Detection of differentially abundant cell subpopulations discriminates biological states in scRNA-seq data

Abstract

Traditional cell clustering analysis used to compare the transcriptomic landscapes between two biological states in single cell RNA sequencing (scRNA-seq) is largely inadequate to functionally identify distinct and important differentially abundant (DA) subpopulations between groups. This problem is exacerbated further when using unsupervised clustering approaches where differences are not observed in clear cluster structure and therefore many important differences between two biological states go entirely unseen. Here, we develop DA-seq, a powerful unbiased, multi-scale algorithm that uniquely detects and decodes novel DA subpopulations not restricted to well separated clusters or known cell types. We apply DA-seq to several publicly available scRNA-seq datasets on various biological systems to detect differences between distinct phenotype in COVID-19 cases, melanomas subjected to immune checkpoint therapy, embryonic development and aging brain, as well as simulated data. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies, but also reveals new DA subpopulations that were not described before. Analysis of these novel subpopulations yields new biological insights that would otherwise be neglected.