Journal
GENOME MEDICINE
Volume 15, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13073-023-01269-1
Keywords
Somatic mutations; Single-cell RNA sequencing data; Recurrently Expressed SNV Analysis; High precision
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This study proposes a computational framework called RESA for identifying expressed somatic mutations from single-cell RNA sequencing data. Through testing on simulated and real datasets, the method demonstrates high accuracy and performance, which is of great significance for mutational analysis.
Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA - Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA.
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