  Motivation: Single cell transcriptome sequencing (scRNA-Seq) has become a
revolutionary tool to study cellular and molecular processes at single cell
resolution. Among existing technologies, the recently developed droplet-based
platform enables efficient parallel processing of thousands of single cells
with direct counting of transcript copies using Unique Molecular Identifier
(UMI). Despite the technology advances, statistical methods and computational
tools are still lacking for analyzing droplet-based scRNA-Seq data.
Particularly, model-based approaches for clustering large-scale single cell
transcriptomic data are still under-explored. Methods: We developed DIMM-SC, a
Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic
data. This approach explicitly models UMI count data from scRNA-Seq experiments
and characterizes variations across different cell clusters via a Dirichlet
mixture prior. An expectation-maximization algorithm is used for parameter
inference. Results: We performed comprehensive simulations to evaluate DIMM-SC
and compared it with existing clustering methods such as K-means, CellTree and
Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster
labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with
prior biological knowledge to benchmark and validate DIMM-SC. Both simulation
studies and real data applications demonstrated that overall, DIMM-SC achieves
substantially improved clustering accuracy and much lower clustering
variability compared to other existing clustering methods. More importantly, as
a model-based approach, DIMM-SC is able to quantify the clustering uncertainty
for each single cell, facilitating rigorous statistical inference and
biological interpretations, which are typically unavailable from existing
clustering methods.
