Advanced analysis and visualization of gene copy number and expression data

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Gene copy number and expression are crucial in cancer development.
• High-throughput microarrays measure these values, but data integration is challenging due to varying gene sets.
• Existing methods struggle with integrating diverse gene expression and copy number microarray data.

Purpose of the Study:

• To present CGH-Plotter v2 for rapid identification of amplified/deleted genomic regions.
• To introduce the ECN-tool for illustrating copy number data annotated by gene expression.
• To provide a robust method for analyzing and visualizing gene copy number alterations in cancer.

Main Methods:

• CGH-Plotter v2 utilizes dynamic programming and genomic location-based smoothing for copy number analysis.
• Data filtering by genomic location (basepair units) and interpolation of missing values are key features.
• The ECN-tool, a MATLAB toolbox, visualizes copy number data alongside gene expression levels.

Main Results:

• CGH-Plotter v2 effectively identifies amplified and deleted genomic areas.
• The software allows filtering, interpolation, and removal of non-informative genomic regions.
• The ECN-tool successfully illustrates copy number variations annotated by gene expression, facilitating identification of key genes.

Conclusions:

• CGH-Plotter v2 offers advanced methods for copy number data analysis.
• The ECN-tool enables clear visualization of copy number and gene expression correlations.
• These integrated tools simplify the identification of genes with combined copy number alterations and altered expression, aiding cancer research.