ArticleNature biotechnology2024
De novo detection of somatic mutations in high-throughput single-cell profiling data sets.
Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- Somatic mosaicism in the brain: linking development, ageing and neurodegeneration.Nature reviews. Neurology · 2026Review
- Focal Cortical Dysplasia Type II: Somatic Mutations, Molecular Mechanisms, and Integrative Multi-Omics Framework.CNS neuroscience & therapeutics · 2026Review
- Clonotypic characterization defines B-cell drivers of clonal expansion and intratumor heterogeneity in IgM monoclonal gammopathies.HemaSphere · 2026Article
- TIM3-targeted delivery of venetoclax overcomes drug resistance and reinvigorates NK cell activity in acute myeloid leukemia.Nature cancer · 2026Article
- Reconstruction of Cell Diversity and Cell Lineages from Somatic Mutations in Single-Cell Transcriptomic Data.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026Article
- Single-cell analysis of the progeria arterial wall reveals progerin-induced progressive, cell type-specific dysfunction and somatic mutation accumulation.Genome medicine · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Late-stage dedifferentiation and epigenetic memory of cancer stem cells in hepatocellular carcinoma.Journal of translational medicine · 2026Article
- Genome integrity, somatic mutation, and the N-of-1 imperative in aging research.Biogerontology · 2026Review
- A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.Cell reports methods · 2026Article
- Single-cell profiling reveals distinct populations of tumor-associated macrophages and metastatic tumor cells in breast cancer brain metastasis.Cell death & disease · 2026Article
- sc-rDSeq: a robust and cost-effective full-length total RNA sequencing method for single cells reveals multilayered heterogeneity in drug-resistant lung cancer cells.Nucleic acids research · 2026Article
- Disease insights from brain somatic mosaicism.Experimental & molecular medicine · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Clonal Hematopoiesis Instructs Maladaptive Tissue Repair to Promote Fibrosis.bioRxiv : the preprint server for biology · 2026Article
- Single-cell multi-omic analysis of mitochondrial mutational mosaicism and dynamics.Nature communications · 2026Article
- Morphogen Gradients as Drivers of Mosaicism During Early Human Development.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- Analysis of microsatellite instability intensity in single-cell resolution with scMnT reveals tumor heterogeneity in colorectal cancer.Briefings in bioinformatics · 2026Article
- Single-cell atlas of human lung aging identifies cell type dyssynchrony and increased transcriptional entropy.Nature communications · 2026Article
- deMEM: a novel divide-and-conquer framework based on de Bruijn graph for scalable multiple sequence alignment.GigaScience · 2026Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
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.
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