Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes in cell populations. Clustering scRNA-seq is essential in identifying new cell types and studying their characteristics. We develop CellBIC (single Cell BImodal Clustering) to cluster scRNA-seq data based on modality in the gene expression distribution. Compared with classical bottom-up approaches that rely on a distance metric, CellBIC performs hierarchical clustering in a top-down manner. CellBIC outperformed the bottom-up hierarchical clustering approach and other recently developed clustering algorithms while maintaining the hierarchical structure of cells. Importantly, CellBIC identifies type 2 diabetes and age specific β cell signatures characterized by SIX3 and CDH2, respectively.
Original language | English |
---|---|
Article number | e124 |
Journal | Nucleic Acids Research |
Volume | 46 |
Issue number | 21 |
Pages (from-to) | 1-8 |
Number of pages | 8 |
ISSN | 0305-1048 |
DOIs | |
Publication status | Published - 30 Nov 2018 |