Haplotype-Aware CNV Analysis from scRNA-Seq

A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at < https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) .

Numbat

Numbat is a haplotype-aware CNV caller from single-cell and spatial transcriptomics data. It integrates signals from gene expression, allelic ratio, and population-derived haplotype information to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship.

Numbat can be used to:

1. Detect allele-specific copy number variations from scRNA-seq and spatial transcriptomics
2. Differentiate tumor versus normal cells in the tumor microenvironment
3. Infer the clonal architecture and evolutionary history of profiled tumors.

Numbat does not require paired DNA or genotype data and operates solely on the donor scRNA-seq data (for example, 10x Cell Ranger output). For details of the method, please checkout our paper:

Teng Gao, Ruslan Soldatov, Hirak Sarkar, Adam Kurkiewicz, Evan Biederstedt, Po-Ru Loh, Peter Kharchenko. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nature Biotechnology (2022).

Numbat-multiome

Numbat was later extended to multi-modality (single-cell RNA and ATAC) data. Check out the vignette and paper below:

Ruitong Li, Jean-Baptiste Alberge, Tina Keshavarzian, Junko Tsuji, Johan Gustafsson, Mahshid Rahmat, Elizabeth D Lightbody, Stephanie L Deng, Santiago Riviero, Mendy Miller, F Naz Cemre Kalayci, Adrian Wiestner, Clare Sun, Mathieu Lupien, Irene Ghobrial, Erin Parry, Teng Gao, Gad Getz. Numbat-multiome: inferring copy number variations by combining RNA and chromatin accessibility information from single-cell data. Briefings in Bioinformatics (2025).

User Guide

For a complete guide, please see Numbat User Guide.

Questions?

We appreciate your feedback! Please raise a github issue for bugs, questions and new feature requests. For bug reports, please attach full log, error message, input parameters, and ideally a reproducible example (if possible).