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The advent of single-cell sequencing opens new avenues for personalizedtreatment. In this paper, we address a two-level clustering problem ofsimultaneous subject subgroup discovery (subject level) and cell type detection(cell level) for single-cell expression data from multiple subjects. However,current statistical approaches either cluster cells without considering thesubject heterogeneity or group subjects without using the single-cellinformation. To bridge the gap between cell clustering and subject grouping, wedevelop a nonparametric Bayesian model, Subject and Cell clustering forSingle-Cell expression data (SCSC) model, to achieve subject and cell groupingsimultaneously. SCSC does not need to prespecify the subject subgroup number orthe cell type number. It automatically induces subject subgroup structures andmatches cell types across subjects. Moreover, it directly models thesingle-cell raw count data by deliberately considering the data's dropouts,library sizes, and over-dispersion. A blocked Gibbs sampler is proposed for theposterior inference. Simulation studies and the application to a multi-subjectiPSC scRNA-seq dataset validate the ability of SCSC to simultaneously clustersubjects and cells.

Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data