + phenName +"_hist.png"
drawHistogram(phed, p_i, title = phenNamePrint, pngFile = histogramFile_png)
if phed.logTransform(p_i):
histogramFile = resdir + phenName + "_hist_logTransformed.pdf"
histogramFile_png = resdir + phenName + "_hist_logTransformed.png"
drawHistogram(phed, p_i, title = phenNamePrint, pngFile = histogramFile_png)
elif not phed.isBinary(p_i):
print "adding scaled const."
phed.addSDscaledConstant(p_i)
if phed.logTransform(p_i):
histogramFile = resdir + phenName + "_hist_logTransformed_const.pdf"
histogramFile_png = resdir + phenName + "_hist_logTransformed_const.png"
drawHistogram(phed, p_i, title = phenNamePrint, pngFile = histogramFile_png)
# phed2.naOutliers(p_i,10)
# histogramFile = resdir + phenName + "_hist_noOutliers.pdf"
# histogramFile_png = resdir + phenName + "_hist_noOutliers.png"
# drawHistogram(phed2, p_i, title = phenName, pdfFile = histogramFile, pngFile = histogramFile_png)
# if phed2.logTransform(p_i):
# histogramFile = resdir + phenName + "_hist_logTransformed_noOutliers.pdf"
# histogramFile_png = resdir + phenName + "_hist_logTransformed_noOutliers.png"
# drawHistogram(phed2, p_i, title = phenName, pdfFile = histogramFile, pngFile = histogramFile_png)
results = results_map[p_i]
resultTypes = resultTypes_map[p_i]
qqplotFile = resdir + phenName + "_qqplot.pdf"
qqplotFile_png = resdir + phenName + "_qqplot.png"
s_dict={}
(As,Ms)=drawQQPlot(results, 1000, phenName = phenNamePrint, resultTypes = resultTypes, pngFile=qqplotFile_png, perm_pvalues = q_pvalues)
s_dict["A"]=As
s_dict["M"]=Ms
qqplotFile = resdir + phenName + "_qqplot_log.pdf"
qqplotFile_png = resdir + phenName + "_qqplot_log.png"
(ds,areas,slopes) = drawLogQQPlot(results, 1000,5, phenName = phenNamePrint, resultTypes = resultTypes, pngFile=qqplotFile_png, perm_pvalues = q_pvalues)
s_dict["A2"]=areas
s_dict["D"]=ds
s_dict["S"]=slopes
stat_dict[p_i] = s_dict
for i in range(0,len(results)):
result = results[i]
result.negLogTransform()
pngFile = resdir + phenName + "_gwplot_" +resultTypes[i]+".png"
plotResults.plotResult(result,pngFile=pngFile,percentile=90,type="pvals", plotBonferroni=True)
#except Exception:
# print "\nPhenotype index", p_i, "failed."
del results_map
gc.collect() #Calling garbage collector, in an attempt to clean up memory..
print stat_dict
stat_file_name = resdir + "confounding_stat_4.txt"
f = open(stat_file_name,"w")
methods = ["KW","Emma"]
f.write("phenotype_name, method_name, is_binary, D, A, B, M, S\n")
for p_i in phenotypeIndices:
if stat_dict.has_key(p_i):
s_dict = stat_dict[p_i]
phenName = phed.getPhenotypeName(p_i)
phenName = " ".join(phenName.split("_")[1:])
for i in range(0,len(methods)):
st = phenName+", "+methods[i]+", "+str(phed.isBinary(p_i))+", "+str(s_dict["D"][i])+", "+str(s_dict["A"][i])+", "+str(s_dict["A2"][i])+", "+str(s_dict["M"][i])+", "+str(s_dict["S"][i])+"\n"
f.write(st)
f.close()
def _plotKinshipDiffs_():
filterProb = 0.2
p_i = 1
res_dir = "/Users/bjarni/tmp/"
runId = "full_"
snpsDataFile = "/Network/Data/250k/dataFreeze_080608/250K_f10_080608.csv"
snpsds = dataParsers.parseCSVData(snpsDataFile, format=1,
deliminator=",") #,debug=True)
phenotypeFile = "/Network/Data/250k/dataFreeze_080608/phenotypes_all_raw_111008.tsv"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter='\t')
snpsdata.coordinateSnpsAndPhenotypeData(phed, p_i, snpsds)
for snpsd in snpsds:
snpsd.filterMinMAF(0.1)
snpsd.filterMonoMorphicSnps()
totalSNPs = []
for i in range(len(snpsds)):
snpsds[i] = snpsds[i].getSnpsData()
totalSNPs += snpsds[i].snps
#For memory, remove random SNPs
snps = []
for snp in totalSNPs:
if random.random() < filterProb:
snps.append(snp)
totalSNPs = snps
print "Calculating the global kinship..."
globalKinship = calcKinship(totalSNPs)
print "done."
normalizedGlobalKinship = globalKinship / mean(globalKinship)
gc.collect(
) #Calling garbage collector, in an attempt to clean up memory..
for i in range(4, 5): #len(snpsds)):
chr = i + 1
snpsd = snpsds[i]
#pylab.subplot(5,1,chr)
# pylab.figure(figsize=(18,4))
# (kinshipDiffs,binPos,local300Kinships) = getKinshipDiffs(snpsd,normalizedGlobalKinship,windowSize=300000)
# pylab.plot(binPos,kinshipDiffs,"r",label='ws$=300000$')
# (kinshipDiffs,binPos,local500Kinships) = getKinshipDiffs(snpsd,normalizedGlobalKinship,windowSize=500000)
# pylab.plot(binPos,kinshipDiffs,"b",label='ws$=500000$')
# pylab.legend(numpoints=2,handlelen=0.005)
# pylab.title("Kinship diff. chr. "+str(chr))
# pylab.savefig(res_dir+runId+"kinshipDiffs_500_300kb_chr"+str(chr)+".pdf",format="pdf")
# pylab.clf()
pylab.figure(figsize=(18, 4))
(emmaDiffs, binPos) = getEmmaDiffs(snpsd,
phed,
p_i,
globalKinship,
windowSize=300000)
pylab.plot(binPos, emmaDiffs, "r", label='ws$=300000$')
pylab.title("Emma avg. p-value diff. 500kb on chr. " + str(chr))
(emmaDiffs, binPos) = getEmmaDiffs(snpsd,
phed,
p_i,
globalKinship,
windowSize=500000)
pylab.plot(binPos, emmaDiffs, "b", label='ws$=500000$')
pylab.title("Emma avg. p-value diff. on chr. " + str(chr))
pylab.legend(numpoints=2, handlelen=0.005)
pylab.savefig(res_dir + runId + "EmmaPvalDiffs_500_300kb_chr" +
str(chr) + ".pdf",
format="pdf")
pylab.clf()
gc.collect(
) #Calling garbage collector, in an attempt to clean up memory..
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = ["outputSNPsFile=","outputPhenotFile=", "filterMonomorphic", "rawDataFormat", "delim=", "missingval=", "withArrayId=", "phenotype=", "phenotypeFile=", "phenotypeName=", "calcKinshipMatrix=", "orderAccessions", "help"]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:u:d:m:a:f:p:h", long_options_list)
except:
traceback.print_exc()
print sys.exc_info()
print __doc__
sys.exit(2)
inputFile = args[0]
output_fname = None
outputPhenotFile = None
delim = ","
missingVal = "NA"
phenotypeFile = None
kinshipMatrixFile = None
phenotype = None
phenotypeName = None
rawDataFormat = False
monomorphic = False
help = 0
withArrayIds = 1
orderAccessions = False
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-a","--withArrayId"):
withArrayIds = int(arg)
elif opt in ("-f","--phenotypeFile"):
phenotypeFile = arg
elif opt in ("calcKinshipMatrix"):
kinshipMatrixFile = arg
elif opt in ("--filterMonomorphic"):
monomorphic = True
elif opt in ("--rawDataFormat"):
rawDataFormat = True
elif opt in ("--minCallProb"):
minCallProb = float(arg)
elif opt in ("-p","--phenotype"):
phenotype = int(arg)
elif opt in ("-o","--outputSNPsFile"):
output_fname = arg
elif opt in ("--orderAccessions"):
orderAccessions = True
elif opt in ("-u","--phenotypeFile"):
outputPhenotFile = arg
elif opt in ("-d","--delim"):
delim = arg
elif opt in ("-m","--missingval"):
missingVal = arg
else:
if help==0:
print "Unkown option!!\n"
print __doc__
sys.exit(2)
if not output_fname:
print output_fname
if help==0:
print "Output file missing!!\n"
print __doc__
sys.exit(2)
waid1 = withArrayIds==1 or withArrayIds==2
waid2 = withArrayIds==2
import dataParsers
snpsds = dataParsers.parseCSVData(inputFile, format=1, deliminator=delim, missingVal=missingVal, withArrayIds=waid1)
if phenotypeFile:
import phenotypeData
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter='\t') #Get Phenotype data
accIndicesToKeep = []
phenAccIndicesToKeep = []
numAcc = len(snpsds[0].accessions)
if phenotype>=0:
#Load phenotype file
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
elif phenotype==None:
sys.stdout.write("Removing accessions which do not have any phenotype values.")
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:
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."
if outputPhenotFile:
print "Filtering phenotype data."
phed.removeAccessions(phenAccIndicesToKeep)
if orderAccessions:
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)
if phenotype>=0:
phed.writeToFile(outputPhenotFile, [phenotype])
else:
phed.writeToFile(outputPhenotFile)
#Filtering monomorphic
if monomorphic:
print "Filtering monomorphic SNPs"
for snpsd in snpsds:
print "Removed", str(snpsd.filterMonoMorphicSnps()),"Snps"
import snpsdata
newSnpsds = []
if not rawDataFormat:
sys.stdout.write("Converting data format")
for snpsd in snpsds:
sys.stdout.write(".")
sys.stdout.flush()
newSnpsds.append(snpsd.getSnpsData())
print ""
waid1 = 0
snpsDataset = snpsdata.SnpsDataSet(newSnpsds,[1,2,3,4,5])
decoder = {1:1, 0:0, -1:'NA'}
else:
snpsDataset = snpsdata.SnpsDataSet(snpsds,[1,2,3,4,5])
decoder=None
snpsDataset.writeToFile(output_fname, deliminator=delim, missingVal = missingVal, withArrayIds = waid1, decoder=decoder)
def _test_():
#Load phenotype data..
import phenotypeData as pd
import gwaResults as gr
phed = pd.readPhenotypeFile('/Users/bjarnivilhjalmsson/Projects/Data/phenotypes/phen_raw_092910.tsv')
pid1 = 1
phed.filter_accessions_w_missing_data(pid1)
phen_name = phed.getPhenotypeName(pid1)
phen_vals = phed.getPhenVals(pid1)
ecotypes = phed.accessions
is_binary = phed.isBinary(pid1)
#Creating the first hdf5 file
hdf5_file_name_1 = '/Users/bjarni.vilhjalmsson/tmp/test1.hdf5'
gwa_record = GWASRecord(hdf5_file_name_1)
gwa_record.init_file()
gwa_record.add_new_phenotype(phen_name, phen_vals, ecotypes, is_binary=is_binary)
print "First file is constructed"
print "Now testing it"
r = gwa_record.get_phenotype_values(phen_name, 'raw')
#print r
phed = pd.readPhenotypeFile('/Users/bjarnivilhjalmsson/Projects/Data/phenotypes/phen_raw_092910.tsv')
pid2 = 5
phed.filter_accessions_w_missing_data(pid2)
phen_name = phed.getPhenotypeName(pid2)
phen_vals = phed.getPhenVals(pid2)
ecotypes = phed.accessions
is_binary = phed.isBinary(pid2)
gwa_record.add_new_phenotype(phen_name, phen_vals, ecotypes, is_binary=is_binary)
print "Now testing it"
r = gwa_record.get_phenotype_values(phen_name, 'raw')
#print r
r = gwa_record.get_phenotype_info(phen_name)
print r
gwa_record.transform_phenotype('FT10', transformation='sqrt')
print "Now testing it"
r = gwa_record.get_phenotype_values(phen_name, 'raw')
#print r
r = gwa_record.get_phenotype_info(phen_name)
print r
result_file = '/Users/bjarnivilhjalmsson/tmp/pi1_pid5_FT10_emmax_none.pvals'
res = gr.Result(result_file=result_file, name='FT10')
res.neg_log_trans()
# for c in ['chromosomes', 'positions', 'scores', 'marfs', 'mafs', 'genotype_var_perc', 'beta0', \
# 'beta1', 'correlations']:
# print c, res.snp_results[c][:10]
gwa_record.add_results(phen_name, 'emmax', res.snp_results['chromosomes'], res.snp_results['positions'],
res.scores, res.snp_results['marfs'], res.snp_results['mafs'],
transformation='raw', genotype_var_perc=res.snp_results['genotype_var_perc'],
beta0=res.snp_results['beta0'], beta1=res.snp_results['beta1'],
correlation=res.snp_results['correlations'])
print "Result added."
print "Now fetching a result."
res = gwa_record.get_results(phen_name, 'emmax')#, min_mac=15, max_pval=0.01)
print "Result loaded"
# for c in ['chromosome', 'position', 'score', 'maf', 'mac', 'genotype_var_perc', 'beta0', \
# 'beta1', 'correlation']:
# print c, res[c][:10]
r = gwa_record.get_phenotype_info()
print r
s1 = time.time()
res = gwa_record.get_results_by_chromosome(phen_name, 'emmax')
print "Result re-loaded"
secs = time.time() - s1
if secs > 60:
mins = int(secs) / 60
secs = secs - mins * 60
print 'Took %d mins and %f seconds.' % (mins, secs)
else:
print 'Took %f seconds.' % (secs)
for chromosome in [1, 2, 3, 4, 5]:
for c in ['position', 'score', 'maf', 'mac', 'genotype_var_perc', 'beta0', \
'beta1', 'correlation']:
print c, res[chromosome][c][:10]
print res['chromosome_ends']
print res['max_score']
print gwa_record.get_phenotype_bins(phen_name)
s1 = time.time()
gwa_record.perform_gwas('LD', analysis_method='kw')
secs = time.time() - s1
if secs > 60:
mins = int(secs) / 60
secs = secs - mins * 60
print 'Took %d mins and %f seconds.' % (mins, secs)
else:
print 'Took %f seconds.' % (secs)
gwa_record.transform_phenotype('LD', transformation='log')
gwa_record.perform_gwas('LD', analysis_method='emmax', transformation='log')
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = [
"outputFile=", "delim=", "missingval=", "sampleNum=", "parallel=",
"parallelAll", "useFloats"
]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:d:m:n: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
withArrayIds = 1
parallel = None
parallelAll = False
sampleNum = None
chromosomes = [1, 2, 3, 4, 5]
useFloats = False
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-o", "--outputFile"):
outputFile = arg
elif opt in ("--parallel"):
parallel = arg
elif opt in ("--parallelAll"):
parallelAll = True
elif opt in ("-d", "--delim"):
delim = arg
elif opt in ("-m", "--missingval"):
missingVal = arg
elif opt in ("n", "--sampleNum"):
sampleNum = int(arg)
elif opt in ("--useFloats"):
useFloats = True
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 "CAMP is being set up with the following parameters:"
print "phenotypeDataFile:", phenotypeDataFile
if len(args) > 2:
print "Phenotype_id:", args[2]
print "snpsDataFile:", snpsDataFile
print "parallel:", parallel
print "parallelAll:", parallelAll
print "sampleNum:", sampleNum
def runParallel(phenotypeIndex, id=""):
#Cluster specific parameters
phed = phenotypeData.readPhenotypeFile(
phenotypeDataFile, delimiter='\t') #Get Phenotype data
phenName = phed.getPhenotypeName(phenotypeIndex)
phenName = phenName.replace("/", "_div_")
phenName = phenName.replace("*", "_star_")
outputFile = resultDir + "CAMP_" + parallel + "_" + phenName + id
shstr = """#!/bin/csh
#PBS -l walltime=24:00:00
#PBS -l mem=6g
#PBS -q cmb
"""
shstr += "#PBS -N C" + phenName + "_" + parallel + "\n"
shstr += "set phenotypeName=" + parallel + "\n"
shstr += "set phenotype=" + str(phenotypeIndex) + "\n"
shstr += "(python " + scriptDir + "Camp.py -o " + outputFile + " "
if sampleNum:
shstr += " -n " + str(sampleNum) + " "
if useFloats:
shstr += " --useFloats "
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)
else:
phenotypeIndex = int(args[2])
runParallel(phenotypeIndex)
return
else:
phenotypeIndex = int(args[2])
#Load phenotype file
phed = phenotypeData.readPhenotypeFile(phenotypeDataFile,
delimiter='\t') #Get Phenotype data
#Load genotype file
snpsds = dataParsers.parseCSVData(snpsDataFile,
format=1,
deliminator=delim,
missingVal=missingVal,
withArrayIds=withArrayIds)
#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 ""
#Writing phenotype data to CAMP format.
(fId, phenotypeFile) = tempfile.mkstemp()
os.close(fId)
phenVals = phed.getPhenVals(phenotypeIndex, asString=False)
if not useFloats:
phenVals = map(int, phenVals)
phenFile = open(phenotypeFile, "w")
for value in phenVals:
phenFile.write(str(value) + "\n")
phenFile.close()
chromosome_list = []
positions_list = []
scores_list = []
interaction_positions_list = []
mafs = []
marfs = []
#Writing SNP data to CAMP format.
for chromosome in chromosomes:
(fId, snpsFile) = tempfile.mkstemp()
os.close(fId)
(fId, posFile) = tempfile.mkstemp()
os.close(fId)
sf = open(snpsFile, "w")
pf = open(posFile, "w")
snpsd = newSnpsds[chromosome - 1]
for i in range(0, len(snpsd.snps)):
snp = snpsd.snps[i]
(marf, maf) = snpsdata.getMAF(snp)
marfs.append(marf)
mafs.append(maf)
str_snp = map(str, snp)
double_snp = []
for nt in str_snp:
double_snp.append(nt)
double_snp.append(nt)
sf.write("".join(double_snp) + "\n")
pf.write(str(snpsd.positions[i]) + "\n")
sf.close()
pf.close()
outFile = outputFile + "_job_" + str(chromosome) + ".out"
errFile = outputFile + "_job_" + str(chromosome) + ".err"
resFile = outputFile + "_" + str(chromosome) + ".out"
print "resFile,outFile,errFile,snpsFile,posFile,phenotypeFile:", resFile, outFile, errFile, snpsFile, posFile, phenotypeFile
results = _runCAMP_(resFile, outFile, errFile, snpsFile, posFile,
phenotypeFile, sampleNum)
positions_list += results["positions"]
scores_list += results["scores"]
for (i, j) in results["snpIndices"]:
if not (j < 0 or i < 0):
marfs.append(0.5) #An ugly hack!!!
mafs.append(0.5)
chromosome_list.append(chromosome)
scoreFile = outputFile + ".scores"
f = open(scoreFile, "w")
f.write("Chromosome,Position,Score,MARF,MAF,Second_Position\n")
for i in range(0, len(positions_list)):
chromosome = chromosome_list[i]
(pos1, pos2) = positions_list[i]
score = scores_list[i]
marf = marfs[i]
maf = mafs[i]
l = map(str, [chromosome, pos1, score, marf, maf, pos2])
f.write(",".join(l) + "\n")
f.close()
def runParallel(phenotypeIndex,id=""):
#Cluster specific parameters
phed=phenotypeData.readPhenotypeFile(phenotypeDataFile, delimiter = '\t') #Get Phenotype data
phenName=phed.getPhenotypeName(phenotypeIndex)
phenName=phenName.replace("/", "_div_")
phenName=phenName.replace("*", "_star_")
outputFile=resultDir+"KW_"+parallel+"_"+phenName+id
shstr="""#!/bin/csh
#PBS -l walltime=100:00:00
#PBS -l mem=4g
#PBS -q cmb
"""
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+" "
shstr+=" -a "+str(withArrayIds)+" "
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 ")
def _plotConfoundingStats_():
#import pylab as plt
resdir = "/Network/Data/250k/tmp-bvilhjal/perm_tests/"
phenotypeFile = "/Network/Data/250k/dataFreeze_011209/phenotypes_all_raw_012509.tsv"
stat_file_dir = "/Users/bjarni/tmp/"
print "Loading phenotype data"
phed = phenotypeData.readPhenotypeFile(phenotypeFile, delimiter = '\t')
phenotypeIndices = phenotypeData.categories_2_phenotypes[1]+phenotypeData.categories_2_phenotypes[2]+phenotypeData.categories_2_phenotypes[3]+phenotypeData.categories_2_phenotypes[4]
m_pvals = {}
a_pvals = {}
ks_pvals = {}
for p_i in phenotypeIndices:
#if not phed.isBinary(p_i):
phenName = phed.getPhenotypeName(p_i)
print "Loading permutation stats data for",phenName
filename = resdir+"KW_perm_f1_n1000_"+phenName+".perm.stat.txt"
f = open(filename,"r")
lines = f.readlines()
pvals = (lines[-1].strip()).split(',')
m_pvals[p_i] = float(pvals[0].split(" ")[-1])
a_pvals[p_i] = float(pvals[1])
ks_pvals[p_i] = float(pvals[2])
x_ticks = []
s_ticks = []
x_pos = 1
for cat in [1,2,3,4]:
for p_i in phenotypeData.categories_2_phenotypes[cat]:
s_ticks.append(phed.getPhenotypeName(p_i))
#plt.text(x_pos+shift,min_stat-0.1*stat_range,p_i,rotation="vertical",size="xx-small")
x_ticks.append(x_pos-0.5)
x_pos += 1
x_pos = x_pos+1
figure = plt.figure(figsize=(14,8))
axes = plt.Axes(figure, [.06,.16,.91,.81])
figure.add_axes(axes)
x_pos = 0
colors = {1:"b",2:"r",3:"g",4:"c"}
for i in [1,2,3,4]:
phenotypeIndices = phenotypeData.categories_2_phenotypes[i]
newPhenotypeIndices = []
for p_i in phenotypeIndices:
#if not phed.isBinary(p_i):
newPhenotypeIndices.append(p_i)
phenotypeIndices = newPhenotypeIndices
m_list = []
for p_i in phenotypeIndices:
m_list.append(m_pvals[p_i])
plt.bar(range(x_pos,len(m_list)+x_pos),m_list,color = colors[i])
x_pos = x_pos+len(m_list)+1
plt.axis([0-0.02*(x_pos-1),1.02*(x_pos-1),-0.02,1.02])
plt.xticks(x_ticks,s_ticks,size="x-small",rotation="vertical")
plt.ylabel("M stat. p-value")
plt.savefig(stat_file_dir+"confounding_M_pvalues.png", format = "png")
plt.clf()
figure = plt.figure(figsize=(14,8))
axes = plt.Axes(figure, [.06,.16,.91,.81])
figure.add_axes(axes)
x_pos = 0
for i in [1,2,3,4]:
phenotypeIndices = phenotypeData.categories_2_phenotypes[i]
newPhenotypeIndices = []
for p_i in phenotypeIndices:
#if not phed.isBinary(p_i):
newPhenotypeIndices.append(p_i)
phenotypeIndices = newPhenotypeIndices
a_list = []
for p_i in phenotypeIndices:
a_list.append(a_pvals[p_i])
plt.bar(range(x_pos,len(a_list)+x_pos),a_list,color = colors[i])
x_pos = x_pos+len(a_list)+1
plt.axis([0-0.02*(x_pos-1),1.02*(x_pos-1),-0.02,1.02])
plt.xticks(x_ticks,s_ticks,size="x-small",rotation="vertical")
plt.ylabel("A stat. p-value")
plt.savefig(stat_file_dir+"confounding_A_pvalues.png", format = "png")
plt.clf()
figure = plt.figure(figsize=(14,8))
axes = plt.Axes(figure, [.06,.16,.91,.81])
figure.add_axes(axes)
x_pos = 0
for i in [1,2,3,4]:
phenotypeIndices = phenotypeData.categories_2_phenotypes[i]
newPhenotypeIndices = []
for p_i in phenotypeIndices:
#if not phed.isBinary(p_i):
newPhenotypeIndices.append(p_i)
phenotypeIndices = newPhenotypeIndices
a_list = []
for p_i in phenotypeIndices:
a_list.append(ks_pvals[p_i])
plt.bar(range(x_pos,len(a_list)+x_pos),a_list,color = colors[i])
x_pos = x_pos+len(a_list)+1
plt.axis([0-0.02*(x_pos-1),1.02*(x_pos-1),-0.02,1.02])
plt.xticks(x_ticks,s_ticks,size="x-small",rotation="vertical")
plt.ylabel("KS stat. p-value")
plt.savefig(stat_file_dir+"confounding_KS_pvalues.png", format = "png")
plt.clf()
print m_pvals, a_pvals, ks_pvals
def analyzeSNPs():
import KW, phenotype_parsers, phenotypeData
import Emma
result_id = "filtered_imputed"
data_dir = "/Users/bjarnivilhjalmsson/Projects/FLC_analysis/"
#ref_seq_name = "2010_Col-0"
ref_seq_name = "raw_ref_col-0"
ref_start = 3170501
ref_chr = 5
#ad_2010 = sequences.readFastaAlignment(data_dir+"FLC_full_edited_merged.aln.fasta",ref_seq_name=ref_seq_name,ref_start=ref_start,
# ref_chr=ref_chr,alignment_type="muscle",ref_direction=1)
#ad_2010 = sequences.readFastaAlignment(data_dir+"FLC_full_merged.aln.fasta",ref_seq_name=ref_seq_name,ref_start=ref_start,
# ref_chr=ref_chr,alignment_type="muscle",ref_direction=1)
#ad = sequences.readFastaAlignment(data_dir+"flc_seqs_aln_merged_011810.fasta",ref_seq_name=ref_seq_name,ref_start=ref_start,
# ref_chr=ref_chr,alignment_type="muscle",ref_direction=1)
#r = ad.get_snps(type=1)
#seq_snpsd = r['snpsd']
#seq_snpsd = seq_snpsd.getSnpsData(missingVal='NA')
#seq_snpsd.onlyBinarySnps()
#i_snpsd = r['indels']
#print indels
#i_snpsd = i_snpsd.getSnpsData(missingVal='NA')
#print zip(i_snpsd.positions, i_snpsd.snps)
#print i_snpsd.accessionsl
seq_snpsd = dataParsers.parseCSVData(
data_dir + "/flc_seqs_aln_imputed_snps_012510.csv")[0]
seq_snpsd = seq_snpsd.getSnpsData(missingVal='NA')
# d2010_file = "/Users/bjarnivilhjalmsson/Projects/Data/2010/2010_073009.csv"
d2010_file = "/Users/bjarnivilhjalmsson/Projects/Data/2010/2010_imputed_012610.csv"
d2010_sd = dataParsers.parse_snp_data(d2010_file, id="2010_data")
# d2010_sd.filter_na_accessions()
d2010_sd.filter_na_snps()
d2010_sd.convert_2_binary()
d2010_sd.filter_maf_snps(0.05)
#kinship_2010 = Emma.calcKinship(d2010_sd.getSnps(0.05))
d2010_sd = d2010_sd.get_region_snpsd(5, 3140000, 3220000)
d2010_sd.remove_redundant_snps(w_missing=True)
d250k_file = "/Users/bjarnivilhjalmsson/Projects/Data/250k/250K_data_t43_081009.csv"
snpsd = dataParsers.parse_snp_data(d250k_file)
snpsd.filter_accessions(seq_snpsd.accessions)
snpsd.convert_2_binary()
snpsd.filter_maf_snps(0.05)
#kinship_250k = Emma.calcKinship(snpsd.getSnps(0.02))
snpsd = snpsd.get_region_snpsd(5, 3140000, 3220000)
snpsd.remove_redundant_snps()
seq_snpsd.remove_accessions(snpsd.accessions)
seq_snpsd.snpsFilterRare(0.05)
seq_snpsd.onlyBinarySnps()
acc_map = []
for i, acc in enumerate(seq_snpsd.accessions):
acc_map.append((i, snpsd.accessions.index(acc)))
seq_snpsd.orderAccessions(acc_map)
seq_snpsd.remove_redundant_snps(w_missing=True)
#snpsd.mergeDataUnion(d2010_sd,priority=2,unionType=3)
#ad.compare_with_snps_data(snpsd) #Something missing here snpsd...?
#i_snpsd =
#snpsd.mergeDataUnion(d250k_sd,unionType=3,verbose=True)
#NOW PERFORM GWAS AND PLOT RESULT!!!!
phend = phenotypeData.readPhenotypeFile(
"/Users/bjarnivilhjalmsson/Projects/Data/phenotypes/FLC_phenotypes_011710.tsv"
)
#phenotype_parsers.load_phentoype_file("/Users/bjarnivilhjalmsson/Projects/FLC_analysis/data_102509/FLC_soil_data_102509.csv")
results_colors = ['blue', 'green', 'red']
#kinship_matrices = [kinship_250k,kinship_250k,kinship_2010]
snpsds = [snpsd, seq_snpsd, d2010_sd]
phenotypeIndices = phend.phenIds
log_transforms = [1, 2]
import analyzePhenotype as ap
import analyzeSNPResult as asr
import copy
# for i in phenotypeIndices:
# #ap.drawHistogram(phend,i,pdfFile="/Users/bjarnivilhjalmsson/tmp/hist_"+str(phend.getPhenotypeName(i))+".pdf")
# #if i in log_transforms:
# phend.logTransform(i)
# #print "log transforming"
# results = []
# filtered_sds=[]
# for sd,k in zip(snpsds,kinship_matrices):
# new_sd = copy.deepcopy(sd)
# res = Emma.run_emma_w_missing_data(new_sd,phend,i,5,k)
# res.negLogTransform()
# snps_indices_to_keep = res.filterMARF(minMaf=0.1)
# print "Got",len(res.scores),len(res.positions),"p-values from Emma."
# results.append(res)
# #pvals = res.scores
# #positions = res.positions
# #pp = zip(pvals,positions)
# #pp.sort()
# #print pp
# #import plotResults as pr
# #pr.plotResult(res,"/Users/bjarnivilhjalmsson/tmp/test.pdf")
# new_sd.filter_snp_indices(snps_indices_to_keep)
# filtered_sds.append(new_sd)
# import regionPlotter as rp
# reg_plotter = rp.RegionPlotter()
# reg_plotter.plot_small_result(results,results_colors=results_colors,
# pdf_file="/Users/bjarnivilhjalmsson/tmp/seqences_250k_"+result_id+"_emma_gwas_"+str(phend.getPhenotypeName(i))+".pdf")
# for j,(r,sd) in enumerate(zip(results,filtered_sds)):
# r_i = r.scores.index(max(r.scores))
# phend.plot_marker_box_plot(i,sd,r_i,pdf_file="/Users/bjarnivilhjalmsson/tmp/box_plot_emma_"+str(phend.getPhenotypeName(i))+"_"+results_colors[j]+".pdf",marker_score=r.scores[r_i])
#
phend = phenotypeData.readPhenotypeFile(
"/Users/bjarnivilhjalmsson/Projects/Data/phenotypes/FLC_phenotypes_011710.tsv"
) #phenotype_parsers.load_phentoype_file("/Users/bjarnivilhjalmsson/Projects/FLC_analysis/data_102509/FLC_soil_data_102509.csv")
for i in phenotypeIndices:
results = []
filtered_sds = []
for sd in snpsds:
new_sd = copy.deepcopy(sd)
res, f_sd = KW.run_kw(new_sd, phend, i, 5)
filtered_sds.append(f_sd)
res.negLogTransform()
print "Got", len(res.scores), len(
res.positions), "p-values from KW."
results.append(res)
#pvals = res.scores
#positions = res.positions
#pp = zip(pvals,positions)
#pp.sort()
#print pp
#import plotResults as pr
#pr.plotResult(res,"/Users/bjarnivilhjalmsson/tmp/test.pdf")
import regionPlotter as rp
reg_plotter = rp.RegionPlotter()
reg_plotter.plot_small_result(
results,
results_colors=results_colors,
pdf_file="/Users/bjarnivilhjalmsson/tmp/seqences_250k_" +
result_id + "_gwas_" + str(phend.getPhenotypeName(i)) + ".pdf")
for j, (r, sd) in enumerate(zip(results, filtered_sds)):
if len(r.scores) != len(sd.snps):
print "Lengths not equal? %d, %d", (len(r.scores),
len(sd.snps))
r_i = r.scores.index(max(r.scores))
phend.plot_marker_box_plot(
i,
sd,
r_i,
pdf_file="/Users/bjarnivilhjalmsson/tmp/box_plot_kw_" +
str(phend.getPhenotypeName(i)) + "_" + results_colors[j] +
".pdf",
marker_score=r.scores[r_i])
def _run_():
if len(sys.argv) == 1:
print __doc__
sys.exit(2)
long_options_list = ["rFile=","chr=", "delim=", "missingval=", "withArrayId=", "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="]
try:
opts, args = getopt.getopt(sys.argv[1:], "o:c:d:m:a: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
withArrayIds = 1
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
for opt, arg in opts:
if opt in ("-h", "--help"):
help = 1
print __doc__
elif opt in ("-a","--withArrayId"):
withArrayIds = int(arg)
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)
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 "withArrayId:",withArrayIds
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
def runParallel(phenotypeIndex,phed):
#Cluster specific parameters
print phenotypeIndex
phenName = phed.getPhenotypeName(phenotypeIndex)
outFileName = resultDir+"Emma_"+parallel+"_"+phenName
shstr = """#!/bin/csh
#PBS -l walltime=100:00:00
#PBS -l mem=8g
#PBS -q cmb
"""
shstr += "#PBS -N E"+phenName+"_"+parallel+"\n"
shstr += "set phenotypeName="+parallel+"\n"
shstr += "set phenotype="+str(phenotypeIndex)+"\n"
shstr += "(python "+emmadir+"Emma.py -o "+outFileName+" "
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)+" "
shstr += " -a "+str(withArrayIds)+" "
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, withArrayIds=withArrayIds)
#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, withArrayIds=withArrayIds)
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)
#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 (not sr) or (sr and not srSkipFirstRun):
sys.stdout.write("Running Primary Emma.\n")
sys.stdout.flush()
pvalFile = _runEmmaScript_(snpsDataset, kinshipSnpsDataset, phed, phenotypeIndex, rFile, chr=chr, delim=delim, missingVal=missingVal, boundaries=boundaries, lrt=lrt)
res = gwaResults.Result(pvalFile,name="EMMA_"+phenotypeName, phenotypeID=phenotypeIndex)
res.filterMAF()
res.negLogTransform()
pngFile = pvalFile+".png"
plotResults.plotResult(res,pngFile=pngFile,percentile=90,type="pvals",ylab="$-$log$_{10}(p)$", plotBonferroni=True,usePylab=False)
srInput = pvalFile
if sr:
_secondRun_(srOutput,srInput,srTopQuantile,srWindowSize,newSnpsds,phed,phenotypeIndex,kinshipSnpsDataset)
print "Generating second run GW plot."
res = gwaResults.Result(srInput,name="KW_"+phenotypeName, phenotypeID=phenotypeIndex)
res.filterMAF()
res.negLogTransform()
srRes = gwaResults.Result(srOutput,name="EMMA_SR_"+phenotypeName, phenotypeID=phenotypeIndex)
srRes.filterMAF()
srRes.negLogTransform()
srPngFile = pvalFile+".sr.png"
plotResults.plotResultWithSecondRun(res,srRes,pngFile=srPngFile,ylab="$-$log$_{10}(p)$", plotBonferroni=True)
