BACKGROUND: With the advancement in high-throughput technologies, researchers 
can simultaneously investigate gene expression and copy number alteration (CNA) 
data from individual patients at a lower cost. Traditional analysis methods 
analyze each type of data individually and integrate their results using Venn 
diagrams. Challenges arise, however, when the results are irreproducible and 
inconsistent across multiple platforms. To address these issues, one possible 
approach is to concurrently analyze both gene expression profiling and CNAs in 
the same individual.
RESULTS: We have developed an open-source R/Bioconductor package (iGC). Multiple 
input formats are supported and users can define their own criteria for 
identifying differentially expressed genes driven by CNAs. The analysis of two 
real microarray datasets demonstrated that the CNA-driven genes identified by 
the iGC package showed significantly higher Pearson correlation coefficients 
with their gene expression levels and copy numbers than those genes located in a 
genomic region with CNA. Compared with the Venn diagram approach, the iGC 
package showed better performance.
CONCLUSION: The iGC package is effective and useful for identifying CNA-driven 
genes. By simultaneously considering both comparative genomic and transcriptomic 
data, it can provide better understanding of biological and medical questions. 
The iGC package's source code and manual are freely available at 
https://www.bioconductor.org/packages/release/bioc/html/iGC.html .