Supporting data for "Unsupervised multi-scale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes"

Cell clustering is an essential step in uncovering cellular architectures in single cell RNA-sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. Here, we develop a multi-scale clustering (MSC) approach to construct sparse cell-cell correlation network for unsupervised identification of <i>de novo</i> cell types and subtypes across multiple resolutions.<br>Based upon simulated, silver and gold standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods, and reveals biologically meaningful cell hierarchy to facilitate the discovery of novel disease associated cell subtypes and mechanisms.