Advanced analysis and visualization of gene copy number and expression data

• Published in: BMC bioinformatics 2009-01, Vol.10 Suppl 1 (S1), p.S70-S70, Article S70
• Main Authors: Autio, Reija, Saarela, Matti, Järvinen, Anna-Kaarina, Hautaniemi, Sampsa, Astola, Jaakko
• Format: Article
• Language: English
• Subjects: Comparative Genomic Hybridization; Gene Dosage - genetics; Gene Expression; Gene Expression Profiling - methods; Genome, Human; Head and Neck Neoplasms - genetics; Humans; Software; Tongue Neoplasms - genetics
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-10-S1-S70

Gene copy number and gene expression values play important roles in cancer initiation and progression. Both can be measured with high-throughput microarrays and some methodologies to integrate and analyze these data exist. However, varying gene sets within different gene expression and copy number microarrays present significant challenges. We report an advanced version of earlier published CGH-Plotter that rapidly can identify amplified and deleted areas using gene copy number data. With CGH-Plotter v2, the copy number values can be filtered based on the genomic location in basepair units. After filtering, the values for the missing genes can be interpolated. Moreover, the effect of non-informative areas in the genome can be systematically removed by smoothing and interpolating. Further, we developed a tool (ECN) to illustrate the CGH-data values annotated based on the gene expression. The ECN-tool is a MATLAB toolbox enabling straightforward illustration of copy numbers annotated based on the gene expression levels. CGH-Plotter v2 provides two methods for analyzing copy number data; dynamic programming and genomic location based smoothing. With ECN-tool the data analyzed with CGH-Plotter v2 can easily be illustrated along the chromosomes individually or along the whole genome. ECN-tool plots the copy number data annotated based on the gene expression data, and it is easy to find the genes that are both over-expressed and amplified or under-expressed and deleted in the samples. From the resulting figures it is straightforward to select interesting genes.