STAC

STAC assesses the statistical significance of DNA copy number aberrations (gains and losses) across multiple array-based comparative genomic hybridization (array-CGH) experiments to identify nonrandom genomic regions relevant to cancer genomics.

Key Features:

• Dual-Statistic Approach: Computes two complementary statistical measures to evaluate significance of gains and losses across experiments.
• Novel Search Strategy: Uses a specialized search procedure to optimize identification of significant genomic alterations.
• Multiple Testing Correction: Assigns P-values to each genomic location using a permutation-based multiple testing corrected approach.
• Validation and Performance: Demonstrated identification of known clinically and biologically significant alterations with 85% concordance in two published cancer datasets.
• Prioritization for Follow-Up: Provides P-values for genomic regions to support prioritization of unbiased follow-up studies.

Scientific Applications:

• Cancer genomics: Identification of recurrent nonrandom amplifications and deletions across tumor samples to inform tumor biology and potential therapeutic targets.
• Discovery of novel CNA regions: Detection of previously unreported regions of gain or loss for subsequent biological investigation.
• Analysis of array-CGH datasets: Comparative analysis across multiple array-CGH experiments to detect statistically significant CNAs.

Methodology:

STAC computes two complementary statistics, applies a novel search strategy, and assigns P-values to genomic locations using a permutation-based multiple testing correction.