Mining alternative splicing patterns in scRNA-seq data using scASfind

Abstract

Single-cell RNA-seq is widely used for transcriptome profiling, but most analyses have focused on gene-level events, with much less attention devoted to alternative splicing. Here, we present scASfind, a novel computational method to allow for quantitative analysis of cell type-specific splicing events. scASfind utilizes an efficient data structure to store the percent spliced-in value for each splicing event. This makes it possible to exhaustively search for patterns among all differential splicing events, allowing us to identify marker events, mutually exclusive events, and large blocks of exons that are specific to one or more cell types. These methods allow researchers to compare cells based on isoforms rather than genes, thereby enabling more nuanced characterization of cell types and states. We demonstrate the advantages of scASfind on two mouse and one human datasets, identifying differences across the several key genes that cannot be detected using gene expression alone.