Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders

Yuge Wang, Hongyu Zhao

Abstract: Author summary Single-cell RNA sequencing (scRNA-seq) techniques enable the profiling of gene expression at the single-cell level, and thus make it possible to uncover the cellular heterogeneity in a complex cell population which is composed of multiple cell types. Due to the complexity of biological system, different cell types may share underlying gene expression programs (GEPs) at different levels. However, such shared patterns are difficult to study by traditional cluster analysis. Based on the assumption that the expression profile of each cell results from a non-linear combination of multiple GEPs, we develop scAAnet, a deep learning model for non-linear archetypal decomposition of scRNA-seq data. We demonstrate that scAAnet is able to both achieve better decomposition performance in simulated data and identify biologically meaningful GEPs that are either cell-type-specific or disease-enriched in three real scRNA-seq datasets. To help interpret results from scAAnet, we also provide downstream analysis tools for the identification of program-specific marker genes. We expect scAAnet can be applied to explore GEPs shared across cells when scRNA-seq is used to study a complex disease or biological system.