A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

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...

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Published inPLoS computational biology Vol. 19; no. 11; p. e1011557
Main Authors Patruno, Lucrezia, Milite, Salvatore, Bergamin, Riccardo, Calonaci, Nicola, D'Onofrio, Alberto, Anselmi, Fabio, Antoniotti, Marco, Graudenzi, Alex, Caravagna, Giulio
Format Journal Article
LanguageEnglish
Published San Francisco Public Library of Science 02.11.2023
Public Library of Science (PLoS)
Subjects
B cells
Basal cell carcinoma
Bayesian analysis
Bayesian statistical decision theory
Cancer
Cells
Chromatin
Cloning
Copy number
Copy number variations
Gastric cancer
Gene expression
Gene mapping
Gene sequencing
Genes
Genomes
Genomic instability
Genotypes
Lymphoma
Lymphomas
Mathematical models
Methods
Molecular modelling
Phenotypes
Ploidy
Prostate cancer
Ribonucleic acid
RNA
Skin cancer
Variables
Italy
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Summary: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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ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1011557