GSEABenchmarkeR

GSEABenchmarkeR provides a reproducible and extensible framework for benchmarking gene set enrichment and network-based enrichment methods using large-scale microarray and RNA-seq expression compendia.

Key Features:

• R/Bioconductor package: Implemented as an R/Bioconductor package for use within Bioconductor workflows.
• Benchmark scope: Benchmarks gene set enrichment and network-based enrichment methodologies.
• Input data types: Operates on large-scale expression compendia derived from microarray and RNA-seq studies, including curated phenotype-rich expression collections.
• Scalability and parallel execution: Supports efficient parallel execution on local workstations, high-performance computing environments, and institutional compute grids to scale to hundreds or thousands of datasets.
• Evaluation metrics: Quantitatively assesses computational runtime, detection of statistically significant gene sets, and biological relevance of enriched pathways with respect to phenotypes.
• Extensibility: Enables incorporation of new gene set enrichment or network-based methods and definition of custom evaluation metrics.
• Standardized inputs and outputs: Provides uniform input handling, standardized execution pipelines, and structured result objects.

Scientific Applications:

• Method comparison: Compare competing gene set and network-based enrichment algorithms in a controlled and standardized manner.
• Performance evaluation: Quantitatively evaluate algorithm performance across hundreds or thousands of microarray and RNA-seq datasets.
• Phenotype association: Assess the biological relevance of enriched pathways relative to phenotypes using curated phenotype-rich collections.
• Method development: Benchmark domain-specific algorithms and guide methodological improvements through standardized evaluation.

Methodology:

Systematic evaluation of enrichment methods using large-scale expression compendia from microarray and RNA-seq studies; efficient parallel execution on local, high-performance, and grid computing environments; standardized execution pipelines with uniform input handling and structured result objects; computation of metrics including runtime, detection of statistically significant gene sets, and biological relevance of enriched pathways.