TAGCNA

TAGCNA identifies significant consensus events (SCEs) from somatic copy number alterations (CNAs) in cancer genomes to pinpoint recurrent CNAs affecting proto-oncogenes and tumor suppressor genes.

Key Features:

• Identification of Significant Consensus Events: Distinguishes SCEs from random background CNAs across multiple subjects to detect recurrent copy-number changes.
• Tag CNA Marker Selection: Selects tag CNA markers from the studied genome as the basis for downstream significance testing.
• Peel-off Permutation Scheme: Employs a peel-off permutation scheme to generate a null distribution that accounts for the structure of tag CNA markers.
• Statistical Robustness: Controls type I error rate to ensure that identified SCEs are statistically significant.
• Validation with Real Data: Performance assessed using simulations with ground-truth data and validated on lung adenocarcinoma and prostate adenocarcinoma datasets.
• Biological Relevance: Identifies SCEs associated with cancer-related genes such as EGFR and CDKN2A/B and detects additional potentially relevant events.

Scientific Applications:

• Driver Gene Identification: Pinpoints genomic regions harboring cancer driver genes through recurrent SCE detection.
• Genomic Analysis Across Cancers: Enables comparative analysis of CNAs across cancer types, as demonstrated in lung adenocarcinoma and prostate adenocarcinoma.
• Research and Clinical Insights: Provides genomic alteration profiles that inform basic research and clinical studies of oncogenesis.

Methodology:

Selection of tag CNA markers from the genome followed by a peel-off permutation scheme to generate a null distribution and assess significance while controlling type I error, with validation via simulations using ground-truth data and testing on lung adenocarcinoma and prostate adenocarcinoma datasets.