GSReg

GSReg infers pathway activities by analyzing differential variability and rank-order conservation in gene set expression to detect dysregulated pathways in contexts such as cancer.

Key Features:

• DIRAC: Assesses conservation of rank order in gene expression across conditions to identify differentially regulated pathways.
• EVA: Evaluates differential variability of multigene expression patterns using a computationally efficient algorithm to detect dysregulated pathways that may be missed by over-representation and enrichment analyses.

Scientific Applications:

• Pathway inference in cancer research: Identifies signaling pathways implicated in cancer initiation and progression by analyzing gene-set level variability rather than individual-gene statistics.
• Comparison with traditional methods: Reveals multivariate expression patterns and differential variability that over-representation and enrichment analyses can fail to detect.

Methodology:

GSReg implements two explicit methods: DIRAC, which examines rank-order conservation in gene expression to assess pathway regulation, and EVA, which quantifies differential variability of gene sets with computational efficiency and complements DIRAC by detecting pathways that enrichment analyses might miss.