def get_scale_factor(args):
scale_factor = args.scaleFactor
bam_handle = bamHandler.openBam(args.bam)
bam_mapped = parserCommon.bam_total_reads(bam_handle, args.ignoreForNormalization)
blacklisted = parserCommon.bam_blacklisted_reads(bam_handle, args.ignoreForNormalization, args.blackListFileName)
bam_mapped -= blacklisted
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(bam_mapped * fragment_length) / args.normalizeTo1x
# the scaling sets the coverage to match 1x
scale_factor *= 1.0 / current_coverage
if debug:
print "Estimated current coverage {}".format(current_coverage)
print "Scaling factor {}".format(args.scaleFactor)
elif args.normalizeUsingRPKM:
# the RPKM is the # reads per tile / \
# ( total reads (in millions) * tile length in Kb)
million_reads_mapped = float(bam_mapped) / 1e6
tile_len_in_kb = float(args.binSize) / 1000
scale_factor *= 1.0 / (million_reads_mapped * tile_len_in_kb)
if debug:
print "scale factor using RPKM is {0}".format(args.scaleFactor)
return scale_factor
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 get_scale_factor(args):
scale_factor = args.scaleFactor
bam_handle = bamHandler.openBam(args.bam, args.bamIndex)
bam_mapped = parserCommon.bam_total_reads(bam_handle,
args.ignoreForNormalization)
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,
args.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(bam_mapped * fragment_length) / args.normalizeTo1x
# the scaling sets the coverage to match 1x
scale_factor *= 1.0 / current_coverage
if debug:
print "Estimated current coverage {}".format(current_coverage)
print "Scaling factor {}".format(args.scaleFactor)
elif args.normalizeUsingRPKM:
# the RPKM is the # reads per tile / \
# ( total reads (in millions) * tile length in Kb)
million_reads_mapped = float(bam_mapped) / 1e6
tile_len_in_kb = float(args.binSize) / 1000
scale_factor *= 1.0 / (million_reads_mapped * tile_len_in_kb)
if debug:
print "scale factor using RPKM is {0}".format(args.scaleFactor)
return scale_factor
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
