def starts_by_depth(bam_file, config, sample_size=None):
"""
Return a set of x, y points where x is the number of reads sequenced and
y is the number of unique start sites identified
If sample size < total reads in a file the file will be downsampled.
"""
binsize = (bam.count(bam_file, config) / 100) + 1
seen_starts = set()
counted = 0
num_reads = []
starts = []
buffer = []
with bam.open_samfile(bam_file) as samfile:
# unmapped reads should not be counted
filtered = ifilter(lambda x: not x.is_unmapped, samfile)
def read_parser(read):
return ":".join([str(read.tid), str(read.pos)])
# if no sample size is set, use the whole file
if not sample_size:
samples = map(read_parser, filtered)
else:
samples = utils.reservoir_sample(filtered, sample_size, read_parser)
shuffle(samples)
for read in samples:
counted += 1
buffer.append(read)
if counted % binsize == 0:
seen_starts.update(buffer)
buffer = []
num_reads.append(counted)
starts.append(len(seen_starts))
seen_starts.update(buffer)
num_reads.append(counted)
starts.append(len(seen_starts))
return pd.DataFrame({"reads": num_reads, "starts": starts})
def count_duplicate_starts(bam_file, sample_size=10000000):
"""
Return a set of x, y points where x is the number of reads sequenced and
y is the number of unique start sites identified
If sample size < total reads in a file the file will be downsampled.
"""
count = Counter()
with bam.open_samfile(bam_file) as samfile:
# unmapped reads should not be counted
filtered = ifilter(lambda x: not x.is_unmapped, samfile)
def read_parser(read):
return ":".join([str(read.tid), str(read.pos)])
samples = utils.reservoir_sample(filtered, sample_size, read_parser)
count.update(samples)
return count
def count_duplicate_starts(bam_file, sample_size=10000000):
"""
Return a set of x, y points where x is the number of reads sequenced and
y is the number of unique start sites identified
If sample size < total reads in a file the file will be downsampled.
"""
count = Counter()
with bam.open_samfile(bam_file) as samfile:
# unmapped reads should not be counted
filtered = ifilter(lambda x: not x.is_unmapped, samfile)
def read_parser(read):
return ":".join([str(read.tid), str(read.pos)])
samples = utils.reservoir_sample(filtered, sample_size, read_parser)
count.update(samples)
return count
def starts_by_depth(bam_file, config, sample_size=None):
"""
Return a set of x, y points where x is the number of reads sequenced and
y is the number of unique start sites identified
If sample size < total reads in a file the file will be downsampled.
"""
binsize = (bam.count(bam_file, config) / 100) + 1
seen_starts = set()
counted = 0
num_reads = []
starts = []
buffer = []
with bam.open_samfile(bam_file) as samfile:
# unmapped reads should not be counted
filtered = ifilter(lambda x: not x.is_unmapped, samfile)
def read_parser(read):
return ":".join([str(read.tid), str(read.pos)])
# if no sample size is set, use the whole file
if not sample_size:
samples = map(read_parser, filtered)
else:
samples = utils.reservoir_sample(filtered, sample_size,
read_parser)
shuffle(samples)
for read in samples:
counted += 1
buffer.append(read)
if counted % binsize == 0:
seen_starts.update(buffer)
buffer = []
num_reads.append(counted)
starts.append(len(seen_starts))
seen_starts.update(buffer)
num_reads.append(counted)
starts.append(len(seen_starts))
return pd.DataFrame({"reads": num_reads, "starts": starts})
