  Single-Cell RNA sequencing (scRNA-seq) measurements have facilitated
genome-scale transcriptomic profiling of individual cells, with the hope of
deconvolving cellular dynamic changes in corresponding cell sub-populations to
better understand molecular mechanisms of different development processes.
Several scRNA-seq analysis methods have been proposed to first identify cell
sub-populations by clustering and then separately perform differential
expression analysis to understand gene expression changes. Their corresponding
statistical models and inference algorithms are often designed disjointly. We
develop a new method -- SimCD -- that explicitly models cell heterogeneity and
dynamic differential changes in one unified hierarchical gamma-negative
binomial (hGNB) model, allowing simultaneous cell clustering and differential
expression analysis for scRNA-seq data. Our method naturally defines cell
heterogeneity by dynamic expression changes, which is expected to help achieve
better performances on the two tasks compared to the existing methods that
perform them separately. In addition, SimCD better models dropout (zero
inflation) in scRNA-seq data by both cell- and gene-level factors and obviates
the need for sophisticated pre-processing steps such as normalization, thanks
to the direct modeling of scRNA-seq count data by the rigorous hGNB model with
an efficient Gibbs sampling inference algorithm. Extensive comparisons with the
state-of-the-art methods on both simulated and real-world scRNA-seq count data
demonstrate the capability of SimCD to discover cell clusters and capture
dynamic expression changes. Furthermore, SimCD helps identify several known
genes affected by food deprivation in hypothalamic neuron cell subtypes as well
as some new potential markers, suggesting the capability of SimCD for
bio-marker discovery.
