PGSEA

PGSEA implements parametric gene set enrichment analysis in R to evaluate enrichment of predefined gene sets in high-throughput gene expression data such as RNA sequencing and microarray experiments within the Bioconductor ecosystem.

Key Features:

• Parametric Approach: Utilizes a parametric framework that fits statistical models and accounts for distributional and variance assumptions across gene expression levels.
• Integration with Bioconductor and R: Implemented in R and integrated within Bioconductor, enabling interoperability with over 934 Bioconductor packages.
• Community-driven development and testing: Developed within the Bioconductor community with formal initial review and continuous automated testing.

Scientific Applications:

• Gene set enrichment analysis (GSEA): Detects enrichment of predefined gene sets in expression datasets from RNA sequencing and microarray experiments.
• Pathway and regulatory mechanism analysis: Enhances detection of pathway-level and gene regulatory signals by modeling distributional properties of expression data.
• Disease biology and treatment response studies: Identifies pathway-level changes and enriched gene sets relevant to disease biology and specific conditions or treatments.

Methodology:

Fits statistical models to gene expression data assuming parametric distributions to evaluate enrichment of predefined gene sets under specific conditions or treatments.