Source code for cnvlib.cnary

"""CNVkit's core data structure, a copy number array."""
from __future__ import print_function, absolute_import, division

import logging

import numpy as np
import pandas as pd

from . import core, gary, metrics, params, smoothing

[docs]class CopyNumArray(gary.GenomicArray): """An array of genomic intervals, treated like aCGH probes. Required columns: chromosome, start, end, gene, log2 Optional columns: gc, rmask, spread, weight, probes """ _required_columns = ("chromosome", "start", "end", "gene", "log2") _required_dtypes = ("string", "int", "int", "string", "float") # ENH: make gene optional # Extra columns, in order: # "gc", "rmask", "spread", "weight", "probes" def __init__(self, data_table, meta_dict=None): gary.GenomicArray.__init__(self, data_table, meta_dict) @property def log2(self): return["log2"] @log2.setter def log2(self, value):["log2"] = value @property def _chr_x_label(self): if 'chr_x' in self.meta: return self.meta['chr_x'] chr_x = ('chrX' if self[0, 'chromosome'].startswith('chr') else 'X') self.meta['chr_x'] = chr_x return chr_x @property def _chr_y_label(self): if 'chr_y' in self.meta: return self.meta['chr_y'] chr_y = ('chrY' if self._chr_x_label.startswith('chr') else 'Y') self.meta['chr_y'] = chr_y return chr_y # More meta to store: # is_sample_male = bool # is_reference_male = bool # file_path # * invalidate 'chr_x' if .chromosome/['chromosome'] is set # Traversal # XXX hair: some genes overlap; some bins cover multiple genes # -> option: whether to split gene names on commas
[docs] def by_gene(self, ignore=params.IGNORE_GENE_NAMES): """Iterate over probes grouped by gene name. Emits pairs of (gene name, CNA of rows with same name) Groups each series of intergenic bins as a 'Background' gene; any 'Background' bins within a gene are grouped with that gene. Bins with names in `ignore` are treated as 'Background' bins, but retain their name. """ start_idx = end_idx = None for _chrom, subgary in self.by_chromosome(): prev_idx = 0 for gene in pd.unique(['gene']): if not (gene == 'Background' or gene in ignore): gene_idx = (['gene'] == gene).nonzero()[0] if not len(gene_idx): logging.warn("Specified gene name somehow missing: %s", gene) continue start_idx = gene_idx[0] end_idx = gene_idx[-1] + 1 if prev_idx < start_idx: # Include intergenic regions yield "Background", subgary.as_dataframe([prev_idx:start_idx]) yield gene, subgary.as_dataframe([start_idx:end_idx]) prev_idx = end_idx if prev_idx < len(subgary) - 1: # Include the telomere yield "Background", subgary.as_dataframe([prev_idx:])
# Manipulation
[docs] def center_all(self, estimator=np.median, skip_low=False): """Recenter coverage values to the autosomes' average (in-place).""" est_funcs = { "mean": np.mean, "median": np.median, "mode": metrics.modal_location, "biweight": metrics.biweight_location, } if isinstance(estimator, basestring): if estimator in est_funcs: estimator = est_funcs[estimator] else: raise ValueError("Estimator must be a function or one of: %s" % ", ".join(map(repr, est_funcs))) table = (self.drop_low_coverage() if skip_low else self).autosomes() if table:['log2'] -= estimator(table['log2'])
[docs] def drop_low_coverage(self): """Drop bins with extremely low log2 coverage values. These are generally bins that had no reads mapped, and so were substituted with a small dummy log2 value to avoid divide-by-zero errors. """ return self.as_dataframe([['log2'] > params.NULL_LOG2_COVERAGE - params.MIN_REF_COVERAGE])
[docs] def squash_genes(self, summary_func=metrics.biweight_location, squash_background=False, ignore=params.IGNORE_GENE_NAMES): """Combine consecutive bins with the same targeted gene name. The `ignore` parameter lists bin names that not be counted as genes to be output. Parameter `summary_func` is a function that summarizes an array of coverage values to produce the "squashed" gene's coverage value. By default this is the biweight location, but you might want median, mean, max, min or something else in some cases. """ def squash_rows(name, rows): """Combine multiple rows (for the same gene) into one row.""" if len(rows) == 1: return tuple(rows.iloc[0]) chrom = core.check_unique(rows.chromosome, 'chromosome') start = rows.iloc[0]['start'] end = rows.iloc[-1]['end'] cvg = summary_func(rows.log2) outrow = [chrom, start, end, name, cvg] # Handle extra fields # ENH - no coverage stat; do weighted average as appropriate for xfield in ('gc', 'rmask', 'spread', 'weight'): if xfield in self: outrow.append(summary_func(rows[xfield])) if 'probes' in self: outrow.append(sum(rows['probes'])) return tuple(outrow) outrows = [] for name, subarr in self.by_gene(ignore): if name == 'Background' and not squash_background: outrows.extend( else: outrows.append(squash_rows(name, return self.as_rows(outrows)
# Chromosomal gender
[docs] def shift_xx(self, male_reference=False): """Adjust chrX coverages (divide in half) for apparent female samples.""" outprobes = self.copy() is_xx = self.guess_xx(male_reference=male_reference) if is_xx and male_reference: # Female: divide X coverages by 2 (in log2: subtract 1) outprobes[outprobes.chromosome == self._chr_x_label, 'log2'] -= 1.0 # Male: no change elif not is_xx and not male_reference: # Male: multiply X coverages by 2 (in log2: add 1) outprobes[outprobes.chromosome == self._chr_x_label, 'log2'] += 1.0 # Female: no change return outprobes
[docs] def guess_xx(self, male_reference=False, verbose=True): """Guess whether a sample is female from chrX relative coverages. Recommended cutoff values: -0.5 -- raw target data, not yet corrected +0.5 -- probe data already corrected on a male profile """ cutoff = 0.5 if male_reference else -0.5 # ENH - better coverage approach: take Z-scores or rank of +1,0 or 0,-1 # based on the available probes, then choose which is more probable rel_chrx_cvg = self.get_relative_chrx_cvg() if rel_chrx_cvg is None: return is_xx = (rel_chrx_cvg >= cutoff) if verbose:"Relative log2 coverage of X chromosome: %g " "(assuming %s)", rel_chrx_cvg, ('male', 'female')[is_xx]) return is_xx
[docs] def get_relative_chrx_cvg(self): """Get the relative log-coverage of chrX in a sample.""" chromosome_x = self[self.chromosome == self._chr_x_label] if not len(chromosome_x): logging.warn("*WARNING* No %s found in probes; check the input", self._chr_x_label) return autosomes = self.autosomes() auto_cvgs = autosomes['log2'] x_cvgs = chromosome_x['log2'] if 'probes' in self: # Weight segments by number of probes to ensure good behavior auto_sizes = autosomes['probes'] x_sizes = chromosome_x['probes'] # ENH: weighted median rel_chrx_cvg = (np.average(x_cvgs, weights=x_sizes) - np.average(auto_cvgs, weights=auto_sizes)) else: rel_chrx_cvg = np.median(x_cvgs) - np.median(auto_cvgs) return rel_chrx_cvg
[docs] def expect_flat_cvg(self, is_male_reference=None): """Get the uninformed expected copy ratios of each bin. Create an array of log2 coverages like a "flat" reference. This is a neutral copy ratio at each autosome (log2 = 0.0) and sex chromosomes based on whether the reference is male (XX or XY). """ if is_male_reference is None: is_male_reference = not self.guess_xx(verbose=False) cvg = np.zeros(len(self), dtype=np.float_) if is_male_reference: # Single-copy X, Y idx = np.asarray((self.chromosome == self._chr_x_label) | (self.chromosome == self._chr_y_label)) else: # Y will be all noise, so replace with 1 "flat" copy idx = np.asarray(self.chromosome == self._chr_y_label) cvg[idx] = -1.0 return cvg
# Reporting
[docs] def residuals(self, segments=None): """Difference in log2 value of each bin from its segment mean. If segments are just regions (e.g. RegionArray) with no log2 values precalculated, subtract the median of this array's log2 values within each region. If no segments are given, subtract each chromosome's median. """ if not segments: resids = [subcna.log2 - subcna.log2.median() for _chrom, subcna in self.by_chromosome()] elif "log2" in segments: resids = [subcna.log2 - seg.log2 for seg, subcna in self.by_ranges(segments)] else: resids = [subcna.log2 - subcna.log2.median() for _seg, subcna in self.by_ranges(segments)] return np.concatenate(resids) if resids else np.array([])
[docs] def guess_average_depth(self, segments=None, window=100): """Estimate the effective average read depth from variance. Assume read depths are Poisson distributed, converting log2 values to absolute counts. Then the mean depth equals the variance , and the average read depth is the estimated mean divided by the estimated variance. Use robust estimators (Tukey's biweight location and midvariance) to compensate for outliers and overdispersion. With `segments`, take the residuals of this array's log2 values from those of the segments to remove the confounding effect of real CNVs. If `window` is an integer, calculate and subtract a smoothed trendline to remove the effect of CNVs without segmentation (skipped if `segments` are given). See: """ # Try to drop allosomes cnarr = self.autosomes() if not len(cnarr): cnarr = self # Remove variations due to real/likely CNVs y_log2 = cnarr.residuals(segments) if window: y_log2 -= smoothing.smoothed(y_log2, window) # Guess Poisson parameter from absolute-scale values y = np.exp2(y_log2) # ENH: use weight argument to these stats loc = metrics.biweight_location(y) spread = metrics.biweight_midvariance(y, loc) scale = loc / spread**2 return scale