"""Import from other formats to the CNVkit format."""
from __future__ import absolute_import, division, print_function
import math
import os.path
import subprocess
from . import core
from .params import NULL_LOG2_COVERAGE
from .ngfrills import echo
from .cnarray import CopyNumArray as CNA
# __________________________________________________________________________
# import-picard
TOO_MANY_NO_COVERAGE = 100
[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):
# 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
[docs]def load_targetcoverage_csv(fname):
"""Parse a target or antitarget coverage file (.csv) into a CopyNumArray.
These files are generated by Picard CalculateHsMetrics.
The fields of the .csv files are actually separated by tabs, not commas.
CSV column names:
chrom (str),
start, end, length (int),
name (str),
%gc, mean_coverage, normalized_coverage (float)
"""
cna_rows = []
no_cvg_cnt = 0 # Count probes with no coverage
for row in core.parse_tsv(fname):
assert len(row) >= 8, \
"Bad line in {}:\n{}".format(fname, row)
chrom = row[0]
start = int(row[1])
end = int(row[2])
gene = unpipe_name(row[4])
gc_val = float(row[5])
cvg = float(row[7]) # Coverage already normalized to avg. 1 by Picard
if cvg == 0:
no_cvg_cnt += 1
coverage = NULL_LOG2_COVERAGE
else:
coverage = math.log(cvg, 2)
cna_rows.append((chrom, start, end, gene, coverage, gc_val))
if no_cvg_cnt > TOO_MANY_NO_COVERAGE:
echo("*WARNING* Sample", fname, "has >", TOO_MANY_NO_COVERAGE,
"probes with no coverage")
pset = CNA.from_rows(core.fbase(fname), cna_rows, ('gc',))
pset.sort()
return pset
[docs]def unpipe_name(name):
"""Fix the duplicated gene names Picard spits out.
Return a string containing the single gene name, sans duplications and pipe
characters.
Picard CalculateHsMetrics combines the labels of overlapping intervals
by joining all labels with '|', e.g. 'BRAF|BRAF' -- no two distinct
targeted genes actually overlap, though, so these dupes are redundant.
Also, in our convention, 'CGH' probes are selected intergenic regions, not
meaningful gene names, so 'CGH|FOO' resolves as 'FOO'.
"""
gene_names = set(name.split('|'))
if len(gene_names) > 1:
if 'CGH' in gene_names and len(gene_names) == 2:
gene_names.remove('CGH')
else:
echo("*WARNING* Ambiguous gene name:", name)
return gene_names.pop()
# __________________________________________________________________________
# import-seg
LOG2_10 = math.log(10, 2) # To convert log10 values to log2
[docs]def import_seg(segfname, chrom_names, chrom_prefix, from_log10):
"""Parse a SEG file. Emit pairs of (sample ID, CopyNumArray)
Values are converted from log10 to log2.
`chrom_names`:
Map (string) chromosome IDs to names. (Applied before chrom_prefix.)
e.g. {'23': 'X', '24': 'Y', '25': 'M'}
`chrom_prefix`: prepend this string to chromosome names
(usually 'chr' or None)
"""
curr_sample = None
curr_rows = []
with open(segfname) as infile:
lines = iter(infile)
next(lines) # Skip the header
for line in lines:
sample, chrom, start, end, nprobes, mean = line.split()
if chrom_names and chrom in chrom_names:
chrom = chrom_names[chrom]
if chrom_prefix:
chrom = chrom_prefix + chrom
mean = float(mean)
if from_log10:
mean *= LOG2_10
this_row = (chrom, int(start), int(end),
("G" if mean >= 0 else "L"), mean, int(nprobes))
if curr_sample != sample:
if curr_sample is not None:
assert len(curr_rows)
# Emit the current set of segments as a sample
yield CNA.from_rows(curr_sample, curr_rows, ('probes',))
# Reset
curr_sample = sample
curr_rows = [this_row]
else:
# Continue building this sample
curr_rows.append(this_row)
# Remainder
if curr_sample is not None:
assert len(curr_rows)
# Emit the current set of segments as a sample
yield CNA.from_rows(curr_sample, curr_rows, ('probes',))
# __________________________________________________________________________
# import-theta
[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 = 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}