GSAA

GSAA performs genome-wide gene set association analysis on RNA-Seq sequence count data to identify statistically significant pathway-level differences between biological states.

Key Features:

• Integration with RNA-Seq data: Handles sequence count data from RNA-Seq experiments for genome-wide gene expression analysis.
• Gene set association analysis: Tests predefined gene sets (functionally related, co-regulated, or physically linked) to detect differences in gene expression and genotypes between two biological states.
• Diverse statistical procedures: Implements multiple statistical methods at both gene-level and gene-set-level tailored to different sample sizes and experimental conditions.
• Robust pathway profiling: Integrates results across multiple statistical procedures to derive robust profiles of significantly altered biological pathways.
• Simulation-based validation: Uses simulations to validate detection of association signals and to quantify their strength.
• Cross-tissue analysis: Applied to RNA-Seq data from diverse tissue samples to generate biological insights distinguishing sample groups.

Scientific Applications:

• Pathway differential activity: Detects differential activity within biological pathways using RNA-Seq count data.
• Genetic basis of complex traits and diseases: Integrates gene expression and genomic information to support investigations of the genetic basis of complex traits and diseases.
• Association discovery: Identifies novel associations between gene sets and phenotypic variations.
• Translational research: Supports analyses relevant to personalized medicine and therapeutic target identification.

Methodology:

Applies multiple statistical procedures tailored for RNA-Seq count data at gene and gene-set levels, integrates results across methods, and employs simulation-based validation to identify and quantify association signals.