A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

Abstract

Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression of cells before and after the condition change, we propose an algorithm, SparseDC, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes. By solving a unified optimization problem, SparseDC completes all three tasks simultaneously. SparseDC is highly computationally efficient and demonstrates its accuracy on both simulated and real data.