Parallelized Inference for Single Cell Transcriptomic Clustering with Split Merge Sampling on DPMM Model

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 Para llelized Split Merge Sampling on D irichlet P rocess M ixture M odel (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 inference on huge datasets. Experiment results show the model achieves about 7% improvement in clustering accuracy for small datasets and more than 20% improvement for large challenging datasets compared with current widely used models. In the mean time, the model’s computing speed is significantly faster. Availability: Source code is publicly available on https://github.com/tiehangd/Para_DPMM/tree/master/Para_DPMM_package