RBM

RBM applies a resampling-based empirical Bayes approach to assess differential expression in two-color microarray and RNA-Seq datasets.

Key Features:

• Empirical Bayes Framework: Uses an empirical Bayes framework with resampling to improve estimation accuracy and robustness in differential expression analysis.
• Resampling Techniques: Incorporates resampling-based procedures to enhance statistical power and reliability when detecting differentially expressed genes.
• Data Compatibility: Supports analysis of two-color microarray and RNA-Seq high-throughput genomic data.
• Bioconductor and R Integration: Implemented for use within the Bioconductor project and the R statistical programming environment.
• Interoperability: Designed to interoperate with other Bioconductor packages.

Scientific Applications:

• Differential Gene Expression Analysis: Identifies genes with significant expression changes across experimental conditions in microarray and RNA-Seq data.
• Cancer Biology: Detects expression changes relevant to cancer-related studies and comparisons.
• Developmental Biology: Assesses gene expression differences across developmental stages or conditions.
• Systems Biology: Provides differential expression results that can inform analyses of biological networks and pathways.

Methodology:

Performs resampling-based empirical Bayes estimation that leverages prior information through Bayesian statistics to infer differential expression.