  Motivation: With the development of droplet based systems, massive single
cell transcriptome data has become available, which enables analysis of
cellular and molecular processes at single cell resolution and is instrumental
to understanding many biological processes. While state-of-the-art clustering
methods have been applied to the data, they face challenges in the following
aspects: (1) the clustering quality still needs to be improved; (2) most models
need prior knowledge on number of clusters, which is not always available; (3)
there is a demand for faster computational speed. Results: We propose to tackle
these challenges with Parallel Split Merge Sampling on Dirichlet Process
Mixture Model (the Para-DPMM model). Unlike classic DPMM methods that perform
sampling on each single data point, the split merge mechanism samples on the
cluster level, which significantly improves convergence and optimality of the
result. The model is highly parallelized and can utilize the computing power of
high performance computing (HPC) clusters, enabling massive clustering on huge
datasets. Experiment results show the model outperforms current widely used
models in both clustering quality and computational speed. Availability: Source
code is publicly available on
https://github.com/tiehangd/Para_DPMM/tree/master/Para_DPMM_package
