Data-driven identification of cell subtypes for single-cell transcriptomic data with Subtypist

Abstract

Abstract Identification of new cell subtypes in single-cell RNA sequencing (scRNA-seq) data is critically crucial in accurately understanding the mechanisms of disease occurrence and development, which provides unprecedented insights into the development of therapeutic strategies, however, despite the abundance of existing methods, most heavily rely on the reference with the fixed cell labels, which fail to uncover new cell subtypes. Herein, we propose Subtypist, a fully data-driven multiscale iterative algorithm for cell subtype identification in scRNA-seq data without relying on external prior references. The benchmarking evaluation on both simulated and real datasets demonstrates its superior performance over other existing methods, and Subtypist was further applied to three disease scenarios, uncovering novel biologically meaningful cell subtypes and revealing their in-depth cell–cell communicative mechanisms underlying hepatocellular carcinoma, esophageal squamous cell carcinoma, and myocardial infarction. In summary, Subtypist enables the data-driven and reproducible identification of cell subtypes, providing an invaluable tool for accurately characterizing the cellular and molecular heterogeneity underlying disease pathogenesis.