SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data

TL;DR

SimCD is a unified hierarchical gamma-negative binomial model that simultaneously performs cell clustering and differential expression analysis in single-cell RNA sequencing data, improving accuracy and biomarker discovery.

Contribution

It introduces a novel joint modeling approach that integrates clustering and differential expression analysis for scRNA-seq data, outperforming existing disjoint methods.

Findings

Better clustering and differential expression detection compared to state-of-the-art methods.

Effectively models dropout and zero inflation in scRNA-seq data.

Identifies known and novel biomarkers related to food deprivation in hypothalamic neurons.

Abstract

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.