def get_scale_factors(args, statsList, mappedList):
if args.scaleFactors:
scale_factors = list(map(float, args.scaleFactors.split(":")))
elif args.scaleFactorsMethod == 'SES':
scalefactors_dict = estimateScaleFactor(
[args.bamfile1, args.bamfile2],
args.sampleLength,
args.numberOfSamples,
1,
mappingStatsList=mappedList,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose,
chrsToSkip=args.ignoreForNormalization)
scale_factors = scalefactors_dict['size_factors']
if args.verbose:
print("Size factors using SES: {}".format(scale_factors))
print("%s regions of size %s where used " %
(scalefactors_dict['sites_sampled'], args.sampleLength))
print(
"ignoring filtering/blacklists, size factors if the number of mapped "
"reads would have been used:")
print(tuple(float(min(mappedList)) / np.array(mappedList)))
elif args.scaleFactorsMethod == 'readCount':
# change the scaleFactor to 1.0
args.scaleFactor = 1.0
# get num of kept reads for bam file 1
args.bam = args.bamfile1
bam1_mapped, _ = get_num_kept_reads(args, statsList[0])
# get num of kept reads for bam file 2
args.bam = args.bamfile2
bam2_mapped, _ = get_num_kept_reads(args, statsList[1])
mapped_reads = [bam1_mapped, bam2_mapped]
# new scale_factors (relative to min of two bams)
scale_factors = float(min(bam1_mapped,
bam2_mapped)) / np.array(mapped_reads)
if args.verbose:
print("Size factors using total number "
"of mapped reads: {}".format(scale_factors))
elif args.scaleFactorsMethod == 'None':
scale_factors = None
return scale_factors
def get_scale_factors(args, statsList, mappedList):
if args.scaleFactors:
scale_factors = list(map(float, args.scaleFactors.split(":")))
elif args.scaleFactorsMethod == 'SES':
scalefactors_dict = estimateScaleFactor(
[args.bamfile1, args.bamfile2],
args.sampleLength, args.numberOfSamples,
1,
mappingStatsList=mappedList,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose,
chrsToSkip=args.ignoreForNormalization)
scale_factors = scalefactors_dict['size_factors']
if args.verbose:
print("Size factors using SES: {}".format(scale_factors))
print("%s regions of size %s where used " %
(scalefactors_dict['sites_sampled'],
args.sampleLength))
print("ignoring filtering/blacklists, size factors if the number of mapped "
"reads would have been used:")
print(tuple(
float(min(mappedList)) / np.array(mappedList)))
elif args.scaleFactorsMethod == 'readCount':
# change the scaleFactor to 1.0
args.scaleFactor = 1.0
# get num of kept reads for bam file 1
args.bam = args.bamfile1
bam1_mapped, _ = get_num_kept_reads(args, statsList[0])
# get num of kept reads for bam file 2
args.bam = args.bamfile2
bam2_mapped, _ = get_num_kept_reads(args, statsList[1])
mapped_reads = [bam1_mapped, bam2_mapped]
# new scale_factors (relative to min of two bams)
scale_factors = float(min(bam1_mapped, bam2_mapped)) / np.array(mapped_reads)
if args.verbose:
print("Size factors using total number "
"of mapped reads: {}".format(scale_factors))
elif args.scaleFactorsMethod == 'None':
scale_factors = None
return scale_factors
def get_scale_factors(args):
bam1 = bamHandler.openBam(args.bamfile1)
bam2 = bamHandler.openBam(args.bamfile2)
bam1_mapped = parserCommon.bam_total_reads(bam1, args.ignoreForNormalization)
bam2_mapped = parserCommon.bam_total_reads(bam2, args.ignoreForNormalization)
if args.scaleFactors:
scale_factors = map(float, args.scaleFactors.split(":"))
else:
if args.scaleFactorsMethod == 'SES':
scalefactors_dict = estimateScaleFactor(
[bam1.filename, bam2.filename],
args.sampleLength, args.numberOfSamples,
1,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose,
chrsToSkip=args.ignoreForNormalization)
scale_factors = scalefactors_dict['size_factors']
if args.verbose:
print "Size factors using SES: {}".format(scale_factors)
print "%s regions of size %s where used " % \
(scalefactors_dict['sites_sampled'],
args.sampleLength)
print "size factor if the number of mapped " \
"reads would have been used:"
print tuple(
float(min(bam1.mapped, bam2.mapped)) / np.array([bam1.mapped, bam2.mapped]))
elif args.scaleFactorsMethod == 'readCount':
scale_factors = float(min(bam1_mapped, bam2_mapped)) / np.array([bam1_mapped, bam2_mapped])
if args.verbose:
print "Size factors using total number " \
"of mapped reads: {}".format(scale_factors)
# in case the subtract method is used, the final difference
# would be normalized according to the given method
if args.ratio == 'subtract':
# The next lines identify which of the samples is not scaled down.
# The normalization using RPKM or normalize to 1x would use
# as reference such sample. Since the other sample would be
# scaled to match the un-scaled one, the normalization factor
# for both samples should be based on the unscaled one.
# For example, if sample A is unscaled and sample B is scaled by 0.5,
# then normalizing factor for A to report RPKM read counts
# is also applied to B.
if scale_factors[0] == 1:
mappedReads = bam1_mapped
bamfile = args.bamfile1
else:
mappedReads = bam2_mapped
bamfile = args.bamfile2
if args.scaleFactors is None:
if args.normalizeTo1x:
# try to guess fragment length if the bam file contains paired end reads
from deeptools.getFragmentAndReadSize import get_read_and_fragment_length
frag_len_dict, read_len_dict = get_read_and_fragment_length(bamfile,
return_lengths=False,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose)
if args.extendReads:
if args.extendReads is True:
# try to guess fragment length if the bam file contains paired end reads
if frag_len_dict:
fragment_length = frag_len_dict['median']
else:
exit("*ERROR*: library is not paired-end. Please provide an extension length.")
if args.verbose:
print("Fragment length based on paired en data "
"estimated to be {}".format(frag_len_dict['median']))
elif args.extendReads < 1:
exit("*ERROR*: read extension must be bigger than one. Value give: {} ".format(args.extendReads))
elif args.extendReads > 2000:
exit("*ERROR*: read extension must be smaller that 2000. Value give: {} ".format(args.extendReads))
else:
fragment_length = args.extendReads
else:
# set as fragment length the read length
fragment_length = int(read_len_dict['median'])
if args.verbose:
print "Estimated read length is {}".format(int(read_len_dict['median']))
current_coverage = float(mappedReads * fragment_length) / args.normalizeTo1x
# the coverage scale factor is 1 / coverage,
coverage_scale_factor = 1.0 / current_coverage
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print "Estimated current coverage {}".format(current_coverage)
print "Scale factor to convert " \
"current coverage to 1: {}".format(coverage_scale_factor)
else:
# by default normalize using RPKM
# the RPKM is:
# Num reads per tile/(total reads (in millions)*tile length in Kb)
millionReadsMapped = float(mappedReads) / 1e6
tileLengthInKb = float(args.binSize) / 1000
coverage_scale_factor = 1.0 / (millionReadsMapped * tileLengthInKb)
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print "scale factor for "
"RPKM is {0}".format(coverage_scale_factor)
return scale_factors
def correctReadCounts(bamFilesList,
binLength,
numberOfSamples,
defaultFragmentLength,
outFileName,
outFileFormat,
outFileNameCorr=None,
region=None,
extendPairedEnds=True,
numberOfProcessors=1,
Nsigmas=2,
maxSignalRatio=10,
blackListFileName=None,
verbose=False):
bam1 = writeBedGraph.openBam(bamFilesList[0])
bam2 = writeBedGraph.openBam(bamFilesList[1])
treatmentMapped = bam1.mapped
controlMapped = bam2.mapped
treatmentControlRatioMapped = float(treatmentMapped) / controlMapped
# 1. Get a table containing number of reads in a sample from the genome.
# Only regions for which both samples have more than zero counts are considered
scaleFactorsDict = estimateScaleFactor(
bamFilesList,
binLength,
numberOfSamples,
defaultFragmentLength,
1,
blackListFileName=blackListFileName,
numberOfProcessors=numberOfProcessors,
verbose=verbose)
"""
num_reads_per_region = getNumReadsPerBin(bamFilesList, binLength, numberOfSamples, defaultFragmentLength, numberOfProcessors, skipZeros=True, verbose=verbose)
if verbose:
print "number of non-zero regions sampled: {}".format(num_reads_per_region.shape[0])
# 2. get Mean and std of treatment (col1) and control (col2)
treatmentMean, controlMean = np.mean(num_reads_per_region, axis=0) # axis=0: that measn by column
treatmentStd, controlStd = np.std(num_reads_per_region, axis=0)
treatmentTotal, controlTotal = np.sum(num_reads_per_region, axis=0)
# 3. Calculate residual in treatment & control data, at regions for which treatment
# signal exceeds mean + std * Nsigmas
# (these are expected to be the regions at which the signal > mean-signal,
# so the residual signal is positive)
overRows = np.where(num_reads_per_region[:,0].copy() >= treatmentMean + treatmentStd*Nsigmas )[0]
over_Nsigma_regions = num_reads_per_region[overRows, :]
treatmentSigMean, controlSigMean = np.mean(over_Nsigma_regions, axis=0)
treatmentExtraSignal = treatmentSigMean - treatmentMean
controlExtraSignal = controlSigMean - controlMean
treatmentControlRatio = float(treatmentTotal) / controlTotal
adjSignalRatio = maxSignalRatio * treatmentControlRatio;
treatmentSignalRatio = float(treatmentExtraSignal) / controlExtraSignal
if treatmentSignalRatio < adjSignalRatio and treatmentSignalRatio > 0:
treatmentSignalRatio = adjSignalRatio
if treatmentSignalRatio < 1:
raise NameError("estimated signal in control file {} is greater than estimated signal in treatmant file {}. Perhaps the file names are swapped?".format(bamFilesList[0], bamFilesList[1]))
else:
controlSignalRatio = 1.0/treatmentSignalRatio
controlRatio = 1.0 / treatmentControlRatio
"""
# scaleFactors = scaleFactorsDict['size_factors']
treatmentMean, controlMean = scaleFactorsDict['meanSES']
treatmentControlRatio = scaleFactorsDict['size_factors'][
1] / scaleFactorsDict['size_factors'][0]
treatmentSignalRatio = treatmentControlRatio
controlRatio = controlSignalRatio = 1.0 / treatmentControlRatio
treatmentTotal = treatmentMapped
controlTotal = controlMapped
print("Treatment mean: {:.2f}, Treatment total:{:.2f}".format(
treatmentMean, treatmentTotal))
print("Control mean: {:.2f}, Control total:{}".format(
controlMean, controlTotal))
print("the ratio of treatment vs. control for enriched regions is: {:.2f}".
format(treatmentSignalRatio))
print(
"the ratio of treatment vs. control ratio: {:.2f} (if based on mapped reads: {:.2f})"
.format(treatmentControlRatio, treatmentControlRatioMapped))
funcArgs = {
'controlMean': controlMean,
'treatmentMean': treatmentMean,
'controlSignalRatio': controlSignalRatio,
'controlRatio': controlRatio,
'treatmentControlRatio': treatmentControlRatio
}
writeBedGraph.writeBedGraph(bamFilesList,
outFileName,
defaultFragmentLength,
computePvalue,
funcArgs,
tileSize=binLength,
region=region,
format=outFileFormat,
zerosToNans=False,
blackListFileName=blackListFileName,
numberOfProcessors=numberOfProcessors,
extendPairedEnds=extendPairedEnds)
if outFileNameCorr:
writeBedGraph.writeBedGraph(bamFilesList,
outFileNameCorr,
defaultFragmentLength,
computeCorrectedReadcounts,
funcArgs,
tileSize=binLength,
region=region,
format=outFileFormat,
zerosToNans=False,
blackListFileName=blackListFileName,
numberOfProcessors=numberOfProcessors,
extendPairedEnds=extendPairedEnds)
def correctReadCounts(bamFilesList, binLength, numberOfSamples, defaultFragmentLength,
outFileName, outFileFormat, outFileNameCorr=None, region=None,
extendPairedEnds=True,
numberOfProcessors=1, Nsigmas = 2, maxSignalRatio=10, verbose=False):
bam1 = writeBedGraph.openBam(bamFilesList[0])
genomeSize = sum(bam1.lengths)
bam2 = writeBedGraph.openBam(bamFilesList[1])
treatmentMapped = bam1.mapped
controlMapped = bam2.mapped
treatmentControlRatioMapped = float(treatmentMapped) / controlMapped
# 1. Get a table containing number of reads in a sample from the genome.
# Only regions for which both samples have more than zero counts are considered
scaleFactorsDict = estimateScaleFactor(bamFilesList,
binLength,
numberOfSamples,
defaultFragmentLength,
1,
numberOfProcessors=numberOfProcessors,
verbose=verbose)
"""
num_reads_per_region = getNumReadsPerBin(bamFilesList, binLength, numberOfSamples, defaultFragmentLength, numberOfProcessors, skipZeros=True, verbose=verbose)
if verbose:
print "number of non-zero regions sampled: {}".format(num_reads_per_region.shape[0])
# 2. get Mean and std of treatment (col1) and control (col2)
treatmentMean, controlMean = np.mean(num_reads_per_region, axis=0) # axis=0: that measn by column
treatmentStd, controlStd = np.std(num_reads_per_region, axis=0)
treatmentTotal, controlTotal = np.sum(num_reads_per_region, axis=0)
# 3. Calculate residual in treatment & control data, at regions for which treatment
# signal exceeds mean + std * Nsigmas
# (these are expected to be the regions at which the signal > mean-signal,
# so the residual signal is positive)
overRows = np.where(num_reads_per_region[:,0].copy() >= treatmentMean + treatmentStd*Nsigmas )[0]
over_Nsigma_regions = num_reads_per_region[overRows, :]
treatmentSigMean, controlSigMean = np.mean(over_Nsigma_regions, axis=0)
treatmentExtraSignal = treatmentSigMean - treatmentMean
controlExtraSignal = controlSigMean - controlMean
treatmentControlRatio = float(treatmentTotal) / controlTotal
adjSignalRatio = maxSignalRatio * treatmentControlRatio;
treatmentSignalRatio = float(treatmentExtraSignal) / controlExtraSignal
if treatmentSignalRatio < adjSignalRatio and treatmentSignalRatio > 0:
treatmentSignalRatio = adjSignalRatio
if treatmentSignalRatio < 1:
raise NameError("estimated signal in control file {} is greater than estimated signal in treatmant file {}. Perhaps the file names are swapped?".format(bamFilesList[0], bamFilesList[1]))
else:
controlSignalRatio = 1.0/treatmentSignalRatio
controlRatio = 1.0 / treatmentControlRatio
"""
# scaleFactors = scaleFactorsDict['size_factors']
treatmentMean, controlMean = scaleFactorsDict['meanSES']
treatmentControlRatio = scaleFactorsDict['size_factors'][1]/scaleFactorsDict['size_factors'][0]
treatmentSignalRatio = treatmentControlRatio
controlRatio = controlSignalRatio = 1.0 /treatmentControlRatio
treatmentTotal = treatmentMapped
controlTotal = controlMapped
print "Treatment mean: {:.2f}, Treatment total:{:.2f}".format(treatmentMean, treatmentTotal)
print "Control mean: {:.2f}, Control total:{}".format(controlMean, controlTotal)
print "the ratio of treatment vs. control for enriched regions is: {:.2f}".format(treatmentSignalRatio)
print "the ratio of treatment vs. control ratio: {:.2f} (if based on mapped reads: {:.2f})".format(treatmentControlRatio, treatmentControlRatioMapped)
funcArgs = {'controlMean': controlMean,
'treatmentMean': treatmentMean,
'controlSignalRatio': controlSignalRatio,
'controlRatio': controlRatio,
'treatmentControlRatio': treatmentControlRatio
}
writeBedGraph.writeBedGraph( bamFilesList,
outFileName,
defaultFragmentLength, computePvalue,
funcArgs, tileSize=binLength, region=region,
format=outFileFormat,
zerosToNans = False,
numberOfProcessors=numberOfProcessors,
extendPairedEnds=extendPairedEnds)
if outFileNameCorr:
writeBedGraph.writeBedGraph( bamFilesList,
outFileNameCorr,
defaultFragmentLength, computeCorrectedReadcounts,
funcArgs, tileSize=binLength, region=region,
format=outFileFormat,
zerosToNans = False,
numberOfProcessors=numberOfProcessors,
extendPairedEnds=extendPairedEnds)
def get_scale_factors(args):
bam1 = bamHandler.openBam(args.bamfile1, args.bamIndex1)
bam2 = bamHandler.openBam(args.bamfile2, args.bamIndex2)
bam1_mapped = parserCommon.bam_total_reads(bam1,
args.ignoreForNormalization)
bam2_mapped = parserCommon.bam_total_reads(bam2,
args.ignoreForNormalization)
if args.scaleFactors:
scale_factors = map(float, args.scaleFactors.split(":"))
else:
if args.scaleFactorsMethod == 'SES':
scalefactors_dict = estimateScaleFactor(
[bam1.filename, bam2.filename],
args.sampleLength,
args.numberOfSamples,
1,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose,
chrsToSkip=args.ignoreForNormalization)
scale_factors = scalefactors_dict['size_factors']
if args.verbose:
print "Size factors using SES: {}".format(scale_factors)
print "%s regions of size %s where used " % \
(scalefactors_dict['sites_sampled'],
args.sampleLength)
print "size factor if the number of mapped " \
"reads would have been used:"
print tuple(
float(min(bam1.mapped, bam2.mapped)) /
np.array([bam1.mapped, bam2.mapped]))
elif args.scaleFactorsMethod == 'readCount':
scale_factors = float(min(bam1_mapped, bam2_mapped)) / np.array(
[bam1_mapped, bam2_mapped])
if args.verbose:
print "Size factors using total number " \
"of mapped reads: {}".format(scale_factors)
# in case the subtract method is used, the final difference
# would be normalized according to the given method
if args.ratio == 'subtract':
# The next lines identify which of the samples is not scaled down.
# The normalization using RPKM or normalize to 1x would use
# as reference such sample. Since the other sample would be
# scaled to match the un-scaled one, the normalization factor
# for both samples should be based on the unscaled one.
# For example, if sample A is unscaled and sample B is scaled by 0.5,
# then normalizing factor for A to report RPKM read counts
# is also applied to B.
if scale_factors[0] == 1:
mappedReads = bam1_mapped
bamfile = args.bamfile1
bamindex = args.bamIndex1
else:
mappedReads = bam2_mapped
bamfile = args.bamfile2
bamindex = args.bamIndex2
if args.scaleFactors is None:
if args.normalizeTo1x:
# try to guess fragment length if the bam file contains paired end reads
from deeptools.getFragmentAndReadSize import get_read_and_fragment_length
frag_len_dict, read_len_dict = get_read_and_fragment_length(
bamfile,
bamindex,
return_lengths=False,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose)
if args.extendReads:
if args.extendReads is True:
# try to guess fragment length if the bam file contains paired end reads
if frag_len_dict:
fragment_length = frag_len_dict['median']
else:
exit(
"*ERROR*: library is not paired-end. Please provide an extension length."
)
if args.verbose:
print(
"Fragment length based on paired en data "
"estimated to be {}".format(
frag_len_dict['median']))
elif args.extendReads < 1:
exit(
"*ERROR*: read extension must be bigger than one. Value give: {} "
.format(args.extendReads))
elif args.extendReads > 2000:
exit(
"*ERROR*: read extension must be smaller that 2000. Value give: {} "
.format(args.extendReads))
else:
fragment_length = args.extendReads
else:
# set as fragment length the read length
fragment_length = int(read_len_dict['median'])
if args.verbose:
print "Estimated read length is {}".format(
int(read_len_dict['median']))
current_coverage = float(
mappedReads * fragment_length) / args.normalizeTo1x
# the coverage scale factor is 1 / coverage,
coverage_scale_factor = 1.0 / current_coverage
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print "Estimated current coverage {}".format(
current_coverage)
print "Scale factor to convert " \
"current coverage to 1: {}".format(coverage_scale_factor)
else:
# by default normalize using RPKM
# the RPKM is:
# Num reads per tile/(total reads (in millions)*tile length in Kb)
millionReadsMapped = float(mappedReads) / 1e6
tileLengthInKb = float(args.binSize) / 1000
coverage_scale_factor = 1.0 / (millionReadsMapped *
tileLengthInKb)
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print "scale factor for "
"RPKM is {0}".format(coverage_scale_factor)
return scale_factors
def get_scale_factors(args):
if args.ratio == 'subtract':
# We need raw counts in this case
normalizeTo1x = args.normalizeTo1x
normalizeUsingRPKM = args.normalizeUsingRPKM
args.normalizeTo1x = False
args.normalizeUsingRPKM = False
# This is only used if we subtract
mapped_reads = [None, None]
if args.scaleFactors:
scale_factors = list(map(float, args.scaleFactors.split(":")))
elif args.scaleFactorsMethod == 'SES':
scalefactors_dict = estimateScaleFactor(
[args.bamfile1, args.bamfile2],
args.sampleLength, args.numberOfSamples,
1,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose,
chrsToSkip=args.ignoreForNormalization)
scale_factors = scalefactors_dict['size_factors']
if args.verbose:
bam1 = bamHandler.openBam(args.bamfile1)
bam2 = bamHandler.openBam(args.bamfile2)
print("Size factors using SES: {}".format(scale_factors))
print("%s regions of size %s where used " %
(scalefactors_dict['sites_sampled'],
args.sampleLength))
print("ignoring filtering/blacklists, size factors if the number of mapped "
"reads would have been used:")
print(tuple(
float(min(bam1.mapped, bam2.mapped)) / np.array([bam1.mapped, bam2.mapped])))
bam1.close()
bam2.close()
elif args.scaleFactorsMethod == 'readCount':
args.bam = args.bamfile1
args.scaleFactor = 1.0
bam1_mapped, _ = get_num_kept_reads(args)
args.bam = args.bamfile2
bam2_mapped, _ = get_num_kept_reads(args)
scale_factors = float(min(bam1_mapped, bam2_mapped)) / np.array([bam1_mapped, bam2_mapped])
mapped_reads = [bam1_mapped, bam2_mapped]
if args.verbose:
print("Size factors using total number "
"of mapped reads: {}".format(scale_factors))
# in case the subtract method is used, the final difference
# would be normalized according to the given method
if args.ratio == 'subtract':
# The next lines identify which of the samples is not scaled down.
# The normalization using RPKM or normalize to 1x would use
# as reference such sample. Since the other sample would be
# scaled to match the un-scaled one, the normalization factor due to RPKM or normalize1x
# for both samples should be based on the unscaled one.
# For example, if sample A is unscaled and sample B is scaled by 0.5,
# then normalizing factor for A to report RPKM read counts
# is also applied to B.
if args.scaleFactors is None:
# check which of the two samples is not scaled down
if scale_factors[0] == 1:
args.bam = args.bamfile1
mapped_reads = mapped_reads[0]
else:
args.bam = args.bamfile2
mapped_reads = mapped_reads[1]
if mapped_reads is None:
mapped_reads, _ = get_num_kept_reads(args)
# Replace the arguments
args.normalizeTo1x = normalizeTo1x
args.normalizeUsingRPKM = normalizeUsingRPKM
if args.scaleFactors is None:
if args.normalizeTo1x:
# try to guess fragment length if the bam file contains paired end reads
from deeptools.getFragmentAndReadSize import get_read_and_fragment_length
frag_len_dict, read_len_dict = get_read_and_fragment_length(args.bam,
return_lengths=False,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose)
if args.extendReads:
if args.extendReads is True:
# try to guess fragment length if the bam file contains paired end reads
if frag_len_dict:
fragment_length = frag_len_dict['median']
else:
exit("*ERROR*: library is not paired-end. Please provide an extension length.")
if args.verbose:
print(("Fragment length based on paired en data "
"estimated to be {}".format(frag_len_dict['median'])))
elif args.extendReads < 1:
exit("*ERROR*: read extension must be bigger than one. Value give: {} ".format(args.extendReads))
elif args.extendReads > 2000:
exit("*ERROR*: read extension must be smaller that 2000. Value give: {} ".format(args.extendReads))
else:
fragment_length = args.extendReads
else:
# set as fragment length the read length
fragment_length = int(read_len_dict['median'])
if args.verbose:
print("Estimated read length is {}".format(int(read_len_dict['median'])))
current_coverage = float(mapped_reads * fragment_length) / args.normalizeTo1x
# the coverage scale factor is 1 / coverage,
coverage_scale_factor = 1.0 / current_coverage
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print("Estimated current coverage {}".format(current_coverage))
print("Scale factor to convert "
"current coverage to 1: {}".format(coverage_scale_factor))
else:
# by default normalize using RPKM
# the RPKM is:
# Num reads per tile/(total reads (in millions)*tile length in Kb)
millionReadsMapped = float(mapped_reads) / 1e6
tileLengthInKb = float(args.binSize) / 1000
coverage_scale_factor = 1.0 / (millionReadsMapped * tileLengthInKb)
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print("Scale factor for RPKM is {0}".format(coverage_scale_factor))
return scale_factors
def get_scale_factors(args):
if args.ratio == 'subtract':
# We need raw counts in this case
normalizeTo1x = args.normalizeTo1x
normalizeUsingRPKM = args.normalizeUsingRPKM
args.normalizeTo1x = False
args.normalizeUsingRPKM = False
# This is only used if we subtract
mapped_reads = [None, None]
if args.scaleFactors:
scale_factors = list(map(float, args.scaleFactors.split(":")))
elif args.scaleFactorsMethod == 'SES':
scalefactors_dict = estimateScaleFactor(
[args.bamfile1, args.bamfile2],
args.sampleLength,
args.numberOfSamples,
1,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose,
chrsToSkip=args.ignoreForNormalization)
scale_factors = scalefactors_dict['size_factors']
if args.verbose:
bam1 = bamHandler.openBam(args.bamfile1)
bam2 = bamHandler.openBam(args.bamfile2)
print("Size factors using SES: {}".format(scale_factors))
print("%s regions of size %s where used " %
(scalefactors_dict['sites_sampled'], args.sampleLength))
print(
"ignoring filtering/blacklists, size factors if the number of mapped "
"reads would have been used:")
print(
tuple(
float(min(bam1.mapped, bam2.mapped)) /
np.array([bam1.mapped, bam2.mapped])))
bam1.close()
bam2.close()
elif args.scaleFactorsMethod == 'readCount':
args.bam = args.bamfile1
args.scaleFactor = 1.0
bam1_mapped, _ = get_num_kept_reads(args)
args.bam = args.bamfile2
bam2_mapped, _ = get_num_kept_reads(args)
scale_factors = float(min(bam1_mapped, bam2_mapped)) / np.array(
[bam1_mapped, bam2_mapped])
mapped_reads = [bam1_mapped, bam2_mapped]
if args.verbose:
print("Size factors using total number "
"of mapped reads: {}".format(scale_factors))
# in case the subtract method is used, the final difference
# would be normalized according to the given method
if args.ratio == 'subtract':
# The next lines identify which of the samples is not scaled down.
# The normalization using RPKM or normalize to 1x would use
# as reference such sample. Since the other sample would be
# scaled to match the un-scaled one, the normalization factor due to RPKM or normalize1x
# for both samples should be based on the unscaled one.
# For example, if sample A is unscaled and sample B is scaled by 0.5,
# then normalizing factor for A to report RPKM read counts
# is also applied to B.
if args.scaleFactors is None:
# check which of the two samples is not scaled down
if scale_factors[0] == 1:
args.bam = args.bamfile1
mapped_reads = mapped_reads[0]
else:
args.bam = args.bamfile2
mapped_reads = mapped_reads[1]
if mapped_reads is None:
mapped_reads, _ = get_num_kept_reads(args)
# Replace the arguments
args.normalizeTo1x = normalizeTo1x
args.normalizeUsingRPKM = normalizeUsingRPKM
if args.scaleFactors is None:
if args.normalizeTo1x:
# try to guess fragment length if the bam file contains paired end reads
from deeptools.getFragmentAndReadSize import get_read_and_fragment_length
frag_len_dict, read_len_dict = get_read_and_fragment_length(
args.bam,
return_lengths=False,
blackListFileName=args.blackListFileName,
numberOfProcessors=args.numberOfProcessors,
verbose=args.verbose)
if args.extendReads:
if args.extendReads is True:
# try to guess fragment length if the bam file contains paired end reads
if frag_len_dict:
fragment_length = frag_len_dict['median']
else:
exit(
"*ERROR*: library is not paired-end. Please provide an extension length."
)
if args.verbose:
print(("Fragment length based on paired en data "
"estimated to be {}".format(
frag_len_dict['median'])))
elif args.extendReads < 1:
exit(
"*ERROR*: read extension must be bigger than one. Value give: {} "
.format(args.extendReads))
elif args.extendReads > 2000:
exit(
"*ERROR*: read extension must be smaller that 2000. Value give: {} "
.format(args.extendReads))
else:
fragment_length = args.extendReads
else:
# set as fragment length the read length
fragment_length = int(read_len_dict['median'])
if args.verbose:
print("Estimated read length is {}".format(
int(read_len_dict['median'])))
current_coverage = float(
mapped_reads * fragment_length) / args.normalizeTo1x
# the coverage scale factor is 1 / coverage,
coverage_scale_factor = 1.0 / current_coverage
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print("Estimated current coverage {}".format(
current_coverage))
print("Scale factor to convert "
"current coverage to 1: {}".format(
coverage_scale_factor))
else:
# by default normalize using RPKM
# the RPKM is:
# Num reads per tile/(total reads (in millions)*tile length in Kb)
millionReadsMapped = float(mapped_reads) / 1e6
tileLengthInKb = float(args.binSize) / 1000
coverage_scale_factor = 1.0 / (millionReadsMapped *
tileLengthInKb)
scale_factors = np.array(scale_factors) * coverage_scale_factor
if args.verbose:
print("Scale factor for RPKM is {0}".format(
coverage_scale_factor))
return scale_factors
