Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses.

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 and we showed in simulated scenario, the proposed BFH would be an optimal method when compared with other popular single cell differential expression methods if both FDR and power were conside

SUBMITTER: Han G 

PROVIDER: S-EPMC11575015 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature