Parallel clustering of single cell transcriptomic data with split-merge sampling on Dirichlet process mixtures

Tiehang Duan, José P. Pinto, Xiaohui Xie

Abstract: 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: (i) the clustering quality still needs to be improved; (ii) most models need prior knowledge on number of clusters, which is not always available; (iii) there is a demand for faster computational speed.