iGC

iGC identifies differentially expressed genes driven by Copy Number Alterations (CNAs) by integrating gene expression and CNA data from individual patients within an R/Bioconductor package.

Key Features:

• Concurrent Analysis: The tool analyzes gene expression profiling and CNAs simultaneously within the same individual sample.
• Multiple Input Formats: iGC supports various input formats for integrated analysis of genomic and transcriptomic data.
• Customizable Criteria: The package allows specification of criteria to define genes affected by CNAs.

Scientific Applications:

• Improved Correlation: Analysis using iGC yields higher Pearson correlation coefficients between identified CNA-driven genes' expression levels and copy numbers than traditional Venn-diagram-based approaches.
• Enhanced Performance: Compared with the Venn diagram approach, iGC improves identification of relevant CNA-driven genes for studies of gene regulation and cancer biology.

Methodology:

Integrated, simultaneous analysis of gene expression profiling and copy number alteration (CNA) data from individual samples using comparative genomic and transcriptomic data to identify CNA-driven gene expression changes.