AEnet: a practical tool to construct the splicing-associated phenotype atlas at a single cell level.

Abstract

Alternative splicing (AS), a crucial driver of proteomic diversity, is a fundamental source of cellular heterogeneity alongside gene expression levels. AS is closely linked to various physiological and pathological processes, including tumor progression and embryonic development. Single-cell RNA sequencing (scRNA-seq) technologies capture AS events through junction reads at cellular resolution, enabling the identification of core AS events that regulate specific cell types or states. However, single-cell sequencing technologies and their data are plagued by inherent limitations, such as shallow sequencing depth, high dropout rates, and batch effects. Furthermore, previous clustering approaches have overlooked the crucial interplay between AS and gene expression in defining distinct "cell types," posing ongoing challenges in this field. In this study, we present a novel method called Alternative Splicing-Gene Expression Network (AEnet), which combines gene expression levels with AS patterns to profile cellular heterogeneity and define what we term "cell subpopulations." AEnet also identifies key AS events and infers the regulatory mechanisms underlying these events. By applying AEnet to tumor cells, pan-cancer immune cells, and embryonic cells, we demonstrate enhanced cell clustering, the identification of novel AS events with potential functional importance, and the discovery of the key splicing factors involved in cell state transitions. The application of AEnet provides new insights into cellular heterogeneity and its role in both physiological and pathological processes.