A novel computational approach to identify cancer cells in scRNA-seq data

Abstract

Single-cell RNA-seq is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Several existing methods for identifying tumor cells use copy number variation inference. This work aims to extend existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data and that a subset of cells in tumor samples are enriched in putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that combining copy number variation inference with putative driver mutation load can augment the number of tumor cells that can be confidently included in downstream analyses of single-cell RNA-seq datasets.