pValues.append(allScores[o1][i]['pValue'])
return pValues
# Parameters for a run
permutations = 200 # Number of times to run meme, get nMotifs per permutation
pValueThreshold = 0.05 # Threshold for TOMTOM similarity
# TODO!!! These parameters could be gotten from the cMonkey Run
nMotifs = 2
regions = ['upstream', '3pUTR']
motifWidth = { 'upstream': [6, 12], '3pUTR': [4, 9] }
revComp = { 'upstream': True, '3pUTR': None }
# Load up the cMonkey run to get permuted pValues
from cMonkeyWrapper import cMonkeyWrapper
c1 = cMonkeyWrapper('cmonkey-run-hsa.RData')
# To pick the important clusters
maxEValue = 10
maxScore = None # 0
maxSurv = None # 0.05/len(c1.getBiclusterNames())
allPssms = {}
if maxScore==None and maxSurv==None:
allPssms['upstream'] = c1.getPssmsUpstream(maxEValue=maxEValue)
allPssms['3pUTR'] = c1.getPssms3pUTR(maxEValue=maxEValue)
elif maxSurv==None:
allPssms['upstream'] = c1.getPssmsUpstream(maxEValue=maxEValue,maxScore=maxScore)
allPssms['3pUTR'] = c1.getPssms3pUTR(maxEValue=maxEValue,maxScore=maxScore)
else:
allPssms['upstream'] = c1.getPssmsUpstream(maxEValue=maxEValue,maxScore=maxScore,maxSurv=maxSurv)
allPssms['3pUTR'] = c1.getPssms3pUTR(maxEValue=maxEValue,maxScore=maxScore,maxSurv=maxSurv)
# First sort the results
sorted1 = qsortBasedOn(dict1.keys(), dict1.values())[0]
# Then control based on FDR
res1 = []
alpha = float(alpha)
#res1 = [sorted1[i] for i in range(len(sorted1)) if dict1[sorted1[i]] <= alpha/float(tests-i)]
for i in range(len(sorted1)):
if dict1[sorted1[i]] <= alpha/float(tests-i):
res1.append(sorted1[i])
else:
break
return res1
# Load up the cMonkey run to get permuted pValues
from cMonkeyWrapper import cMonkeyWrapper
c1 = cMonkeyWrapper('iter3000.RData')
# Getting
allSeqs = c1.getSeqsUpstream()
bgFile = 'tmp/meme/bgFile.meme'
seed = None
print 'Not using a seed.'
# Get Sequences for the run
if not allSeqs==None:
allSeqs = allSeqs
else:
allSeqs = {}
asf = open(allSeqsFile,'r')
for line in asf.readlines():
splitUp = line.strip().split(',')
