GPTomics/bio-copy-number-allele-specific-copy-number

Version Compatibility

Reference examples tested with: ASCAT 3.1+, Sequenza 3.0+ (sequenza-utils 3.0+), FACETS 0.6+ (snp-pileup), PureCN 2.6+, R 4.3+, Python 3.10+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('ASCAT') / 'sequenza' / 'facets' / 'PureCN', then ?function
• CLI: sequenza-utils --version, snp-pileup --help

Sequenza 3.0 depends on the copynumber Bioconductor package, REMOVED from Bioconductor 3.18+ (2023). Install a maintained fork (ShixiangWang/copynumber or igordot/copynumber) before Sequenza will load. ASCAT's GC-correction function was renamed across 2.x->3.x (ascat.GCcorrect -> ascat.correctLogR) — verify against the installed version.

Allele-Specific Copy Number

"How many copies of each allele, in what fraction of cells, at what tumor purity" -> Jointly model read depth and B-allele frequency to fit tumor purity, ploidy, and integer major/minor copy number per segment. Depth alone gives only relative copy ratio; depth + BAF gives absolute allele-specific copy number. This skill is required whenever the question involves LOH, absolute copy number, purity, ploidy, or whole-genome doubling — CNVkit and GATK somatic CNV cannot answer those.

• R: ASCAT (WGS, SNP array), sequenza (WES/WGS), facets (panel/WES/WGS), PureCN (tumor-only panel/WES)
• CLI: purple (Hartwig WGS pipeline, with AMBER + COBALT)

The Identifiability Problem — Why This Is Hard

Purity and ploidy are not identifiable from depth alone. The same log-ratio profile is explained equally well by many (purity, ploidy) pairs: a homozygous deletion at 30% purity looks identical to a heterozygous deletion at 60% purity; an entire profile can be reinterpreted at 2x ploidy with halved purity. Every allele-specific caller breaks this degeneracy by adding BAF — allelic imbalance constrains which solution is real. The consequence: the likelihood surface is multimodal, the fit can lock onto an integer-multiple of the true ploidy, and a single point estimate must never be trusted without inspecting the fit diagnostic (ASCAT sunrise plot, Sequenza cellularity/ploidy contour, FACETS dipLogR). A real published example: the same tumor scored ploidy 4.27 by FACETS (WGS) and 2.42 by ASCAT (SNP array).

Caller Taxonomy

| Tool | Input | Segmentation | Best for | Fails when | |------|-------|--------------|----------|------------| | ASCAT | SNP array or WGS logR+BAF | ASPCF (allele-specific PCF) | WGS, SNP6, large cohorts | Near-diploid genome with few aberrations cannot anchor purity -> defaults toward purity ~100% | | Sequenza | Tumor-normal WES/WGS (seqz) | copynumber PCF | Exome, accessible install | Picks a near-diploid local optimum; needs manual review of alternative solutions | | FACETS | Tumor-normal, snp-pileup | Joint logR+BAF CBS | Targeted panels, WES, clinical NGS | cval too low -> hyperfragmentation; EM locks onto an integer-multiple ploidy | | PURPLE | WGS, AMBER+COBALT+SVs | Integrates SV breakpoints | WGS with matched SV calls | Targeted/WES (designed for WGS); needs the Hartwig tool stack | | PureCN | Tumor-only WES/panel + PoN | Coverage + VCF, normal DB | No matched normal | Sparse hets; small panels; needs a well-built normal database | | Battenberg | WGS logR+BAF, phased | ASCAT-based clonal + subclonal | Subclonal CN, clonal evolution | Heavy; needs phasing reference — see subclonal-copy-number |

Decision Tree by Data Type

| Scenario | Recommended caller | Rationale | |----------|--------------------|-----------| | Tumor-normal WGS | ASCAT or PURPLE | PURPLE if SV calls available (resolves breakpoints); ASCAT otherwise | | Tumor-normal WES | Sequenza or FACETS | Both joint logR+BAF; FACETS faster, Sequenza reports alternative solutions | | Targeted panel (tumor-normal) | FACETS | Designed for panel het density; clinical-NGS standard | | Tumor-only panel / WES | PureCN | Models a normal database; the standard tumor-only solution | | SNP array (legacy) | ASCAT | ASCAT was built for SNP arrays | | Subclonal CN / clonal evolution | Battenberg / TITAN | See subclonal-copy-number | | Only relative gain/loss needed | CNVkit / GATK | Allele-specific machinery is unnecessary |

FACETS — Tumor-Normal Allele-Specific CN

Goal: Fit purity, ploidy, and integer allele-specific CN for a panel or WES pair.

Approach: Pile up read counts at common SNPs with snp-pileup, then run the two-pass FACETS workflow — a high-cval purity run whose dipLogR seeds a low-cval sensitivity run for focal events.

# Step 1: pileup at dbSNP common sites (normal first, then tumor)
snp-pileup -g -q15 -Q20 -P100 -r25,0 dbsnp_common.vcf.gz \
    sample.snp_pileup.csv.gz normal.bam tumor.bam

library(facets)
set.seed(1234)                                  # FACETS uses random initialization
rcmat <- readSnpMatrix('sample.snp_pileup.csv.gz')
xx <- preProcSample(rcmat)                      # gbuild default 'hg19'; pass gbuild='hg38' for GRCh38

# Pass 1: purity/ploidy at a coarse cval (panels ~150-300; WGS ~25-100)
oo1 <- procSample(xx, cval = 300)
fit1 <- emcncf(oo1)

# Pass 2: focal sensitivity, seeded by the diploid baseline from pass 1
oo2 <- procSample(xx, cval = 150, dipLogR = oo1$dipLogR)
fit2 <- emcncf(oo2)

cat('purity', fit2$purity, 'ploidy', fit2$ploidy, 'dipLogR', oo2$dipLogR, '\n')
# fit2$cncf has per-segment tcn.em (total CN) and lcn.em (minor CN); lcn.em == 0 -> LOH
plotSample(x = oo2, emfit = fit2)               # ALWAYS inspect this diagnostic plot

Sequenza — Exome Tumor-Normal

Goal: Estimate cellularity/ploidy and allele-specific CN from a WES pair, with explicit alternative solutions.

Approach: Build a seqz file from the BAMs, bin it, then run the extract/fit/results chain; inspect the cellularity/ploidy contour and the reported alternative solutions.

sequenza-utils bam2seqz -n normal.bam -t tumor.bam --fasta ref.fa \
    -gc hg38.gc50.wig.gz -o sample.seqz.gz
sequenza-utils seqz_binning --seqz sample.seqz.gz -w 50 -o sample.bin.seqz.gz

library(sequenza)
seqz <- sequenza.extract('sample.bin.seqz.gz')
CP <- sequenza.fit(seqz)                        # grid search over cellularity x ploidy
sequenza.results(seqz, CP, 'sampleID', out.dir = 'sequenza_out')
# Inspect *_CP_contours.pdf and *_alternative_solutions.txt before accepting the fit.

ASCAT — WGS / SNP Array

Goal: Fit purity (rho), ploidy (psi), and allele-specific CN genome-wide.

Approach: Load logR/BAF, correct for GC (and optionally replication timing), segment with ASPCF, run the ASCAT fit, and read the sunrise plot.

library(ASCAT)
ascat.bc <- ascat.loadData('Tumor_LogR.txt', 'Tumor_BAF.txt',
                           'Germline_LogR.txt', 'Germline_BAF.txt')
ascat.bc <- ascat.correctLogR(ascat.bc, GCcontentfile = 'GC_G1000.txt',
                              replictimingfile = 'RT_G1000.txt')   # RT optional
ascat.bc <- ascat.aspcf(ascat.bc)
ascat.output <- ascat.runAscat(ascat.bc, gamma = 1)   # gamma=1 for NGS; ~0.55 for arrays
# ascat.output$purity, $ploidy, $goodnessOfFit; $nA / $nB are major/minor CN per segment
# Inspect the sunrise plot: banding at multiples of ploidy signals an ambiguous fit.

PureCN — Tumor-Only

Goal: Recover purity, ploidy, allele-specific CN, and LOH without a matched normal.

Approach: Build a normal database (PoN) once, then run runAbsoluteCN with the tumor coverage and a VCF; PureCN uses the normal DB and a mapping-bias model in place of a matched normal.

library(PureCN)
ret <- runAbsoluteCN(
    tumor.coverage.file = 'tumor_coverage.txt.gz',
    vcf.file = 'tumor.vcf.gz',
    normalDB = readRDS('normalDB.rds'),       # built once from >= ~20 process-matched normals
    genome = 'hg38', sampleid = 'tumor',
    interval.file = 'baits_intervals.txt')
# ret$results[[1]]$purity / $ploidy; createCurationFile() flags fits needing manual review

Failure Modes

ASCAT defaults to ~100% purity on a near-diploid genome

Trigger: A tumor with very few copy-number aberrations and overall ploidy near 2.

Mechanism: ASCAT infers purity from the depth/BAF deviation of aberrant segments. With almost no aberrant segments there is nothing to anchor purity against, so the grid search drifts to the boundary.

Symptom: Reported purity ~1.0 (or implausibly high) with an almost flat profile; the sunrise plot is nearly featureless.

Fix: Treat purity as indeterminate, not 100%. Cross-check with an orthogonal estimate (SNV VAF mode for clonal mutations, pathology estimate). A genuinely quiet genome simply does not support a confident purity call.

FACETS hyperfragmentation from too-low cval

Trigger: cval set too low for the data (e.g. panel data run at WGS-scale cval).

Mechanism: cval is the segmentation critical value; low values let the segmenter split on noise, shattering the profile into spurious micro-segments.

Symptom: Hundreds of tiny segments; tcn.em/lcn.em incoherent with cnlr.median; jagged plotSample output.

Fix: Use cval ~150-300 for panels/WES, ~25-100 for WGS. Run the two-pass workflow (coarse purity run -> dipLogR-seeded sensitivity run). If naive tcn and EM tcn.em disagree wildly, the fit is bad — re-tune cval.

Integer-multiple ploidy flip

Trigger: Any allele-specific caller on a genome where the diploid baseline is ambiguous (few hets, low purity, or genuine WGD).

Mechanism: The likelihood surface has near-equal modes at ploidy P and 2P; the optimizer can select the wrong one, halving or doubling all copy numbers.

Symptom: Two callers disagree by a factor of ~2 in ploidy; "balanced" CN states that should be odd come out even (or vice versa); SNV multiplicities inconsistent with the called CN.

Fix: Inspect the fit diagnostic (sunrise/contour). Cross-check ploidy against the fraction of the genome at odd vs even CN and against clonal-SNV VAF. Prefer the solution consistent with known biology; if truly ambiguous, report both.

Sequenza fails to load / picks a near-diploid optimum

Trigger: Fresh Sequenza install on Bioconductor 3.18+; or accepting sequenza.fit's point estimate without review.

Mechanism: Sequenza depends on copynumber, removed from Bioconductor 3.18+. Separately, the LPP grid search can settle on a near-diploid local optimum when a higher-ploidy solution fits comparably.

Symptom: copynumber not available at load; or a ploidy ~2 call that conflicts with visible large-scale imbalance.

Fix: Install a maintained copynumber fork. Always inspect *_CP_contours.pdf and *_alternative_solutions.txt; if a non-diploid alternative fits nearly as well and matches the BAF pattern, prefer it.

Low-purity death zone

Trigger: Tumor purity below ~40% (common in breast, lung adenocarcinoma, melanoma).

Mechanism: Allelic imbalance and depth deviation both shrink with purity; below ~40% the signal approaches the noise floor and segmentation fails.

Symptom: No confident fit; purity estimate unstable across reruns; flat BAF.

Fix: Below ~40% purity, allele-specific calling is unreliable; below ~20% it is not possible with bulk sequencing. Report indeterminate; consider deeper sequencing or microdissection.

Reconciliation: When Callers Disagree

| Pattern | Likely cause | Action | |---------|--------------|--------| | Caller A ploidy ~= 2x caller B | Integer-multiple ploidy flip | Check odd/even CN fraction and SNV multiplicity; pick the biology-consistent fit | | Purity differs widely, ploidy agrees | One caller hit a boundary on a quiet genome | Trust the caller whose diagnostic plot shows real structure | | FACETS vs ASCAT integer CN differ | Different segmentation (CBS vs ASPCF) at boundaries | Compare segment edges; arm-level calls usually agree, focal may not | | Tumor-only (PureCN) vs tumor-normal differ | Tumor-only has weaker purity constraint | Prefer the matched-normal fit when available |

Operational rule: Report an allele-specific fit as confident only when (1) the fit diagnostic (sunrise/contour/dipLogR) shows clear, non-degenerate structure, (2) purity is above ~40%, (3) ploidy is consistent with the odd/even CN fraction and with clonal-SNV multiplicity, and (4) for ambiguous cases, the alternative solutions have been reviewed. A bare purity/ploidy number with no diagnostic inspection is not a result.

Quantitative Thresholds

| Threshold | Value | Source / Rationale | |-----------|-------|--------------------| | Purity floor | ~40% reliable; ~20% absolute floor | Below ~40% segmentation fails (Gusnanto 2012; sCNAphase) | | FACETS cval (panel/WES) | 150-300 | FACETS docs; lower -> hyperfragmentation | | FACETS cval (WGS) | 25-100 | FACETS docs; scales with marker density | | ASCAT gamma | 1.0 (NGS); ~0.55 (SNP array) | ASCAT docs; platform-specific logR shrinkage | | LOH definition | minor CN (lcn) = 0 | Minor allele lost; total CN may still be >= 2 (CN-neutral LOH) | | PureCN normal DB size | >= ~20 process-matched normals | PureCN docs; mapping-bias and coverage model | | Het SNP density for stable BAF | thousands genome-wide / hundreds per arm | Sparse hets give noisy allele-fraction segmentation |

Common Errors

| Error / symptom | Cause | Solution | |-----------------|-------|----------| | Sequenza: copynumber not found | Removed from Bioconductor 3.18+ | Install a maintained copynumber fork | | ascat.GCcorrect not found | Renamed in ASCAT 3.x | Use ascat.correctLogR | | FACETS profile shattered | cval too low | Raise cval; two-pass workflow | | Purity reported ~1.0, flat genome | Near-diploid, unanchored | Report indeterminate; cross-check with SNV VAF | | All CN halved or doubled vs expectation | Integer-multiple ploidy flip | Inspect fit diagnostic; check SNV multiplicity | | PureCN unstable tumor-only fit | Sparse hets / weak normal DB | Larger normal DB; deeper sequencing; flag for curation |

References

• Van Loo P et al 2010. Allele-specific copy number analysis of tumors. PNAS 107:16910 (ASCAT)
• Ross EM et al 2021. Allele-specific multi-sample copy number segmentation in ASCAT. Bioinformatics 37:1909
• Favero F et al 2015. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann Oncol 26:64
• Shen R, Seshan VE 2016. FACETS: allele-specific copy number and clonal heterogeneity analysis. Nucleic Acids Res 44:e131
• Riester M et al 2016. PureCN: copy number calling and SNV classification using targeted short read sequencing. Source Code Biol Med 11:13
• Priestley P et al 2019. Pan-cancer whole-genome analyses of metastatic solid tumours (PURPLE). Nature 575:210

Related Skills

• copy-number/copy-ratio-segmentation - logR normalization and segmentation feeding these callers
• copy-number/subclonal-copy-number - Battenberg/TITAN subclonal CN, whole-genome doubling
• copy-number/hrd-scoring - LOH/LST/TAI scars computed from allele-specific output
• copy-number/cnvkit-analysis - Relative depth-only calling (when allelic resolution is not needed)
• copy-number/gatk-cnv - GATK somatic CNV (relative; no purity/ploidy)
• variant-calling/vcf-basics - SNV VCFs supplying BAF and clonal-mutation cross-checks