No abstract

Identifying expressed somatic mutations directly from single-cell RNA sequencing (scRNA-seq) data is challenging but highly valuable. Computational methods have been attempted but no reliable methods have been reported to identify somatic mutations with high fidelity. We present RESA -- Recurrently Expressed SNV Analysis, a computational framework that identifies expressed somatic mutations from scRNA-seq data with high precision. We test RESA in multiple cancer cell line datasets, where RESA demonstrates average area under the curve (AUC) of 0.9 on independently held out test sets, and achieves average precision of 0.71 when evaluated by bulk whole exome, which is substantially higher than previous approaches. In addition, RESA detects a median of 201 mutations per cell, 50 times more than what was reported in experimental technologies with simultaneous expression and mutation profiling. Furthermore, applying RESA to scRNA-seq from a melanoma patient, we demonstrate that RESA recovers the known BRAF driver mutation of the sample and melanoma dominating mutational signatures, identifies mutation associated expression signatures, reveals nondriver perturbed and stage specific cancer hallmarks, and unveils the complex relationship between genomic and transcriptomic intratumor heterogeneity. Therefore, RESA could provide novel views in the study of intratumor heterogeneity and relate genetic alterations to transcriptional changes at single cell level.