Source code for cnvlib.metrics

"""Robust metrics to evaluate performance of copy number estimates.
import numpy as np
import pandas as pd

from . import descriptives

[docs]def do_metrics(cnarrs, segments=None, skip_low=False): """Compute coverage deviations and other metrics for self-evaluation.""" # Catch if passed args are single CopyNumArrays instead of lists from .cnary import CopyNumArray as CNA if isinstance(cnarrs, CNA): cnarrs = [cnarrs] if isinstance(segments, CNA): segments = [segments] elif segments is None: segments = [None] else: segments = list(segments) if skip_low: cnarrs = (cna.drop_low_coverage() for cna in cnarrs) rows = ((cna.meta.get("filename", cna.sample_id), len(seg) if seg is not None else '-' ) + ests_of_scale(cna.residuals(seg).values) for cna, seg in zip_repeater(cnarrs, segments)) colnames = ["sample", "segments", "stdev", "mad", "iqr", "bivar"] return pd.DataFrame.from_records(rows, columns=colnames)
[docs]def zip_repeater(iterable, repeatable): """Repeat a single segmentation to match the number of copy ratio inputs""" rpt_len = len(repeatable) if rpt_len == 1: rpt = repeatable[0] for it in iterable: yield it, rpt else: i = -1 for i, (it, rpt) in enumerate(zip(iterable, repeatable)): yield it, rpt # Require lengths to match if i + 1 != rpt_len: raise ValueError("""Number of unsegmented and segmented input files did not match (%d vs. %d)""" % (i, rpt_len))
[docs]def ests_of_scale(deviations): """Estimators of scale: standard deviation, MAD, biweight midvariance. Calculates all of these values for an array of deviations and returns them as a tuple. """ std = np.std(deviations, dtype=np.float64) mad = descriptives.median_absolute_deviation(deviations) iqr = descriptives.interquartile_range(deviations) biw = descriptives.biweight_midvariance(deviations) return (std, mad, iqr, biw)