Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.
tool_type
mixed
primary_tool
DNAcopy
Version Compatibility
Reference examples tested with: R 4.3+ with DNAcopy 1.76+, Python 3.10+ with numpy 1.26+, pandas 2.2+; QDNAseq 1.38+ (optional, GC/mappability normalization).
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('DNAcopy') then ?segment to confirm arguments
Python: pip show numpy pandas
If code throws an error, introspect the installed package and adapt the example. CBS lives in Bioconductor DNAcopy; HMM segmentation is provided by caller-specific backends (CNVkit uses pomegranate; HaarSeg has its own R/Python packages).
Copy-Ratio Segmentation
"Turn noisy per-bin depth into clean copy-number segments" -> Two stages, both error-prone. First, normalize the depth profile so the only remaining variation is copy number (not GC, mappability, or replication timing). Second, partition the normalized profile into segments of constant copy number. The segmentation algorithm choice has a bias signature, and the diploid-baseline choice can invert every call.
predictable
R: DNAcopy::segment (CBS, the reference implementation)
Python: HMM via pomegranate; HaarSeg via haarseg
The output feeds every CNV caller (cnvkit-analysis, gatk-cnv, allele-specific-copy-number)
Stage 1: Why Depth Is Biased Before It Is Copy Number
Raw read depth confounds copy number with three systematic biases:
Bias
Cause
Correction
GC content
PCR efficiency and probe hybridization vary with GC
Loess fit of depth vs GC (QDNAseq), or matched normal
Mappability
Multi-mapping reads under-counted in repetitive regions
Panel of normals — the dominant bias for exomes/panels
The key postdoc-level point: GC correction alone is insufficient. The wave artifact in cancer WGS is driven by replication timing, a biological signal GC normalization cannot flatten. Only a matched normal or a panel of normals removes it. This is why a CNVkit flat reference (GC-only) produces systematic false focal calls and why GATK tangent normalization exists.
Stage 2: Segmentation Algorithm Taxonomy
Algorithm
Model
Strength
Fails when
CBS (circular binary segmentation)
Recursive t-statistic breakpoint test
High precision; excellent on small focal segments
Low depth (~3x): recall drops to ~42% (worse under over-dispersed counts); ~2 orders slower; fragments across assembly gaps
HMM
Hidden CN states, emission + transition
Depth-robust; high recall at low coverage
Less precise on small focal segments (~5 kb: ~76% precision vs CBS ~96%, Poisson model); EM finds only local optima
HaarSeg
Wavelet (Haar) multiscale edge detection
Very fast; good for shallow WGS
Less precise breakpoints than CBS; threshold-sensitive
Fused lasso (flasso)
L1-penalized piecewise-constant fit
Smooth; tunable sparsity
Penalty hard to set; can over-smooth focal events
ASPCF
Allele-specific piecewise-constant fit
Joint logR+BAF segmentation (ASCAT)
Needs BAF; see allele-specific-copy-number
Quantitative benchmark (Zhang et al 2024, Brief Bioinform): the cited precision/recall numbers (CBS ~42% recall at 3x; HMM ~81% recall at 3x; CBS ~96% precision vs HMM ~76% on 5 kb focal segments under a Poisson model) summarise that paper's reported direction of the trade-off. Verify the exact figures against the published tables before quoting them in print; the qualitative trade-off (depth-vs-event-size, CBS-vs-HMM) is robust across recent benchmarks but the precise percentages depend on the simulation model (Poisson vs over-dispersed negative-binomial). There is no universally correct choice.
Goal: Remove GC-content bias from a per-bin depth profile.
Approach: Fit a loess curve of depth versus GC content, divide each bin by its fitted value, log2-transform. This corrects GC but not replication timing — use a normal for that.
import numpy as np
import pandas as pd
from statsmodels.nonparametric.smoothers_lowess import lowess
defgc_correct(bins):
'''GC-correct a per-bin depth profile. bins: columns chrom, start, depth, gc.
Returns log2 copy ratio relative to the GC-corrected genome median.'''
df = bins[(bins['depth'] > 0) & bins['gc'].between(0.3, 0.7)].copy()
fitted = lowess(df['depth'], df['gc'], frac=0.3, return_sorted=False)
df['corrected'] = df['depth'] / fitted
df['log2'] = np.log2(df['corrected'] / df['corrected'].median())
return df
For exomes and panels, a panel of normals (per-bin median of normals, or PCA denoising) is preferred over GC-only correction because it also removes capture and replication-timing bias.
Segmentation — CBS with DNAcopy
Goal: Segment a normalized log2 profile into copy-number regions.
Approach: Build a CNA object, smooth single-bin outliers, run CBS, then merge adjacent segments whose means differ by less than a noise-scaled threshold (sdundo).
library(DNAcopy)# bins: data frame with chrom, maploc (bin midpoint), log2
cna <- CNA(genomdat = bins$log2, chrom = bins$chrom, maploc = bins$maploc,
data.type ='logratio', sampleid ='tumor')
cna <- smooth.CNA(cna)# damp single-bin outliers# alpha = breakpoint significance; undo.splits='sdundo' merges segments whose means# are within undo.SD noise standard deviations -- the main guard against oversegmentation.
seg <- segment(cna, alpha =0.01, undo.splits ='sdundo', undo.SD =2,
verbose =1)
write.table(seg$output,'tumor.segments.tsv', sep ='\t',quote=FALSE, row.names =FALSE)
Failure Modes
Oversegmentation / hyperfragmentation
Trigger: CBS alpha too liberal, undo.SD too small, or a noisy (high-MAD) profile; FACETS cval too low.
Mechanism: The breakpoint test fires on noise; the profile shatters into many tiny segments that do not correspond to real copy-number changes.
Symptom: Hundreds of short segments; segment count scales with noise, not biology; downstream integer CN incoherent with per-bin medians.
Fix: Raise alpha toward 0.01 or stricter, increase undo.SD (e.g. 2-3), or denoise the input first (better PoN, drop low-coverage bins). Three signatures (Steele 2022) had to be discarded as oversegmentation artifacts — fragmentation propagates into every downstream analysis, including copy-number signatures.
The diploid-baseline centering trap
Trigger: Centering the log2 profile on its median or mode in a hyper-aneuploid or whole-genome-doubled genome.
Mechanism: Centering assumes the commonest log2 value is diploid. In a WGD genome the commonest state is tetraploid; centering on it shifts the whole profile so true diploid regions read as deletions and amplifications read as neutral.
Symptom: Genome-wide gain or loss inconsistent with biology; segmentation is fine but every call has the wrong sign.
Fix: Do not depth-center aneuploid genomes. Anchor the diploid baseline with BAF/SNV data via an allele-specific caller, which estimates absolute ploidy. GISTIC and most callers require a correctly centered seg file as input.
CBS recall degrades at low depth
Trigger: CBS on shallow data (< ~5x WGS, or low-coverage bins).
Mechanism: The two-sample t-statistic loses power when per-bin variance swamps the mean difference; CBS misses real breakpoints (recall ~42% at 3x under a Poisson model, worse under over-dispersed counts), while the segments it does call stay fairly precise.
Symptom: Real events absent from the segmentation; recall poor on a genome with known CNVs.
Fix: Use HMM (depth-robust, ~81% recall at 3x) or HaarSeg for shallow data; or increase bin size to raise per-bin counts before segmenting.
CBS fragments across assembly gaps
Trigger: CBS run over a profile with centromere/telomere gaps not handled as chromosome breaks.
Mechanism: CBS treats gapped data as independent subsets; spurious breakpoints appear at gap edges.
Fix: Segment per chromosome arm, or supply gap-aware chromosome coordinates so CBS does not bridge gaps.
HMM EM converges to a local optimum
Trigger: HMM with poor initial parameters or too few iterations.
Mechanism: Baum-Welch EM is not globally optimal; emission/transition parameters can settle in a local optimum, mis-assigning states.
Symptom: Reruns give different state assignments; CN states inconsistent with the visible profile.
Fix: Use informative priors (diploid-centered for germline, broader for tumor), run multiple initializations, and sanity-check state means against the per-bin distribution.
Reconciliation: When Segmentations Disagree
Pattern
Likely cause
Action
CBS shatters where HMM gives clean broad segments
Low depth — CBS over-fits noise
Trust HMM; CBS needs more depth
HMM misses a focal event CBS finds
HMM window resolution too coarse
Trust CBS for focal; HMM blurs small events
Both agree on arms, differ on focal boundaries
Different breakpoint resolution
Arm calls are robust; treat focal boundaries as approximate
Segmentation differs run-to-run
HMM local optima, or unfixed random seed
Fix seeds; use multiple HMM initializations
Operational rule: Match the algorithm to depth and event size — CBS for adequate-depth focal work, HMM/HaarSeg for shallow or broad. Confirm the diploid baseline against an allele-specific ploidy estimate before any sign-dependent interpretation. Report arm-level segments with confidence; treat focal boundaries as algorithm-dependent.
Quantitative Thresholds
Threshold
Value
Source / Rationale
CBS alpha
0.01
DNAcopy default; breakpoint significance
CBS undo.SD
2-3
Merges segments within N noise SD; guards oversegmentation
Depth where CBS recall degrades
< ~5x WGS
Zhang 2024; CBS recall falls sharply (HMM is depth-robust)
GC range kept for loess
0.3-0.7
Extreme-GC bins are unreliable; standard restriction
Bin size, shallow WGS CNV
~500 kb - 1 Mb
Larger bins raise per-bin counts for stable segmentation
Sample MAD usable
< 0.5
Above this, segmentation chases noise regardless of algorithm
