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De novo detection of somatic mutations in high-throughput single-cell profiling data sets
Nature Biotechnology ( IF 33.1 ) Pub Date : 2023-07-06 , DOI: 10.1038/s41587-023-01863-z
Francesc Muyas 1 , Carolin M Sauer 1 , Jose Espejo Valle-Inclán 1 , Ruoyan Li 2 , Raheleh Rahbari 2 , Thomas J Mitchell 2, 3, 4 , Sahand Hormoz 5, 6, 7 , Isidro Cortés-Ciriano 1
Affiliation  

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.



中文翻译:


高通量单细胞分析数据集中体细胞突变的从头检测



单细胞分辨率的体细胞突变表征对于研究癌症进化、克隆嵌合体和细胞可塑性至关重要。在这里,我们描述了 SComatic,一种设计用于直接检测单细胞转录组和 ATAC-seq(转座酶可访问染色质序列的分析)数据集中的体细胞突变的算法,无需匹配的批量或单细胞 DNA 测序数据。 SComatic 使用过滤器和非肿瘤样本参数化的统计测试将体细胞突变与多态性、RNA 编辑事件和假象区分开来。使用来自涵盖癌症和非肿瘤样本的 688 个单细胞 RNA-seq (scRNA-seq) 和单细胞 ATAC-seq (scATAC-seq) 数据集中的超过 260 万个单细胞,我们表明 SComatic 可以检测单细胞中的突变准确地,即使是来自多克隆组织的分化细胞,这些细胞不适合使用现有方法进行突变检测。根据匹配的基因组测序和 scRNA-seq 数据进行验证,SComatic 在不同数据集上的 F1 分数在 0.6 到 0.7 之间,而表现第二好的方法的 F1 分数为 0.2–0.4。总之,SComatic 允许进行从头突变特征分析,以及在单细胞分辨率下研究克隆异质性和突变负担。

更新日期:2023-07-06
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