SPFA

SPFA analyzes signal variations received by effector genes across signaling pathways to identify disease-associated functional attributes.

Key Features:

• Novel methodology: Focuses on differences in signals received by effector genes between normal and diseased states across signaling pathways.
• Comparative evaluation: Compared against seven existing methods using 33 Gene Expression Omnibus (GEO) datasets.
• Performance metrics: Assesses performance using median rank of target pathways, median p-value of target pathways, and percentage of significant pathways, reporting top median-rank performance and fourth-best median p-value in the comparison.
• Research application focus: Enables pinpointing abnormal functional attributes and specific pathway components implicated in disease processes.
• Implementation: Provided as R code and functions.

Scientific Applications:

• Disease-associated pathway identification: Detects signaling pathways whose effector-gene signal patterns differ between normal and disease conditions.
• Effector-gene functional characterization: Characterizes functional attributes of effector genes that drive cellular behaviors in disease contexts.
• Therapeutic-target prioritization: Supports prioritization of pathway components for downstream therapeutic target investigation.

Methodology:

Compares signal variations received by effector genes across signaling pathways between normal and disease states and was evaluated by comparison to seven methods on 33 GEO datasets using median rank, median p-value, and percentage of significant pathways.