BADER

BADER analyzes RNA sequencing count data by fitting a Bayesian hierarchical model to compute posterior probabilities of differential expression for each gene between two groups labeled A and B.

Key Features:

• Bayesian Hierarchical Modeling: Models RNA sequencing count data with a Bayesian hierarchical approach to capture variability at multiple levels of the data structure.
• Posterior Probability Calculation: Calculates the posterior probability of differential expression for each gene between groups A and B.
• Posterior Samples Generation: Returns joint posterior distributions as posterior samples for genes, enabling uncertainty quantification.
• Gene Set Enrichment Analysis: Produces posterior samples that can be used to perform gene set enrichment analysis to identify affected pathways or processes.

Scientific Applications:

• Differential Gene Expression Analysis: Provides probabilistic inference of differential expression in high-throughput RNA sequencing experiments.
• Pathway and Gene Set Analysis: Supports pathway-level interpretation by supplying posterior samples for gene set enrichment procedures.
• Integration with Bioconductor and R: Interfaces with the Bioconductor project and the R programming language for analysis workflows.

Methodology:

Fits a Bayesian hierarchical model to RNA sequencing count data, computes posterior probabilities of differential expression between groups A and B, and returns joint posterior distributions as posterior samples.