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We now provide a ready-to-run Docker container that includes the package and all prerequisites (see section Docker). Alternatively, you can follow the installation procedure below.

Numbat uses cellsnp-lite for generating SNP pileup data and eagle2 for phasing. Please follow their installation instructions and make sure their binary executables can be found in your $PATH.

  1. cellsnp-lite
  2. eagle2
  3. samtools

Additionally, Numbat needs a common SNP VCF and phasing reference panel. You can use the 1000 Genome reference below:

  1. 1000G SNP VCF
# hg38
# hg19
  1. 1000G Reference Panel
# hg38
# hg19

You can install the numbat R package via CRAN:

install.packages('numbat', dependencies = TRUE)

Alternatively, you can install the GitHub version:



We provide a ready-to-run Docker container that includes the Numbat R package and all of its dependencies. You can launch it as follows:

docker run -v /work:/mnt/mydata -it pkharchenkolab/numbat-rbase:latest /bin/bash

where /work is a local data folder you would like to access and write to. Within the container, cellsnp-lite, eagle2 and samtools are available in path and the 1000 Genome SNP VCF and phasing panel files (hg38) are stored under /data. You can also launch R and run interactive analysis using Numbat (or run an R script).

Preparing data

  1. Prepare the allele data. Run the preprocessing script (numbat/inst/bin/pileup_and_phase.R) to count alleles and phase SNPs.
usage: pileup_and_phase.R [-h] --label LABEL --samples SAMPLES --bams BAMS
                          [--barcodes BARCODES] --gmap GMAP [--eagle EAGLE]
                          --snpvcf SNPVCF --paneldir PANELDIR --outdir OUTDIR
                          --ncores NCORES [--UMItag UMITAG]
                          [--cellTAG CELLTAG] [--smartseq] [--bulk]

Run SNP pileup and phasing with 1000G

  -h, --help           show this help message and exit
  --label LABEL        Individual label. One per run.
  --samples SAMPLES    Sample name(s); comma delimited if multiple. 
                       All samples must belong to the same individual.
  --bams BAMS          BAM file(s); one per sample, comma delimited if multiple.
  --barcodes BARCODES  Cell barcode file(s); one per sample, 
                       comma delimited if multiple.
  --gmap GMAP          Path to genetic map provided by Eagle2 (e.g.
  --eagle EAGLE        Path to Eagle2 binary file
  --snpvcf SNPVCF      SNP VCF for pileup
  --paneldir PANELDIR  Directory to phasing reference panel (BCF files)
  --outdir OUTDIR      Output directory
  --ncores NCORES      Number of cores
  --smartseq           Running with SMART-seq mode; Supply a txt file containing 
                       directories of BAM files to --bams and a txt file 
                       containing cell names to --barcodes (each entry on its 
                       own line for both; ordering must match).

For example, within the Numbat Docker container you can run the preprocessing script like this:

Rscript /numbat/inst/bin/pileup_and_phase.R \
    --label {sample} \
    --samples {sample} \
    --bams /mnt/mydata/{sample}.bam \
    --barcodes /mnt/mydata/{sample}_barcodes.tsv \
    --outdir /mnt/mydata/{sample} \
    --gmap /Eagle_v2.4.1/tables/genetic_map_hg38_withX.txt.gz \
    --snpvcf /data/genome1K.phase3.SNP_AF5e2.chr1toX.hg38.vcf \
    --paneldir /data/1000G_hg38 \
    --ncores ncores

Important: If your 10x bam is multiplexed (containing cells from multiple individuals), please only provide cell barcodes for a single individual in each genotyping run.

This will produce a file named {sample}_allele_counts.tsv.gz under the specified output directory, which contains cell-level phased allele counts. It contains columns cell, snp_id, CHROM, POS, cM (genetic distance in centimorgan), REF, ALT, AD (ALT allele count), DP (total allele count), GT (phased genotype), gene. If multiple samples from the same individual were provided, there will be one allele count file for each sample. Other outputs include phased vcfs under phasing/ folder and raw pileup counts under pileup/.

  1. Prepare the expression data. Numbat takes a gene by cell integer UMI count matrix as input. You can directly use results from upstream transcriptome quantification pipelines such as 10x CellRanger.

  2. Prepare the expression reference, which is a gene by cell type matrix of normalized expression values (raw gene counts divided by total counts). For a quick start, you may use a our HCA collection (ref_hca) that ships with the package. If you have matched normal cells (ideally, of various cell type) from the same patient or dataset and would like to make your own references, you may use this utility function:

# count_mat is a gene x cell raw count matrices
# cell_annot is a dataframe with columns "cell" and "group"
ref_internal = aggregate_counts(count_mat, cell_annot)

Running Numbat

In this example (ATC2 from Gao et al), the gene expression count matrix and allele dataframe are already prepared for you.


count_mat_ATC2 = readRDS(url(''))
df_allele_ATC2 = readRDS(url(''))

# run
out = run_numbat(
    count_mat_ATC2, # gene x cell integer UMI count matrix 
    ref_hca, # reference expression profile, a gene x cell type normalized expression level matrix
    df_allele_ATC2, # allele dataframe generated by pileup_and_phase script
    genome = "hg38",
    t = 1e-5,
    ncores = 4,
    plot = TRUE,
    out_dir = './test'

Note: If you wish to use your own custom reference, please use the aggregate_counts function as per the example in preparing data. You do not need to include the reference cells in count_mat or df_allele; only provide them as lambdas_ref.

Run parameters

There are a few parameters you can consider tuning to a specific dataset.

CNV detection

  • t: the transition probability used in the HMM. A lower t is more appropriate for tumors with more complex copy number landscapes (from which you can expect more breakpoints) and is sometimes better for detecting subclonal events. A higher t is more effective for controlling false-positive rates of CNV calls.
  • gamma: Overdispersion in allele counts (default: 20). For 10x data, 20 is recommended. Non-UMI protocols (e.g. SMART-Seq) usually produce noisier allele data, and a smaller value of gamma is recommended (e.g. 5).
  • min_cells: minimum number of cells for which an pseudobulk HMM will be run. If the allele coverage per cell is very sparse for a dataset, then I would consider setting this threshold to be higher.
  • multi_allelic: Whether to enable calling of multiallelic CNVs

CNV filtering

  • min_LLR: minimum log-likelihood ratio threshold to filter CNVs (default: 5). To ensure quality of phylogeny inference, we only use confident CNVs to reconstruct the phylogeny.
  • max_entropy: another criteria that we use to filter CNVs before phylogeny construction (default: 0.5). The entropy of the binary posterior quantifies the uncertainty of an event across single cells. The value can be from 0 to 1 where 1 is the least stringent.


  • n_cut: Number of cuts on the phylogeny to define subclones. Note that n cuts should result in n+1 clones (the top-level normal diploid clone is always included). Alternatively, one can specify max_cost or tau to control the number of subclones.
  • max_cost: Likelihood threshold to collapse internal branches. Higher max_cost generally leads to fewer clones.
  • tau: Stringency to simplify the mutational history. Basically sets max_cost as tau times the number of cells.

Iterative optimization

  • init_k: initial number of subclusters to use for the hclust initialization. Numbat by default uses hierarchical clustering (hclust) of smoothed expression values to approximate an initial phylogeny. This will cut the initial tree into k clusters. More clusters means more resolution at the initial stage for subclonal CNV detection. By default, we set init_k to be 3.
  • max_iter: maximum number of iterations. Numbat iteratively optimizes the phylogeny and copy number estimations. In practice, we find that results after 2 iterations are usually stable.
  • check_convergence: stop iterating if the results have converged (based on consensus CNV segments).


  • ncores: number of cores to use for single-cell CNV testing
  • ncores_nni: number of cores to use for phylogeny inference

Other run modes

  • call_clonal_loh: whether to call clonal LOH regions. Recommended for samples with high purity without matched normal cells. See Detecting clonal LOH for details.
  • segs_consensus_fix: a dataframe of pre-determined consensus CNVs (for example, from bulk WGS/WES analysis) that will stay fixed during analysis. See Using existing CNV calls for details.

Detecting clonal LOH

In samples with high tumor purity (e.g., tumor cell lines) without matched normal cells, heterozygous SNPs are challenging to identify in regions of LoH, leading to decreased power of CNV detection. Regions of clonal LoH have decreased SNP density and can be identified by a specialized HMM (see detect_clonal_loh in function reference). You can set call_clonal_loh = TRUE to automatically identify and exclude regions of clonal deletions/LoH in the main workflow. Alternatively, you can manually run detect_clonal_loh and provide the detected segments as a dataframe (with columns CHROM, seg, seg_start, seg_end) via the segs_loh argument.

In addition, if you have matched DNA data (e.g. WGS/WES) of the same individual, you can directly use the SNP profile genotyped from DNA, which should avoid the above problem. See Using prephased SNP profiles for details.

Using existing CNV calls

Sometimes users already have CNV calls from bulk WGS, WES, or array analysis. In this case, you can supply the existing CNV profile via segs_consensus_fix parameter to fix the CNV boundaries and states. To do so, you may provide a dataframe with the following columns:

  • CHROM: integer; chromosome (1-22)
  • seg: character; segment ID (e.g. 1a, 1b, 2a, 2b, etc.)
  • seg_start: integer; segment start position
  • seg_end: integer; segment end position
  • cnv_state: character; copy number state (neu, del, amp, loh, bamp, bdel)

Please note that diploid segments (cnv_state = "neu") should also be included (i.e. segs_consensus_fix should be a complete copy number profile including all chromosomes).

Understanding results

A detailed vignette on how to interpret and visualize Numbat results is available:

For a full list of output files and column descriptions, please see:

Phasing options

The CNV detection power of Numbat can be further enhanced by conducting population-based phasing with a larger and more diverse reference panel (i.e. reference haplotypes). The default pipeline above uses the 1000 Genome panel, which contains genotypes from 2,504 individuals. Larger reference panels include:

  • gnomAD HGDP + 1KG panel (n=4,099). You can download the reference files using gsutil: gs://gcp-public-data--gnomad/resources/hgdp_1kg/phased_haplotypes.

  • TOPMed panel (n=97,256). You can upload your VCF to the TOPMed imputation server.

Using prephased SNP profiles

Using DNA-derived genotype information is another way to improve SNP density and phasing. If you have SNP calls from DNA genotyping (e.g. WGS/WES), you can first perform phasing on the DNA-derived VCF. Then run cellsnp-lite on scRNA-seq BAMs against the DNA-derived VCF to generate allele counts (only include heterozygous SNPs). Finally, merge the phased GT fields (from phased DNA-derived VCF) with the obtained allele counts to produce an allele dataframe in the format of df_allele (see section Preparing data).

Spatial data

Numbat can also be used to analyze spatial transcriptomics (e.g. 10x Visium) data. Please see the spatial tutorial for details.

Mouse data

Numbat (v1.2.0) now works for a F1 hybrid mouse bred from two known lab mice strains. More details are available in the mouse tutorial.


Q: What is the expression reference for and how does it impact analysis?

A: The expression reference (lambdas_ref) is used to regress out any cell-type-specific expression differences that are unrelated to CNVs. It is used in both single-cell testing and pseudobulk CNV calling using HMM. Ideally, expression reference should be created from a normal (non-tumor) dataset closely matching the biological and technical conditions of the target dataset (e.g., same tissue types, cell types, sequencing platforms). A closely matching reference can help reduce the noise in the expression-based CNV signal (logFC) during pseudobulk and single-cell analysis.

Q: We expect a certain CNV to be in a particular sample, but it is not detected by Numbat.

A: In general, there are three scenarios that a CNV is not called.

  1. The CNV is not called in pseudobulk HMM. You can check bulk_subtrees_*.png to see if the event was called. If it was not, I would suggest trying to find a better expression reference to reduce the noise in logFC, or changing the parameters used to configure the HMM (gamma, t, etc). Sometimes the CNV is very subclonal and was not isolated as a pseudobulk by the initial clustering. You can see if this was the case by looking at the exp_roll_clust.png and if so, try increasing k_init.

  2. CNV is called in pseudobulks but did not appear in segs_consensus_*.tsv and joint_post_*tsv. This is because the event is filtered out due to low LLR. Lowering min_LLR threshold will help.

  3. CNV is called in pseudobulks, is present in segs_consensus_*.tsv and joint_post_*tsv, but did not appear in phylogeny. This is because the event is filtered out due to high entropy (weak evidence) in single cells. Raising max_entropy threshold will help. You can check the entropy of specific events in joint_post_*tsv in the column avg_entropy.

Q: Numbat predicts most of the genome to be amplified, and there are gaps in the pseudobulk allele profile with no heterozygous SNPs.

A: This is a sign that the sample consists mostly of tumor (with very few normal cells), and clonal deletions are present. The heterozygous SNPs cannot be identified in these regions, therefore leaving gaps. You can identify these clonally deleted regions via the SNP-density HMM prior to running the main run_numbat workflow (see section Detecting Clonal LOH).

Q: There seems to be a global baseline shift in the CNV profile produced by Numbat as compared to my other CNV callers; specifically, Numbat calls gains for the segments that appear to be neutral in other analyses, and neutral for segments that appear to be losses.

A: Many existing methods (e.g. InferCNV/CopyKAT) infer copy number variations relative to the median ploidy, which can dilute signals of aberrant regions or mistake neutral regions for aberrant due to baseline shifts caused by hyperdiploidy or hypodiploidy. Instead, Numbat first tries to identify diploid regions based on allele evidence (balanced allelic frequencies), and uses these regions as baseline for CNV calling. For more information, please refer to the publications describing Numbat and FACETS.