"""Import from other formats to the CNVkit format."""
from __future__ import absolute_import, division, print_function
from builtins import map, next, zip
import logging
import os.path
import subprocess
import numpy as np
# __________________________________________________________________________
# import-picard
[docs]def find_picard_files(file_and_dir_names):
"""Search the given paths for 'targetcoverage' CSV files.
Per the convention we use in our Picard applets, the target coverage file
names end with '.targetcoverage.csv'; anti-target coverages end with
'.antitargetcoverage.csv'.
"""
filenames = []
for tgt in file_and_dir_names:
if os.path.isdir(tgt):
logging.warn("Searching the given directory tree [DEPRECATED]\n"
"** Instead, specify the filenames directly, using "
"wildcards or the Unix command 'find'")
# Collect the target coverage files from this directory tree
fnames = subprocess.check_output(['find', tgt,
'-name', '*targetcoverage.csv']
).splitlines()
if not fnames:
raise RuntimeError("Given directory %s does not contain any "
"'*targetcoverage.csv' files."
% tgt)
filenames.extend(fnames)
elif os.path.isfile(tgt):
filenames.append(tgt)
else:
raise ValueError("Given path is neither a file nor a directory: %s"
% tgt)
filenames.sort()
return filenames
# __________________________________________________________________________
# import-theta
[docs]def do_import_theta(segarr, theta_results_fname, ploidy=2):
theta = parse_theta_results(theta_results_fname)
# THetA doesn't handle sex chromosomes well
segarr = segarr.autosomes()
for copies in theta['C']:
if len(copies) != len(segarr):
copies = copies[:len(segarr)]
# Drop any segments where the C value is None
mask_drop = np.array([c is None for c in copies], dtype='bool')
segarr = segarr[~mask_drop].copy()
ok_copies = np.asfarray([c for c in copies if c is not None])
# Replace remaining segment values with these integers
segarr["cn"] = ok_copies.astype('int')
ok_copies[ok_copies == 0] = 0.5
segarr["log2"] = np.log2(ok_copies / ploidy)
segarr.sort_columns()
yield segarr
[docs]def parse_theta_results(fname):
"""Parse THetA results into a data structure.
Columns: NLL, mu, C, p*
"""
with open(fname) as handle:
header = next(handle).rstrip().split('\t')
body = next(handle).rstrip().split('\t')
assert len(body) == len(header) == 4
# NLL
nll = float(body[0])
# mu
mu = body[1].split(',')
mu_normal = float(mu[0])
mu_tumors = list(map(float, mu[1:]))
# C
copies = body[2].split(':')
if len(mu_tumors) == 1:
# 1D array of integers
# Replace X with None for "missing"
copies = [[int(c) if c.isdigit() else None
for c in copies]]
else:
# List of lists of integer-or-None (usu. 2 x #segments)
copies = [[int(c) if c.isdigit() else None
for c in subcop]
for subcop in zip(*[c.split(',') for c in copies])]
# p*
probs = body[3].split(',')
if len(mu_tumors) == 1:
# 1D array of floats, or None for "X" (missing/unknown)
probs = [float(p) if not p.isalpha() else None
for p in probs]
else:
probs = [[float(p) if not p.isalpha() else None
for p in subprob]
for subprob in zip(*[p.split(',') for p in probs])]
return {"NLL": nll,
"mu_normal": mu_normal,
"mu_tumors": mu_tumors,
"C": copies,
"p*": probs}