"""Supporting functions for the 'fix' command."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Optional, Union
import numpy as np
import pandas as pd
from . import descriptives, params, smoothing
if TYPE_CHECKING:
from cnvlib.cnary import CopyNumArray
from numpy import ndarray
from pandas.core.series import Series
[docs]
def do_fix(
target_raw: CopyNumArray,
antitarget_raw: CopyNumArray,
reference: CopyNumArray,
diploid_parx_genome: Optional[str] = None,
do_gc: bool = True,
do_edge: bool = True,
do_rmask: bool = True,
do_cluster: bool = False,
smoothing_window_fraction: None = None,
) -> CopyNumArray:
"""Normalize tumor/test sample coverage using a reference and correct biases.
This is the core normalization step in the CNVkit pipeline. It combines target
and antitarget coverage data, applies the reference baseline, corrects for
systematic biases (GC content, edge effects, RepeatMasker), and produces
log2 copy ratios ready for segmentation.
The "fix" name refers to fixing/correcting coverage biases.
Parameters
----------
target_raw : CopyNumArray
Raw coverage data for on-target/baited regions from the test sample.
Typically a .targetcoverage.cnn file from the `coverage` command.
antitarget_raw : CopyNumArray
Raw coverage data for off-target/antitarget regions from the test sample.
Typically a .antitargetcoverage.cnn file from the `coverage` command.
Can be empty for amplicon sequencing.
reference : CopyNumArray
Pooled reference created from normal samples using `do_reference`.
Contains expected neutral coverage and bias correction features.
diploid_parx_genome : str, optional
Reference genome name for pseudo-autosomal region handling
(e.g., 'hg19', 'hg38', 'mm10'). Ensures PAR regions on X/Y chromosomes
are treated as diploid during normalization.
Default: None
do_gc : bool, optional
Apply GC bias correction using GC content from reference.
Corrects for systematic coverage variations due to GC%.
Default: True
do_edge : bool, optional
Apply edge effect correction for target bins.
Corrects for reduced coverage near bait interval boundaries.
Default: True
do_rmask : bool, optional
Apply RepeatMasker correction for antitarget bins.
Corrects for coverage variations in repetitive regions.
Default: True
do_cluster : bool, optional
If the reference contains multiple sub-clusters (from hierarchical
clustering during reference creation), select the cluster with
highest correlation to this sample.
Default: False
smoothing_window_fraction : None, optional
Reserved parameter for future smoothing functionality.
Currently not implemented.
Default: None
Returns
-------
CopyNumArray
Normalized copy number ratios (.cnr file) with log2 ratios centered
around 0 (neutral copy number). Ready for segmentation with the
`segment` command.
Notes
-----
The normalization process:
1. **Bias correction** (separately for targets and antitargets):
- GC correction: Adjusts for GC content-dependent coverage bias
- Edge correction: Adjusts for lower coverage near bait edges (targets only)
- RepeatMasker correction: Adjusts for repeats (antitargets only)
2. **Reference subtraction**:
- Subtracts reference log2 values from sample log2 values
- If do_cluster=True, selects best-matching reference cluster
3. **Weighting**:
- Applies statistical weights based on coverage variability
- Down-weights unreliable bins
4. **Centering**:
- Centers the genome-wide distribution around log2=0
- Accounts for sex chromosomes and PAR regions
The output .cnr file contains one row per bin with columns:
- chromosome, start, end, gene: Genomic location
- log2: Copy ratio in log2 scale (0 = diploid, +1 = gain, -1 = loss)
- depth: Read depth
- weight: Statistical weight for segmentation
See Also
--------
do_reference : Creates the reference used for normalization
load_adjust_coverages : Internal function that performs bias correction
apply_weights : Calculates statistical weights for bins
Examples
--------
Basic usage:
>>> fixed = do_fix(
... target_raw=read_cna('Sample.targetcoverage.cnn'),
... antitarget_raw=read_cna('Sample.antitargetcoverage.cnn'),
... reference=read_cna('reference.cnn')
... )
With cluster selection:
>>> fixed = do_fix(
... target_raw, antitarget_raw, reference,
... do_cluster=True # Select best-matching reference cluster
... )
For male sample with PAR handling:
>>> fixed = do_fix(
... target_raw, antitarget_raw, reference,
... diploid_parx_genome='hg38'
... )
"""
# Load, recenter and GC-correct target & antitarget probes separately
logging.info("Processing target: %s", target_raw.sample_id)
cnarr, ref_matched = load_adjust_coverages(
target_raw,
reference,
True,
do_gc,
do_edge,
False,
diploid_parx_genome,
smoothing_window_fraction=smoothing_window_fraction,
)
logging.info("Processing antitarget: %s", antitarget_raw.sample_id)
anti_cnarr, ref_anti = load_adjust_coverages(
antitarget_raw,
reference,
False,
do_gc,
False,
do_rmask,
diploid_parx_genome,
smoothing_window_fraction=smoothing_window_fraction,
)
if len(anti_cnarr):
# Combine target and antitarget bins
cnarr.add(anti_cnarr)
ref_matched.add(ref_anti)
# Find reference clusters, if requested
log2_key = "log2"
spread_key = "spread"
if do_cluster:
ref_log2_cols = [
col
for col in ref_matched.data.columns
if col == "log2" or col.startswith("log2")
]
if len(ref_log2_cols) == 1:
logging.info(
"Reference does not contain any sub-clusters; using %s", log2_key
)
else:
# Get correlations between test sample and each reference cluster
corr_coefs = np.array(
[cnarr.log2.corr(ref_matched[ref_col]) for ref_col in ref_log2_cols]
)
ordered = [
(k, r) for r, k in sorted(zip(corr_coefs, ref_log2_cols, strict=False), reverse=True)
]
logging.info(
"Correlations with each cluster:\n\t%s",
"\n\t".join([f"{k}\t: {r}" for k, r in ordered]),
)
log2_key = ordered[0][0]
if log2_key.startswith("log2_"):
suffix = log2_key.split("_", 1)[1]
spread_key = "spread_" + suffix
logging.info(" -> Choosing columns %r and %r", log2_key, spread_key)
# Normalize coverages according to the reference
# (Subtract the reference log2 copy number to get the log2 ratio)
cnarr.data["log2"] -= ref_matched[log2_key]
cnarr = apply_weights(cnarr, ref_matched, log2_key, spread_key)
cnarr.center_all(skip_low=True, diploid_parx_genome=diploid_parx_genome)
return cnarr
[docs]
def load_adjust_coverages(
cnarr: CopyNumArray,
ref_cnarr: CopyNumArray,
skip_low: bool,
fix_gc: bool,
fix_edge: bool,
fix_rmask: bool,
diploid_parx_genome: Optional[str],
smoothing_window_fraction: None = None,
) -> tuple[CopyNumArray, CopyNumArray]:
"""Load and filter probe coverages; correct using reference and GC."""
if "gc" in cnarr:
# Don't choke on Picard-derived files that have the GC column
cnarr = cnarr.keep_columns((*cnarr._required_columns, "depth"))
# No corrections needed if there are no data rows (e.g. no antitargets)
if not len(cnarr):
return cnarr, ref_cnarr[:0]
ref_matched = match_ref_to_sample(ref_cnarr, cnarr)
# Drop bins that had poor coverage in the pooled reference
ok_cvg_indices = ~mask_bad_bins(ref_matched)
logging.info("Keeping %d of %d bins", sum(ok_cvg_indices), len(ref_matched))
cnarr = cnarr[ok_cvg_indices]
ref_matched = ref_matched[ok_cvg_indices]
# Apply corrections for known systematic biases in coverage
cnarr.center_all(skip_low=skip_low, diploid_parx_genome=diploid_parx_genome)
# Skip bias corrections if most bins have no coverage (e.g. user error)
if (
cnarr["log2"] > params.NULL_LOG2_COVERAGE - params.MIN_REF_COVERAGE
).sum() <= len(cnarr) // 2:
logging.warning(
"WARNING: most bins have no or very low coverage; "
"check that the right BED file was used"
)
else:
# Smoothing window fraction converges on percentile binning, like Picard
frac = smoothing_window_fraction
if frac is None:
frac = max(0.01, len(cnarr) ** -0.5)
cnarr_index_reset = False
if fix_gc:
if "gc" in ref_matched:
logging.info("Correcting for GC bias...")
cnarr = center_by_window(cnarr, frac, ref_matched["gc"])
cnarr_index_reset = True
else:
logging.warning("WARNING: Skipping correction for GC bias")
if fix_edge:
logging.info("Correcting for density bias...")
edge_bias = get_edge_bias(cnarr, params.INSERT_SIZE)
cnarr = center_by_window(cnarr, frac, edge_bias)
cnarr_index_reset = True
if fix_rmask:
if "rmask" in ref_matched:
logging.info("Correcting for RepeatMasker bias...")
cnarr = center_by_window(cnarr, frac, ref_matched["rmask"])
cnarr_index_reset = True
else:
logging.warning("WARNING: Skipping correction for RepeatMasker bias")
if cnarr_index_reset:
ref_matched.data = ref_matched.data.reset_index(drop=True)
return cnarr, ref_matched
[docs]
def mask_bad_bins(cnarr: CopyNumArray) -> Series:
"""Flag the bins with excessively low or inconsistent coverage.
Returns
-------
np.array
A boolean array where True indicates bins that failed the checks.
"""
mask = (
(cnarr["log2"] < params.MIN_REF_COVERAGE)
| (cnarr["log2"] > -params.MIN_REF_COVERAGE)
| (cnarr["spread"] > params.MAX_REF_SPREAD)
)
if "depth" in cnarr:
mask |= cnarr["depth"] == 0
if "gc" in cnarr:
assert params.GC_MIN_FRACTION >= 0 and params.GC_MIN_FRACTION <= 1
assert params.GC_MAX_FRACTION >= 0 and params.GC_MAX_FRACTION <= 1
lower_gc_bound = min(params.GC_MIN_FRACTION, params.GC_MAX_FRACTION)
upper_gc_bound = max(params.GC_MIN_FRACTION, params.GC_MAX_FRACTION)
mask |= (cnarr["gc"] > upper_gc_bound) | (cnarr["gc"] < lower_gc_bound)
return mask
[docs]
def match_ref_to_sample(
ref_cnarr: CopyNumArray, samp_cnarr: CopyNumArray
) -> CopyNumArray:
"""Filter the reference bins to match the sample (target or antitarget)."""
# Assign each bin a unique string ID based on genomic coordinates
samp_labeled = samp_cnarr.data.set_index(pd.Index(samp_cnarr.coords()))
ref_labeled = ref_cnarr.data.set_index(pd.Index(ref_cnarr.coords()))
for dset, name in ((samp_labeled, "sample"), (ref_labeled, "reference")):
dupes = dset.index.duplicated()
if dupes.any():
raise ValueError(
(
"Duplicated genomic coordinates in {} set. Total duplicated regions: {}, starting with:\n"
"{}."
).format(
name,
len(dset.index[dupes]),
"\n".join(map(str, dset.index[dupes][:10])),
)
)
# Take the reference bins with IDs identical to those in the sample
ref_matched = ref_labeled.reindex(index=samp_labeled.index)
# Check for signs that the wrong reference was used
num_missing = pd.isna(ref_matched.start).sum()
if num_missing > 0:
raise ValueError(
f"Reference is missing {num_missing} bins found in {samp_cnarr.sample_id}"
)
x = ref_cnarr.as_dataframe(
ref_matched.reset_index(drop=True).set_index(samp_cnarr.data.index)
)
return x
[docs]
def center_by_window(
cnarr: CopyNumArray, fraction: float, sort_key: Union[Series, ndarray]
) -> CopyNumArray:
"""Smooth out biases according to the trait specified by sort_key.
E.g. correct GC-biased bins by windowed averaging across similar-GC
bins; or for similar interval sizes.
"""
# Separate neighboring bins that could have the same key
# (to avoid re-centering actual CNV regions -- only want an independently
# sampled subset of presumably overall-CN-neutral bins)
df = cnarr.data.reset_index(drop=True)
rng = np.random.default_rng(0xA5EED)
shuffle_order = rng.permutation(df.index)
# df = df.reindex(shuffle_order)
df = df.iloc[shuffle_order]
# Apply the same shuffling to the key array as to the target probe set
if isinstance(sort_key, pd.Series):
# XXX why
sort_key = sort_key.to_numpy()
sort_key = sort_key[shuffle_order]
# Sort the data according to the specified parameter
order = np.argsort(sort_key, kind="mergesort")
df = df.iloc[order]
biases = smoothing.rolling_median(df["log2"], fraction)
# biases = smoothing.savgol(df['log2'], fraction)
df["log2"] -= biases
fixarr = cnarr.as_dataframe(df)
fixarr.sort()
return fixarr
[docs]
def get_edge_bias(cnarr: CopyNumArray, margin: int) -> Series:
"""Quantify the "edge effect" of the target tile and its neighbors.
The result is proportional to the change in the target's coverage due to
these edge effects, i.e. the expected loss of coverage near the target
edges and, if there are close neighboring tiles, gain of coverage due
to "spill over" reads from the neighbor tiles.
(This is not the actual change in coverage. This is just a tribute.)
"""
output_by_chrom = []
for _chrom, subarr in cnarr.by_chromosome():
tile_starts = subarr["start"].to_numpy()
tile_ends = subarr["end"].to_numpy()
tgt_sizes = tile_ends - tile_starts
# Calculate coverage loss at (both edges of) each tile
losses = edge_losses(tgt_sizes, margin)
# Find tiled intervals within a margin (+/- bp) of the given probe
# (excluding the probe itself), then calculate the relative coverage
# "gain" due to the neighbors, if any
gap_sizes = tile_starts[1:] - tile_ends[:-1]
ok_gaps_mask = gap_sizes < margin
ok_gaps = gap_sizes[ok_gaps_mask]
left_gains = edge_gains(tgt_sizes[1:][ok_gaps_mask], ok_gaps, margin)
right_gains = edge_gains(tgt_sizes[:-1][ok_gaps_mask], ok_gaps, margin)
gains = np.zeros(len(subarr))
gains[np.concatenate([[False], ok_gaps_mask])] += left_gains
gains[np.concatenate([ok_gaps_mask, [False]])] += right_gains
output_by_chrom.append(gains - losses)
return pd.Series(np.concatenate(output_by_chrom), index=cnarr.data.index)
[docs]
def edge_losses(target_sizes: ndarray, insert_size: int) -> ndarray:
"""Calculate coverage losses at the edges of baited regions.
Letting i = insert size and t = target size, the proportional loss of
coverage near the two edges of the baited region (combined) is:
.. math :: i/2t
If the "shoulders" extend outside the bait $(t < i), reduce by:
.. math :: (i-t)^2 / 4it
on each side, or (i-t)^2 / 2it total.
"""
losses = insert_size / (2 * target_sizes)
# Drop the shoulder part that would extend past the bait
small_mask = target_sizes < insert_size
t_small = target_sizes[small_mask]
losses[small_mask] -= (insert_size - t_small) ** 2 / (2 * insert_size * t_small)
return losses
[docs]
def edge_gains(target_sizes: ndarray, gap_sizes: ndarray, insert_size: int) -> ndarray:
"""Calculate coverage gain from neighboring baits' flanking reads.
Letting i = insert size, t = target size, g = gap to neighboring bait,
the gain of coverage due to a nearby bait, if g < i, is::
.. math :: (i-g)^2 / 4it
If the neighbor flank extends beyond the target (t+g < i), reduce by::
.. math :: (i-t-g)^2 / 4it
If a neighbor overlaps the target, treat it as adjacent (gap size 0).
"""
if not (gap_sizes <= insert_size).all():
raise ValueError(
"Gaps greater than insert size:\n"
+ gap_sizes[gap_sizes > insert_size].head()
)
gap_sizes = np.maximum(0, gap_sizes)
gains = (insert_size - gap_sizes) ** 2 / (4 * insert_size * target_sizes)
# Drop the flank part that extends past this baited region
past_other_side_mask = target_sizes + gap_sizes < insert_size
g_past = gap_sizes[past_other_side_mask]
t_past = target_sizes[past_other_side_mask]
gains[past_other_side_mask] -= (insert_size - t_past - g_past) ** 2 / (
4 * insert_size * t_past
)
return gains
[docs]
def apply_weights(
cnarr: CopyNumArray,
ref_matched: CopyNumArray,
log2_key: str,
spread_key: str,
epsilon: float = 1e-4,
) -> CopyNumArray:
"""Calculate weights for each bin.
Bin weight is an estimate of (1 - variance) and within the range
``(0, 1]``.
Weights are derived from:
- Each bin's size
- Sample's genome-wide average (on/off-target) coverage depth
- Sample's genome-wide observed (on/off-target) bin variances
And with a pooled reference:
- Each bin's coverage depth in the reference
- The "spread" column of the reference (approx. stdev)
These estimates of variance assume the number of aligned reads per bin
follows a Poisson distribution, approximately log-normal.
Parameters
----------
cnarr : CopyNumArray
Sample bins.
ref_match : CopyNumArray
Reference bins.
log2_key : string
The 'log2' column name in the reference to use. A clustered reference
may have a suffix indicating the cluster, e.g. "log2_1".
spread_key : string
The 'spread' or 'spread_<cluster_id>' column name to use.
epsilon : float
Minimum value for bin weights, to avoid 0-weight bins causing errors
later during segmentation. (CBS doesn't allow 0-weight bins.)
Returns: The input `cnarr` with a `weight` column added.
"""
# Weight by sample-level features -- works for flat reference, too
logging.debug("Weighting bins by size and overall variance in sample")
simple_wt = np.zeros(len(cnarr))
# Calculate separately for on-, off-target bins
is_anti = cnarr["gene"].isin(params.ANTITARGET_ALIASES)
tgt_cna = cnarr[~is_anti]
tgt_var = (
descriptives.biweight_midvariance(tgt_cna.drop_low_coverage().residuals()) ** 2
)
bin_sz = np.sqrt(tgt_cna["end"] - tgt_cna["start"])
tgt_simple_wts = 1 - tgt_var / (bin_sz / bin_sz.mean())
simple_wt[~is_anti] = tgt_simple_wts
if is_anti.any():
# Check for a common user error
anti_cna = cnarr[is_anti]
anti_ok = anti_cna.drop_low_coverage()
frac_anti_low = 1 - (len(anti_ok) / len(anti_cna))
if frac_anti_low > 0.5:
# Off-target bins are mostly garbage -- skip reweighting
logging.warning(
"WARNING: Most antitarget bins ({:.2f}%, {:d}/{:d})"
" have low or no coverage; is this amplicon/WGS?".format(
100 * frac_anti_low, len(anti_cna) - len(anti_ok), len(anti_cna)
)
)
anti_var = descriptives.biweight_midvariance(anti_ok.residuals()) ** 2
anti_bin_sz = np.sqrt(anti_cna["end"] - anti_cna["start"])
anti_simple_wts = 1 - anti_var / (anti_bin_sz / anti_bin_sz.mean())
simple_wt[is_anti] = anti_simple_wts
# Report any difference in bin set variability
var_ratio = max(tgt_var, 0.01) / max(anti_var, 0.01)
if var_ratio > 1:
logging.info("Targets are %.2f x more variable than antitargets", var_ratio)
else:
logging.info(
"Antitargets are %.2f x more variable than targets", 1.0 / var_ratio
)
if (ref_matched[spread_key] > epsilon).any() and (
np.abs(np.mod(ref_matched[log2_key], 1)) > epsilon
).any():
# Pooled/paired reference only
logging.debug("Weighting bins by coverage spread in reference")
# NB: spread ~= SD, so variance ~= spread^2
fancy_wt = 1.0 - ref_matched[spread_key] ** 2
# Average w/ simple weights, giving this more emphasis
x = 0.9
weights = x * fancy_wt + (1 - x) * simple_wt
else:
# Flat reference, only 1 weight estimate
weights = simple_wt
return cnarr.add_columns(weight=weights.clip(epsilon, 1.0))