Bayesian-frequentist Hybrid Inference Framework for Single Cell RNA-seq Analyses

Overview

Journal Res Sq

Date 2023 Oct 27

PMID 37886581

Authors

Gang Han

Dongyan Yan

Zhe Sun

Jiyuan Fang

Xinyue Chang

Lucas Wilson

Yushi Liu

Affiliations

Soon will be listed here.

Abstract

Background: Single cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive.

Results: Motivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power.

Conclusion: In our idiopathic pulmonary fibrosis (IPF) case study, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses.

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