intcomp

intcomp performs quantitative benchmarking of cancer gene detection algorithms by integrative analysis of genome-wide DNA copy number and gene expression data to identify genomic alterations that influence gene expression.

Key Features:

• Integrative analysis: Integrates genome-wide DNA copy number and gene expression profiles to detect regions where copy number alterations correlate with expression changes.
• Quantitative benchmarking: Implements a transparent benchmarking procedure to compare the performance of cancer gene detection and prioritization algorithms.
• R package implementation: Provided as an R package for computational analysis and reproducible workflows.
• Algorithms and datasets: Includes collections of algorithms, datasets, and benchmarking results to support comparative evaluations.

Scientific Applications:

• Cancer genomics: Prioritizes genes implicated in oncogenic processes by linking copy number alterations to expression changes.
• Biomarker and therapeutic target identification: Identifies genomic drivers of altered gene expression that may serve as biomarkers or therapeutic targets.
• Algorithm evaluation: Provides quantitative comparisons of cancer gene detection and prioritization methods.

Methodology:

Joint analysis of genome-wide DNA copy number and gene expression data, focusing on correlations between copy number variations and gene expression changes and quantitative benchmarking of cancer gene prioritization algorithms.