facopy

facopy models associations between genomic copy number alterations (CNAs) and specific genomic features, including genes, to support cancer genomics analyses.

Key Features:

• Direct Association Measurement: Measures associations between CNAs and specific genomic features, including genes.
• Gene-Set Enrichment Analysis: Performs gene-set enrichment analysis for genes to assess downstream pathway effects of CNAs.
• Systematic CNA Distribution Analysis: Examines CNA distribution differences across tumor phenotypes, including tumor type, location, and progression stages.
• Statistical Modeling of Confounders: Integrates statistical models that account for normal cell contamination and intra-tumor heterogeneity.
• Synthetic SNP Array Data Generation: Generates synthetic datasets that mimic tumor samples genotyped using SNP arrays across varying levels of normal cell contamination.
• Benchmarking and Method Comparison: Supports benchmarking of CNA and LOH detection methods and facilitates comparisons with ASCAT, GAP, GenoCNA, GPHMM, MixHMM, and OncoSNP.

Scientific Applications:

• Investigation of CNA Roles: Investigating the role of CNAs in tumorigenesis and tumor evolution.
• Biomarker and Target Discovery: Identifying potential biomarkers and therapeutic targets associated with specific cancer phenotypes.
• Method Benchmarking: Benchmarking and evaluating CNA and LOH detection methods under varying normal contamination and heterogeneity.
• Model Validation: Validating CNA models by comparing synthetic datasets to real tumor sample results.

Methodology:

Integrates statistical models that account for normal cell contamination and intra-tumor heterogeneity, generates synthetic SNP array tumor datasets across varying contamination levels, and compares synthetic data to real tumor samples to benchmark and validate CNA and LOH detection methods such as ASCAT, GAP, GenoCNA, GPHMM, MixHMM, and OncoSNP.