SPIA

SPIA integrates differential expression data, including log fold changes, with signaling pathway topology and combines enrichment and perturbation evidence to identify signaling pathways impacted in gene expression studies as an R package.

Key Features:

• Integration of Enrichment and Perturbation Evidence: Combines enrichment analysis with perturbation analysis to consider both the number of differentially expressed (DE) genes in pathways and their functional impact.
• Pathway Topology Consideration: Incorporates signaling pathway topology to account for interactions and gene relationships within pathways rather than treating pathways as simple gene sets.
• Bootstrap Significance Assessment: Employs a bootstrap procedure to generate empirical P-values for observed pathway perturbations.
• Global Pathway Significance Calculation: Integrates enrichment and perturbation evidence into a combined global pathway significance P-value.
• Improved Sensitivity and Specificity: Demonstrates increased specificity and sensitivity relative to several other pathway analysis methods based on simulations and real dataset analyses.

Scientific Applications:

• Gene expression class comparison studies: Identifies signaling pathways impacted by differentially expressed genes between sample groups.
• Pathway-level interpretation of disease mechanisms: Aids in elucidating underlying mechanisms of conditions and diseases by assessing pathway perturbations.
• Cross-dataset pathway analysis: Applies to diverse gene expression datasets in genomics and bioinformatics for comparative pathway analysis.

Methodology:

Collecting differentially expressed genes and their log fold changes; analyzing pathway topology to assess gene interactions within pathways; conducting enrichment analysis to detect DE gene presence in pathways; measuring perturbations to evaluate functional impact on pathways; and using a bootstrap procedure to compute empirical significance and a combined global pathway significance P-value.