  Background: Single-cell RNA sequencing (scRNA-seq) is a powerful profiling
technique at the single-cell resolution. Appropriate analysis of scRNA-seq data
can characterize molecular heterogeneity and shed light into the underlying
cellular process to better understand development and disease mechanisms. The
unique analytic challenge is to appropriately model highly over-dispersed
scRNA-seq count data with prevalent dropouts (zero counts), making
zero-inflated dimensionality reduction techniques popular for scRNA-seq data
analyses. Employing zero-inflated distributions, however, may place extra
emphasis on zero counts, leading to potential bias when identifying the latent
structure of the data. Results: In this paper, we propose a fully generative
hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating
the need for explicitly modeling zero inflation. At the same time, hGNB can
naturally account for covariate effects at both the gene and cell levels to
identify complex latent representations of scRNA-seq data, without the need for
commonly adopted pre-processing steps such as normalization. Efficient Bayesian
model inference is derived by exploiting conditional conjugacy via novel data
augmentation techniques. Conclusion: Experimental results on both simulated
data and several real-world scRNA-seq datasets suggest that hGNB is a powerful
tool for cell cluster discovery as well as cell lineage inference.
