Semisoft Clustering of Single-cell Data

Motivated by the dynamics of development, in which cells of recognizable types, or pure cell types, transition into other types over time, we propose a method of semisoft clustering that can classify both pure and intermediate cell types from data on gene expression from individual cells. Called semisoft clustering with pure cells (SOUP), this algorithm reveals the clustering structure for both pure cells and transitional cells with soft memberships. SOUP involves a two-step process: Identify the set of pure cells and then estimate a membership matrix. To find pure cells, SOUP uses the special block structure in the expression similarity matrix. Once pure cells are identified, they provide the key information from which the membership matrix can be computed. By modeling cells as a continuous mixture of K discrete types we obtain more parsimonious results than obtained with standard clustering algorithms. Moreover, using soft membership estimates of cell type cluster centers leads to better estimates of developmental trajectories. The strong performance of SOUP is documented via simulation studies, which show its robustness to violations of modeling assumptions. The advantages of SOUP are illustrated by analyses of two independent datasets of gene expression from a large number of cells from fetal brain.