Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process

Abstract

Single-cell RNA sequencing (scRNA-seq) is a powerful technology that allows researchers to understand gene expression patterns at the single-cell level and uncover the heterogeneous nature of cells. Clustering is an important tool in scRNA-seq analysis to discover groups of cells with similar gene expression patterns and identify potential cell types. Integration of multiple scRNA-seq datasets is a pressing challenge, and in this direction, a novel model is developed to extend clustering methods to appropriately combine inference across multiple datasets. The model simultaneously addresses normalization to deal with the inherent noise and uncertainty in scRNA-seq, infers cell types, and integrates multiple datasets for shared clustering in principled manner through a hierarchical Bayesian framework. A Gibbs sampler is developed that copes with the high-dimensionality of scRNA-seq through consensus clustering. The methodological developments are driven by experimental data from embryonic cells, with the aim of understanding the role of PAX6 in prenatal development, and more specifically how cell-subtypes and their proportions change when knocking out this factor.