progeny

progeny infers signaling pathway activities from gene expression by leveraging Pathway RespOnsive GENes derived from perturbation experiments to capture context-specific downstream signaling signatures.

Key Features:

• Perturbation-derived PRGs: Uses a compendium of perturbation experiments to identify Pathway RespOnsive GENes (PRGs) that represent downstream pathway signatures.
• Linear modeling: Constructs a linear model that integrates gene expression data with perturbation-derived information to estimate pathway activities.
• Accounts for post-translational modifications: Focuses on downstream responsive genes to capture pathway activity effects not apparent from mapping gene expression to pathway components.
• Driver mutation recovery: Recovers the effects of known driver mutations on signaling pathway activity.
• Drug indication and survival markers: Infers pathway activities associated with drug responses and distinguishes oncogenic versus tumor suppressor pathway roles linked to patient survival.

Scientific Applications:

• Cancer signaling analysis: Infer signaling pathway activity in oncology studies to investigate molecular mechanisms of cancer.
• Driver mutation interpretation: Associate driver mutations with changes in signaling pathway activity.
• Drug response marker discovery: Identify pathway activities correlated with therapeutic responses for drug indication studies.
• Survival and prognostic studies: Distinguish oncogenic and tumor suppressor pathway roles to analyze associations with patient survival.

Methodology:

Constructs a linear model that integrates gene expression data with information derived from a compendium of perturbation experiments to identify Pathway RespOnsive GENes and infer pathway activities while focusing on downstream signatures to capture effects of post-translational modifications.