Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data.

ABSTRACT:

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 bi

SUBMITTER: Dadaneh SZ 

PROVIDER: S-EPMC7487589 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature