annotate pairend_distro.py @ 6:48e97429a749 draft default tip

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/lumpy_sv commit 37c0f0938a5cad74c954a89827a6a08bbbb81cb0

#!/usr/bin/env python
#  (c) 2012 - Ryan M. Layer
#  Hall Laboratory
#  Quinlan Laboratory
#  Department of Computer Science
#  Department of Biochemistry and Molecular Genetics
#  Department of Public Health Sciences and Center for Public Health Genomics,
#  University of Virginia
#  [e-mail]

import sys
from optparse import OptionParser

import numpy as np

# some constants for sam/bam field ids
SAM_FLAG = 1
SAM_REFNAME = 2
SAM_MATE_REFNAME = 6
SAM_ISIZE = 8

parser = OptionParser()
parser.add_option("-r", "--read_length", type="int", dest="read_length",
                  help="Read length")
parser.add_option("-X", dest="X", type="int",
                  help="Number of stdevs from mean to extend")
parser.add_option("-N", dest="N", type="int", help="Number to sample")
parser.add_option("-o", dest="output_file", help="Output file")
parser.add_option("-m", dest="mads", type="int", default=10,
                  help='''Outlier cutoff in # of median absolute deviations
                          (unscaled, upper only)''')


def unscaled_upper_mad(xs):
    """Return a tuple consisting of the median of xs followed by the
    unscaled median absolute deviation of the values in xs that lie
    above the median.
    """
    med = np.median(xs)
    return med, np.median(xs[xs > med] - med)


(options, args) = parser.parse_args()

if not options.read_length:
    parser.error('Read length not given')

if not options.X:
    parser.error('X not given')

if not options.N:
    parser.error('N not given')

if not options.output_file:
    parser.error('Output file not given')


required = 97
restricted = 3484
flag_mask = required | restricted

L = []
c = 0

for length in sys.stdin:
    if c >= options.N:
        break

    A = length.rstrip().split('\t')
    flag = int(A[SAM_FLAG])
    refname = A[SAM_REFNAME]
    mate_refname = A[SAM_MATE_REFNAME]
    isize = int(A[SAM_ISIZE])

    want = mate_refname == "=" and flag & flag_mask == required and isize >= 0
    if want:
        c += 1
        L.append(isize)

# warn if very few elements in distribution
min_elements = 1000
if len(L) < min_elements:
    sys.stderr.write("Warning: only %s elements in distribution (min: %s)\n" %
                     (str(len(L)), str(min_elements)))
    mean = "NA"
    stdev = "NA"

else:
    # Remove outliers
    L = np.array(L)
    L.sort()
    med, umad = unscaled_upper_mad(L)
    upper_cutoff = med + options.mads * umad
    L = L[L < upper_cutoff]
    new_len = len(L)
    removed = c - new_len
    sys.stderr.write("Removed %s outliers with isize >= %s\n" %
                     (str(removed), str(upper_cutoff)))
    c = new_len

    mean = np.mean(L)
    stdev = np.std(L)

    start = options.read_length
    end = int(mean + options.X*stdev)

    H = [0] * (end - start + 1)
    s = 0

    for x in L:
        if (x >= start) and (x <= end):
            j = int(x - start)
            H[j] = H[int(x - start)] + 1
            s += 1

    f = open(options.output_file, 'w')

    for i in range(end - start):
        o = str(i) + "\t" + str(float(H[i])/float(s)) + "\n"
        f.write(o)
    f.close()
print('mean:' + str(mean) + '\tstdev:' + str(stdev))