AMARETTO

AMARETTO integrates copy number, DNA methylation, and gene expression data to identify cancer driver genes and connect them to co-expressed gene modules for improved understanding of cancer regulatory networks.

Key Features:

• Multi-omics integration: Combines copy number, DNA methylation, and gene expression data from cancer samples.
• Driver gene identification: Identifies a set of cancer driver genes through integrative analysis.
• Module detection: Connects driver genes to clusters of co-expressed genes defined as modules.
• Pancancer analysis: Applicable across multiple cancer sites for broad driver gene discovery.
• Survival and subtype prediction: Modules accurately predict survival outcomes and molecular subtypes.
• Pathway enrichment: Captures modules enriched in angiogenesis, cell cycle, and EMT pathways.

Scientific Applications:

• Cancer driver gene discovery: Identifies novel cancer driver genes directing canonical cancer pathways.
• Regulatory network inference: Reconstructs regulatory relationships between driver genes and their target modules.
• Pancancer characterization: Enables cross-tumor analysis of driver genes and associated modules.
• Prognostic biomarker identification: Modules predict patient survival across cancer types.
• Molecular subtype classification: Modules distinguish molecular subtypes in cancer.

Methodology:

AMARETTO uses integrative multi-omics analysis and penalized regression to identify driver genes and connect them to co-expressed gene modules.