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Motivation: Single cell transcriptome sequencing (scRNA-Seq) has become arevolutionary tool to study cellular and molecular processes at single cellresolution. Among existing technologies, the recently developed droplet-basedplatform enables efficient parallel processing of thousands of single cellswith direct counting of transcript copies using Unique Molecular Identifier(UMI). Despite the technology advances, statistical methods and computationaltools are still lacking for analyzing droplet-based scRNA-Seq data.Particularly, model-based approaches for clustering large-scale single celltranscriptomic data are still under-explored. Methods: We developed DIMM-SC, aDirichlet Mixture Model for clustering droplet-based Single Cell transcriptomicdata. This approach explicitly models UMI count data from scRNA-Seq experimentsand characterizes variations across different cell clusters via a Dirichletmixture prior. An expectation-maximization algorithm is used for parameterinference. Results: We performed comprehensive simulations to evaluate DIMM-SCand compared it with existing clustering methods such as K-means, CellTree andSeurat. In addition, we analyzed public scRNA-Seq datasets with known clusterlabels and in-house scRNA-Seq datasets from a study of systemic sclerosis withprior biological knowledge to benchmark and validate DIMM-SC. Both simulationstudies and real data applications demonstrated that overall, DIMM-SC achievessubstantially improved clustering accuracy and much lower clusteringvariability compared to other existing clustering methods. More importantly, asa model-based approach, DIMM-SC is able to quantify the clustering uncertaintyfor each single cell, facilitating rigorous statistical inference andbiological interpretations, which are typically unavailable from existingclustering methods.

DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data