Numbat-multiome: inferring copy number variations by combining RNA and chromatin accessibility information from single-cell data

Aberrant alterations in genome copy number, chromatin accessibility, and transcriptional programs all play pivotal roles in cancer. The Numbat algorithm has been widely adopted to perform copy number variation (CNV) inference from single-cell RNA sequencing (scRNA-seq) data. Here, we introduce Numbat-multiome that extends the capabilities of Numbat to perform CNV inference from both scRNA-seq and accessible chromatin profiles (through single-cell Assay of Transposase [Tn5]-Accessible Chromatin sequencing [scATAC-seq] data), either separately or in an integrated manner. Our approach unifies data originating from different modalities through a binning strategy that relies on a common genomic coordinate system across modalities. We demonstrate the tool's robust performance in four running modes (RNA gene, RNA bin, ATAC bin, and Combined bin) using benchmark cohorts of tumors with dynamic changes in expression patterns and copy number heterogeneity, including early-stage multiple myeloma and Richter's syndrome arising from chronic lymphocytic leukemia, validated against whole-genome sequencing. Numbat-multiome achieves high precision and recall (median F1>0.9) across different CNV event types, with consistent performance across sample types and event lengths. The tool's ability to track clonal evolution in serial samples and identify rare subclones allows for integration of epigenomic profiles at the subclonal level, providing new insights into the stepwise genetic and epigenetic changes underlying cancer phenotypic shifts.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-08-31
DOI
https://doi.org/10.1093/bib/bbaf516
Citations
2
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
1.61

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article

Numbat-multiome: inferring copy number variations by combining RNA and chromatin accessibility information from single-cell data

Gad A. Getz, Mathieu Lupien, Ruitong Li, Jean-Baptiste Alberge et al.
2 citations
Briefings in Bioinformatics
Single-cell and spatial transcriptomics
1.61
article

Numbat-multiome: inferring copy number variations by combining RNA and chromatin accessibility information from single-cell data

Gad A. Getz, Mathieu Lupien, Ruitong Li, Jean-Baptiste Alberge, Elizabeth D. Lightbody, Irene M. Ghobrial, Adrian Wiestner, Stephanie Deng, Junko Tsuji, Tina Keshavarzian, Erin Michelle Parry, Teng Gao, Mahshid Rahmat, Clare Sun, Johan E. Gustafsson, Mendy Miller, F Naz Cemre Kalayci, Santiago Riviero
article en
2 citations

Abstract

Aberrant alterations in genome copy number, chromatin accessibility, and transcriptional programs all play pivotal roles in cancer. The Numbat algorithm has been widely adopted to perform copy number variation (CNV) inference from single-cell RNA sequencing (scRNA-seq) data. Here, we introduce Numbat-multiome that extends the capabilities of Numbat to perform CNV inference from both scRNA-seq and accessible chromatin profiles (through single-cell Assay of Transposase [Tn5]-Accessible Chromatin sequencing [scATAC-seq] data), either separately or in an integrated manner. Our approach unifies data originating from different modalities through a binning strategy that relies on a common genomic coordinate system across modalities. We demonstrate the tool's robust performance in four running modes (RNA gene, RNA bin, ATAC bin, and Combined bin) using benchmark cohorts of tumors with dynamic changes in expression patterns and copy number heterogeneity, including early-stage multiple myeloma and Richter's syndrome arising from chronic lymphocytic leukemia, validated against whole-genome sequencing. Numbat-multiome achieves high precision and recall (median F1>0.9) across different CNV event types, with consistent performance across sample types and event lengths. The tool's ability to track clonal evolution in serial samples and identify rare subclones allows for integration of epigenomic profiles at the subclonal level, providing new insights into the stepwise genetic and epigenetic changes underlying cancer phenotypic shifts.

Briefings in BioinformaticsVol. 26(5)
Broad Institute (US), Ontario Institute for Cancer Research (CA), Boston Children's Hospital (US), Howard Hughes Medical Institute (US), Harvard University (US), University of Toronto (CA), Lemuel Shattuck Hospital (US), Princess Margaret Cancer Centre (CA), National Heart, Lung, and Blood Institute (US), Dana-Farber Cancer Institute (US), Center for Cancer Research (US)
Massachusetts General Hospital, CLL Global Research Foundation, Government of Ontario, National Institutes of Health, Canadian Institutes of Health Research, National Cancer Institute
Openalex Percentile: Top 14%
Single-cell and spatial transcriptomics
1.61
DOIOpen Access PDFOpenAlex
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