apeglm

apeglm applies Bayesian shrinkage to estimate effect sizes of generalized linear model (GLM) coefficients for RNA sequencing (RNA-seq) differential expression analysis using a heavy-tailed Cauchy prior.

Key Features:

• Bayesian Shrinkage Estimation: Applies Bayesian shrinkage estimators to refine effect size estimates for GLM coefficients, improving stability for low or highly variable count data.
• Heavy-Tailed Cauchy Prior Distribution: Uses a heavy-tailed Cauchy prior on effect sizes to reduce bias and control variance, supporting more reliable effect size estimates and gene rankings.
• Integration with DESeq2: Integrates with the DESeq2 R package to compute shrinkage-adjusted effect sizes within DESeq2's GLM-based differential expression framework.

Scientific Applications:

• RNA-seq differential expression: Improves detection and ranking of differentially expressed genes in RNA-seq studies for genomics and transcriptomics analyses.
• Downstream interpretation and validation: Provides more precise effect size estimates to support downstream biological interpretation and experimental validation of differential expression results.

Methodology:

Approximates posterior distributions of individual GLM coefficients via a Bayesian framework using a heavy-tailed Cauchy prior for effect sizes.