bio-single-cell-cnv-inference

name	bio-single-cell-cnv-inference
description	Infer large-scale copy-number alterations from tumor single-cell or single-nucleus RNA-seq to separate malignant from normal cells and call subclones, using inferCNV, copyKAT, Numbat, and SCEVAN. Use when separating malignant from normal cells in a tumor scRNA-seq dataset, inferring chromosome-arm CNVs or aneuploidy from expression, calling tumor subclones from single cells, choosing a CNV-inference method (reference-based vs reference-free, expression-only vs allele-aware), or deciding which cells are tumor before downstream analysis.
tool_type	r
primary_tool	inferCNV

Version Compatibility

Reference examples tested with: inferCNV 1.18+, copyKAT 1.1+, numbat 1.4+, SCEVAN 1.0+

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

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Copy-Number Inference from Single-Cell RNA-seq

"Which cells are tumor, and what CNVs and subclones do they carry?" -> Estimate large-scale copy-number from smoothed expression across genomic windows, compare against a normal reference, and cluster cells into malignant vs normal and into subclones.

• R (reference-based, expression-only): inferCNV - smooth expression along chromosomes against a defined normal reference, optional HMM for discrete CNV states
• R (reference-free, expression-only): copyKAT, SCEVAN - estimate the diploid baseline internally and segment, then classify aneuploid vs diploid
• R (haplotype-aware, allele + expression): Numbat - add phased B-allele frequency for the best subclone and copy-neutral-LOH resolution

Governing principle

Averaged expression over a genomic window is a PROXY for DNA copy number, not a measurement of it. A chromosome-arm gain raises the average expression of the many genes sitting on that arm, and a loss lowers it, so smoothing expression across long runs of contiguous genes reconstructs a coarse copy-number landscape. Three consequences follow and drive every decision. First, the signal is noisy and indirect: it must be smoothed over windows of dozens to hundreds of neighboring genes, and its resolution is chromosome-arm or large-segment (around 5 Mb), never focal genes or exons - this is the hard line separating it from DNA-based copy-number (copy-number/cnvkit-analysis, copy-number/gatk-cnv), which measures DNA read depth and resolves focal amplifications and deletions. Second, the value is RELATIVE: copy-number is only defined against a copy-neutral baseline, so a NORMAL reference is required, and the choice of that reference is the single most consequential decision - a wrong or mismatched reference fabricates CNVs out of ordinary cell-type expression differences. Third, calling a cell malignant is a CLUSTERING decision on this noisy proxy, so it is a hypothesis that needs orthogonal support (lineage markers, mutations, allele evidence), and subclone calls are even softer hypotheses.

Tumor CNV profiles are patient-PRIVATE, so CNV inference runs PER SAMPLE / per patient. Integrating or batch-correcting cells across patients before inference mixes distinct private karyotypes and erases the per-patient signal the analysis depends on (single-cell/batch-integration notes this). Integrate across patients only for shared transcriptional-state analysis, never as input to CNV calling.

CNV-quiet tumors are invisible to this approach. Many hematologic and low-grade tumors carry little large-scale CNV, so expression-based inference returns a near-flat profile - absence of inferred CNV is NOT evidence that cells are normal, and a confident malignant call then requires allele evidence or orthogonal markers. Balanced whole-genome doubling is invisible for the same reason and to all expression methods, not just copyKAT: per-cell library-size normalization removes a uniform ploidy multiple, so a 4N WGD reads copy-neutral against a 2N reference for inferCNV and Numbat's expression channel alike, and only allele or ploidy evidence reveals it.

Choosing a CNV-inference method

Method	Model / assumption	Reference	Allele-aware	Use when	Fails when
inferCNV	Smooth expression along chromosomes vs a normal reference; optional HMM for discrete states	Reference-based (needs normal cells)	No	A clean in-sample normal reference exists; want interpretable arm-level heatmap + HMM states	No trustworthy reference; CNV-quiet tumor; needs focal resolution
copyKAT	Bayesian segmentation + hierarchical clustering + GMM; estimates diploid baseline internally	Reference-free (optional known normals)	No	No reference cells annotated; want a quick aneuploid-vs-diploid call at ~5 Mb	Mostly-aneuploid sample with no diploid baseline; whole-genome doubling confuses the root
SCEVAN	Variational multichannel segmentation sharing breakpoints across a clone	Reference-free	No	Want automatic malignant/non-malignant + subclones in one call	Same baseline ambiguity as copyKAT; very sparse data
Numbat	Joint expression + phased B-allele frequency + population haplotypes; iterative phylogeny	Reference (expression) + population phasing	Yes	Best subclone resolution needed; copy-neutral LOH matters; allele counts obtainable	No BAM/phasing available; very low SNP coverage (shallow or snRNA)

Reference-based (inferCNV, Numbat expression side) is the most reliable when a clean normal reference is available; reference-free (copyKAT, SCEVAN) trades that for not needing one but is vulnerable to baseline ambiguity. Expression-only methods (inferCNV, copyKAT, SCEVAN) are simpler and need only counts; the allele-aware method (Numbat) is the most powerful for subclones and copy-neutral events but requires per-cell allele counts and phasing. Run an expression method to get the malignant/normal split, then Numbat when subclone structure is the question, and reconcile. When methods compete, verify current best practice against the installed package documentation before committing to one.

Choosing the normal reference

Goal: Pick a copy-neutral baseline that defines what "no CNV" looks like, since the reference choice determines whether the inferred CNVs are real or artifacts.

Approach: Prefer non-malignant cells from the SAME sample (T cells, B cells, myeloid, endothelial, fibroblasts identified by lineage markers) because they share the patient, protocol, and ambient-RNA background; fall back to an external normal only when no in-sample normals exist, and expect batch artifacts.

The reference cells must be confidently non-malignant and abundant enough for a stable baseline mean: aim for tens to hundreds of reference cells, not a handful, because a few cells give a noisy baseline that fabricates CNVs in every observation cell. A reference that is itself a malignant or stressed population, or a single mismatched cell type, will make every other cell look aneuploid relative to it. The reference must also MATCH the tumor's sex: because inferCNV works on expression, X-inactivation dosage-compensates most chrX expression so there is no uniform two-fold chrX shift, but an external or cross-individual normal (including shipped or pooled-donor references) of the opposite sex still fabricates a convincing uniform sex-chromosome CNV through chrY genes present in XY versus near-absent in XX, XIST high in XX versus silent in XY, and the attenuated X-inactivation escape genes - match sexes or drop chrX and chrY before inference. With no annotated normals, use a reference-free method (copyKAT, SCEVAN) rather than guessing a reference; do not pass tumor-contaminated cells as the reference.

inferCNV - reference-based expression smoothing

Goal: Build a chromosome-ordered expression heatmap against a defined normal reference and call discrete CNV states per region.

Approach: Assemble a raw counts matrix, a cell-to-group annotation file, and a gene-ordering file with genomic coordinates, name the normal groups as the reference, then run with droplet-appropriate cutoff, denoising, and the HMM.

library(infercnv)

infercnv_obj <- CreateInfercnvObject(
    raw_counts_matrix = 'counts.matrix',
    annotations_file = 'cell_annotations.txt',
    delim = '\t',
    gene_order_file = 'gene_ordering.txt',
    ref_group_names = c('Tcell', 'Myeloid'))

infercnv_obj <- infercnv::run(
    infercnv_obj,
    cutoff = 0.1,
    out_dir = 'infercnv_out',
    cluster_by_groups = TRUE,
    denoise = TRUE,
    HMM = TRUE,
    num_threads = 4)

ref_group_names lists the normal groups from the annotation file; set it to NULL only when no reference exists (less reliable). cutoff = 0.1 suits 10x and other droplet data; use cutoff = 1 for full-length Smart-seq. cluster_by_groups = TRUE clusters within annotated groups rather than forcing one global tree. The HMM (HMM_type = 'i6' default, six copy states; 'i3' for a simpler deletion/neutral/amplification model) yields per-region discrete states under out_dir.

copyKAT - reference-free aneuploid vs diploid

Goal: Classify cells as aneuploid (tumor) or diploid (normal) without an annotated reference, and obtain a per-cell copy-number matrix.

Approach: Pass the raw gene-by-cell matrix; copyKAT estimates the diploid baseline by segmentation and clustering, then labels each cell, optionally anchored by any known-normal barcodes.

library(copykat)

res <- copykat(
    rawmat = exp_rawdata,
    id.type = 'S',
    ngene.chr = 5,
    win.size = 25,
    KS.cut = 0.1,
    sam.name = 'tumor1',
    distance = 'euclidean',
    norm.cell.names = '',
    genome = 'hg20',
    n.cores = 4)

pred <- res$prediction
cna <- res$CNAmat

res$prediction$copykat.pred is aneuploid, diploid, or not.defined per cell; res$CNAmat holds smoothed copy-number values in ~220 kb bins (the output bin size; effective detection resolution is still ~5 Mb). KS.cut controls segmentation stringency (raise it for fewer, larger segments). Supplying confident normal barcodes via norm.cell.names anchors the diploid baseline and improves accuracy when the sample is mostly aneuploid.

Numbat - haplotype-aware allele + expression

Goal: Resolve subclones and copy-neutral LOH by combining smoothed expression with phased B-allele frequencies.

Approach: Generate per-cell allele counts and population phasing with the pileup_and_phase.R preprocessing script, build an expression reference from matched normals (or the shipped ref_hca), then run the joint model.

Rscript pileup_and_phase.R --label tumor1 --samples tumor1 \
    --bams tumor1.bam --barcodes barcodes.tsv \
    --gmap genetic_map_hg38_withX.txt.gz --snpvcf genome1K.phase3.SNP.vcf \
    --paneldir 1000G_panel/ --outdir numbat_out/ --ncores 8

library(numbat)

ref <- aggregate_counts(count_mat_normal, cell_annot)
out <- run_numbat(
    count_mat,
    lambdas_ref = ref,
    df_allele = df_allele,
    genome = 'hg38',
    t = 1e-5,
    ncores = 4,
    plot = TRUE,
    out_dir = 'numbat_out')

nb <- Numbat$new(out_dir = 'numbat_out')

df_allele is the allele dataframe written by pileup_and_phase.R (columns include cell, snp_id, CHROM, POS, AD, DP, GT). lambdas_ref is a gene-by-cell-type expression reference from aggregate_counts(count_mat, cell_annot) where cell_annot has cell and group columns, or the package-shipped ref_hca. t is the HMM transition probability. The loaded Numbat object exposes clone_post (clone assignments) and per-cell copy-number posteriors. Numbat needs no paired-normal DNA but does need a BAM and phasing reference.

Turning the inferCNV heatmap into per-cell malignant calls

Goal: Get a per-cell malignant-vs-normal label from inferCNV, which (unlike copyKAT and SCEVAN) returns a heatmap and HMM region states but no automatic per-cell class.

Approach: Subcluster the observation cells on their CNV signal and/or compute a per-cell CNV score, then threshold; carry the result onto the cells with add_to_seurat and validate the cut against lineage markers and allele/mutation evidence.

infercnv_obj <- infercnv::run(
    infercnv_obj, cutoff = 0.1, out_dir = 'infercnv_out',
    cluster_by_groups = FALSE, analysis_mode = 'subclusters',
    denoise = TRUE, HMM = TRUE, num_threads = 4)

seurat_obj <- infercnv::add_to_seurat(
    seurat_obj = seurat_obj, infercnv_output_path = 'infercnv_out', top_n = 10)

obs <- read.table('infercnv_out/infercnv.observations.txt', header = TRUE, row.names = 1)
cnv_score <- colSums((obs - 1)^2)
malignant  cnv_score  quantilecnv_score 

analysis_mode = 'subclusters' (with cluster_by_groups = FALSE) partitions the observation cells by CNV signal so a malignant subcluster separates from a copy-neutral one. The per-cell CNV score (sum of squared deviation of the denoised infercnv.observations.txt profile from the copy-neutral value, or each cell's correlation to the mean putative-tumor profile) gives a continuous malignancy axis to threshold; add_to_seurat writes per-cell and per-chromosome CNA metadata back onto the object for plotting. The malignant/normal cut is a hypothesis: confirm it against lineage markers (single-cell/cell-annotation) and allele or mutation evidence, never treat the threshold as ground truth.

Threshold and parameter reference

Parameter	Tool	Default / typical	Rationale
cutoff	inferCNV	0.1 (droplet), 1 (Smart-seq)	Drops genes below mean expression; droplet data are sparse so the threshold is lower
HMM_type	inferCNV	i6	Six copy states (0 to 3+) give finer state calls; i3 (del/neutral/amp) is simpler and more robust
ref_group_names	inferCNV	named normals	The copy-neutral baseline; NULL only when no reference exists, at a reliability cost
ngene.chr	copyKAT	5	Minimum genes per chromosome per cell so a cell has signal on each chromosome
win.size	copyKAT	25	Genes per segment for smoothing; larger windows denoise but blur small events
KS.cut	copyKAT	0.1	Segmentation stringency; raise for fewer, larger segments in noisy data
genome	copyKAT	hg20	Must match the assembly the gene coordinates came from (hg20 or mm10)
t	Numbat	1e-5	HMM transition probability; lower favors longer segments
resolution (~5 Mb)	all expression methods	~5 Mb	The floor of expression-based CNV; focal events below this are invisible

Common Errors

Symptom	Cause	Fix
Every cell looks aneuploid, including the immune cells	Reference cells are tumor-contaminated or a mismatched cell type	Re-pick confident non-malignant in-sample normals by lineage markers; do not use a stressed/malignant population as reference
Flat profile, no CNVs called, but cells are clearly tumor	CNV-quiet / low-grade tumor, or snRNA sparsity	Absence of CNV is not normality; switch to allele-aware Numbat or confirm malignancy with markers/mutations
copyKAT cannot find a diploid baseline / labels almost all aneuploid	Mostly-aneuploid sample with no internal diploid cells, or balanced whole-genome doubling that library-size normalization hides	Supply known-normal barcodes via norm.cell.names, or use inferCNV with an external reference; confirm ploidy with allele/DNA evidence since balanced WGD looks copy-neutral to all expression methods
Uniform chrX gain/loss and a chrY call across all tumor cells	Reference and tumor are opposite sex (external/shipped/pooled-donor reference)	Match reference and tumor sex, or drop chrX and chrY before inference
Recurrent localized segment over 6p, 14q, 22q, or 2p that tracks lineage	High, variable HLA (MHC 6p) and immunoglobulin/TCR expression clusters genomically and mimics a segment, especially with immune references	Mask or distrust segments over HLA and Ig/TCR loci; do not call them CNV
Stripe artifacts that track proliferating cells	Cell-cycle and high-expression gene programs mimic CNV	Account for / regress cell cycle, enable denoising, and treat cycle-correlated bands skeptically
Subclones from inferCNV/copyKAT do not replicate	Expression-only subclone calls are weak hypotheses	Confirm with Numbat allele evidence or DNA; report subclones as hypotheses
CNV signal vanishes after integrating samples	Cross-patient integration erased patient-private karyotypes	Run CNV inference per sample BEFORE any cross-patient integration
Genes silently dropped / wrong chromosome bands	Gene-order file or genome build mismatched to the counts	Match gene_order_file and genome to the same assembly and gene IDs as the matrix

Related Skills

• single-cell/preprocessing - QC and normalization that precede CNV inference; ambient RNA and depth affect the proxy
• single-cell/clustering - Provides the cell groups and the malignant-vs-normal clustering the CNV call refines
• single-cell/cell-annotation - Identifies the non-malignant lineages used as the normal reference
• single-cell/batch-integration - Why CNV inference must run per patient before any cross-patient integration
• copy-number/cnvkit-analysis - DNA/WES-based copy-number that measures read depth and resolves focal events, the orthogonal contrast to this expression proxy

References

• Patel AP et al. 2014, Science 344:1396-1401 - single-cell RNA-seq of glioblastoma; first per-cell CNV estimation from averaged expression.
• Tirosh I et al. 2016, Science 352:189-196 - melanoma scRNA-seq; inferring large-scale CNV from smoothed expression to separate malignant cells (the inferCNV approach).
• Gao R et al. 2021, Nat Biotechnol 39:599-608 - copyKAT; reference-free Bayesian segmentation calling aneuploid vs diploid and subclones at ~5 Mb.
• Gao T et al. 2023, Nat Biotechnol 41:417-426 - Numbat; haplotype-aware joint allele and expression somatic CNV inference from scRNA-seq.
• Muller S et al. 2018, Bioinformatics 34:3217-3219 - CONICS/CONICSmat; arm-level CNV from expression mapped to tumor sub-clones.
• De Falco A et al. 2023, Nat Commun 14:1074 - SCEVAN; variational multichannel segmentation auto-classifying malignant cells and clonal substructure.