ArticlePLoS computational biology2023
A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Gene mutant dosage is associated with prognosis and metastatic tropism in 60,000 clinical cancer samples.Nature genetics · 2026Article
- CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancer genomes.bioRxiv : the preprint server for biology · 2026Article
- Stage-specific evolutionary dynamics in pediatric leukemia inferred using Bayesian stochastic modeling.PloS one · 2026Article
- Benchmarking scRNA-seq copy number variation callers.Nature communications · 2025Article
- Numbat-multiome: inferring copy number variations by combining RNA and chromatin accessibility information from single-cell data.Briefings in bioinformatics · 2025Article
- Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics.Nature methods · 2024Article
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Authors and funding
9 authors.
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Abstract
Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.
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