Estimating a phylogenetic forest for single-cell RNA-sequencing data

Music City Center 

Single-cell RNA-sequencing (scRNA-seq) technologies provide researchers with unprecedented opportunities to identify cell types and understand cell lineages. With the emergence of scRNA-seq studies that assay a large number of subjects, there is growing interest in aligning and comparing cell lineages between different individuals, especially for those with different clinical conditions. However, comparing cell lineages learned from scRNA-seq data collected from multiple individuals is challenging due to (a) scRNA-seq data can suffer from severe batch effects and (b) certain cell types may occur in some but not all individuals. In this study, we propose a Bayesian hierarchical model built upon Dirichlet diffusion tree to learn a phylogenetic forest for scRNA-seq data collected from multiple individuals. Our proposed model can automatically align the topologies of the phylogenetic trees of different individuals. We develop an efficient Markov chain Monte Carlo algorithm for posterior inference. Simulation studies and real data analysis demonstrate that our proposed model outperforms the state-of-the-art methods.

single-cell RNA-sequencing data

phylogenetic tree

Dirichlet diffusion tree

Bayesian hierarchical model 

Section on Statistics in Genomics and Genetics