학술논문
De novo detection of somatic mutations in high-throughput single-cell profiling data sets.
Document Type
Academic Journal
Author
Muyas F; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.; Sauer CM; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.; Valle-Inclán JE; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.; Li R; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.; Rahbari R; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.; Mitchell TJ; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.; Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.; Department of Surgery, University of Cambridge, Cambridge, UK.; Hormoz S; Department of Systems Biology, Harvard Medical School, Boston, MA, USA.; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Cortés-Ciriano I; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK. icortes@ebi.ac.uk.
Source
Publisher: Nature America Publishing Country of Publication: United States NLM ID: 9604648 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-1696 (Electronic) Linking ISSN: 10870156 NLM ISO Abbreviation: Nat Biotechnol Subsets: MEDLINE
Subject
Language
English
Abstract
Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.
(© 2023. The Author(s).)
(© 2023. The Author(s).)