Source code for cnvlib.importers

"""Import from other formats to the CNVkit format."""
from __future__ import absolute_import, division, print_function

import logging
import math
import os.path
import subprocess

import numpy as np
import pandas as pd

from . import core, params
from .cnary import CopyNumArray as CNA

# __________________________________________________________________________
# 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): # 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 import_picard_pertargetcoverage(fname): """Parse a Picard CalculateHsMetrics PER_TARGET_COVERAGE file. Return a CopyNumArray. Input column names: chrom (str), start, end, length (int), name (str), %gc, mean_coverage, normalized_coverage (float) """ dframe = pd.read_table(fname, na_filter=False) coverages = np.asarray(dframe['mean_coverage']) no_cvg_idx = (coverages == 0) if sum(no_cvg_idx) > TOO_MANY_NO_COVERAGE: logging.warn("*WARNING* Sample %s has >%d bins with no coverage", fname, TOO_MANY_NO_COVERAGE) # Avoid math domain error coverages[no_cvg_idx] = 2**params.NULL_LOG2_COVERAGE cnarr = CNA.from_columns({"chromosome": dframe["chrom"], "start": dframe["start"] - 1, "end": dframe["end"], "gene": dframe["name"].apply(unpipe_name), "gc": dframe["%gc"], "log2": np.log2(coverages)}, {"sample_id": core.fbase(fname)}) cnarr.sort() return cnarr
[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. Meaningless target names are dropped, e.g. 'CGH|FOO|-' resolves as 'FOO'. In case of ambiguity, the longest name is taken, e.g. "TERT|TERT Promoter" resolves as "TERT Promoter". """ if '|' not in name: return name gene_names = set(name.split('|')) if len(gene_names) == 1: return gene_names.pop() cleaned_names = gene_names.difference(params.IGNORE_GENE_NAMES) if cleaned_names: gene_names = cleaned_names new_name = sorted(gene_names, key=len, reverse=True)[0] if len(gene_names) > 1: logging.warn("*WARNING* Ambiguous gene name %r; using %r", name, new_name) return new_name
# __________________________________________________________________________ # 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 as an iterable of CopyNumArray instances. `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) `from_log10`: Convert values from log10 to log2. """ dframe = pd.read_table(segfname, na_filter=False) if len(dframe.columns) == 6: dframe.columns = ['sample_id', 'chromosome', 'start', 'end', 'nprobes', 'mean'] elif len(dframe.columns) == 5: dframe.columns = ['sample_id', 'chromosome', 'start', 'end', 'mean'] else: raise ValueError("SEG format expects 5 or 6 columns; found {}: {}" .format(len(dframe.columns), ' '.join(dframe.columns))) # Calculate values for output columns dframe['chromosome'] = dframe['chromosome'].apply(str) if chrom_names: dframe['chromosome'] = dframe['chromosome'].apply(lambda c: chrom_names.get(c, c)) if chrom_prefix: dframe['chromosome'] = dframe['chromosome'].apply(lambda c: chrom_prefix + c) if from_log10: dframe['mean'] *= LOG2_10 dframe['gene'] = ["G" if mean >= 0 else "L" for mean in dframe['mean']] for sid in pd.unique(dframe['sample_id']): sample = dframe[dframe['sample_id'] == sid] cols = {'chromosome': sample['chromosome'], 'start': sample['start'], 'end': sample['end'], 'gene': sample['gene'], 'log2': sample['mean']} if 'nprobes' in dframe: cols['probes'] = sample['nprobes'] cns = CNA.from_columns(cols, {'sample_id': sid}) cns.sort() yield cns
# __________________________________________________________________________ # 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}