'%s',
'\t',
header='\t'.join(genomeBins.binNames),
comments='')
# Print Interaction Data
print '\nInteraction Data\n\t%s\n\t%s\n\t%s\n\t%s' % (
'total: %s' % (logArray[0]), 'accepted: %s' %
(logArray[3]), 'no chromosome: %s' % (logArray[1]), 'no bin: %s' %
(logArray[2]))
# Count number of bins above minimum
colSums = countMatrix.sum(axis=0)
binsAboveMin = sum(colSums >= args.minCount)
# Print bin data
print '\nBin Data\n\t%s\n\t%s\n\t%s\n\t%s' % (
'total: %s' % (len(colSums)), 'bins above minimum: %s' %
(binsAboveMin), 'mean count: %.0f' %
(np.mean(colSums)), 'median count: %.0f' % (np.median(colSums)))
if binsAboveMin > 0:
# Normalise matrix
normMatrix, biasData = interactionMatrix.normaliseMatrix(
countMatrix, args.minCount)
# Save interactions
np.savetxt(args.normMatrix,
normMatrix,
'%.6f',
'\t',
header='\t'.join(genomeBins.binNames),
comments='')
np.savetxt(args.biasFile,
np.array([genomeBins.binNames, biasData]).T, '%s', '\t')
# Save interactions
np.savetxt(args.countMatrix, countMatrix, '%s', '\t',
header = '\t'.join(genomeBins.binNames), comments = '')
# Print Interaction Data
print '\nInteraction Data\n\t%s\n\t%s\n\t%s\n\t%s' %(
'total: %s' %(logArray[0]),
'accepted: %s' %(logArray[3]),
'no chromosome: %s' %(logArray[1]),
'no bin: %s' %(logArray[2])
)
# Count number of bins above minimum
colSums = countMatrix.sum(axis=0)
binsAboveMin = sum(colSums >= args.minCount)
# Print bin data
print '\nBin Data\n\t%s\n\t%s\n\t%s\n\t%s' %(
'total: %s' %(len(colSums)),
'bins above minimum: %s' %(binsAboveMin),
'mean count: %.0f' %(np.mean(colSums)),
'median count: %.0f' %(np.median(colSums))
)
if binsAboveMin > 0:
# Normalise matrix
normMatrix, biasData = interactionMatrix.normaliseMatrix(
countMatrix, args.minCount)
# Save interactions
np.savetxt(args.normMatrix, normMatrix, '%.6f', '\t',
header = '\t'.join(genomeBins.binNames), comments = '')
np.savetxt(args.biasFile, np.array([genomeBins.binNames,biasData]).T,
'%s', '\t')
' median bin count: %.0f' %(np.median(colSums))
)
# Extract combined bin data
logData += '\nCombined Samples:\n Bin Data:\n%s\n%s\n' %(
' bin number: %s' %(genomeBins.binCount),
' bins below minimum: %s' %(sum(failedBins))
)
# Save log files
for prefix in prefixList:
with open(prefix + '.matrixLog', 'w') as outFile:
outFile.write(logData)
# Normalise matrices
if sum(failedBins) < genomeBins.binCount:
# Loop through count matrices
for prefix in prefixList:
# Normalise matrix
normMatrix, biasData = interactionMatrix.normaliseMatrix(
prefix + '.countMatrix.gz', failedBins)
# Save normalised interactions
np.savetxt(prefix + '.normMatrix.gz', normMatrix, '%.6f', '\t',
header = '\t'.join(genomeBins.binNames), comments = '')
# Extract bin level data
maskMatrix = analyseInteraction.maskMatrix(prefix + '.normMatrix.gz')
maskMatrix.binDF['bias'] = biasData
maskMatrix.binDirection()
maskMatrix.binDistance()
maskMatrix.binDF.to_csv(prefix + '.binData', '\t', '')
# Extract global data
distance = maskMatrix.combinedDistance(0.1)
np.savetxt(prefix + '.dist.gz', distance, '%s', '\t')
