Differential Inference for Single-Cell RNA-Seq Data

• Activity Number: 428 - Clustering and Dimension-Reduction Methods: From Omics to Single-Cell Data
• Type: Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Fangda Song* and Yingying Wei
• Companies: The Chinese University of Hong Kong, Shenzhen and The Chinese University of Hong Kong
• Keywords: scRNA-seq; Bayesian hierarchical model; Differential Expression; Treatment Effects

Abstract

With the wide-adoption of single-cell RNA-seq (scRNA-seq) technologies, designs of scRNA-seq experiments are becoming more complicated with multiple treatments or biological conditions. However, despite the active research on batch effects correction, cell type clustering, and missing data imputation for scRNA-seq data, rigorous statistical methods to compare scRNA-seq experiments under different conditions are still lacking. Here, we propose a Bayesian hierarchical model to rigorously quantify the treatment effects on both cellular compositions and cell-type-specific gene expression levels for scRNA-seq data. We implement a highly scalable algorithm to handle the large number of cells. Application of our proposed method to a pancreatic study demonstrates that considering the biological conditions of samples in the analysis further boosts the clustering accuracy as compared to traditional analysis pipelines for scRNA-seq data and identifies cell-type-specific and condition-specific differentially expressed genes.