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arXiv · 1111.4639

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

Leo Lahti·Martin Schäfer·Hans-Ulrich Klein·Silvio Bicciato·Martin Dugas
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Abstract

A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.org

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Leo Lahti, Martin Schäfer, Hans-Ulrich Klein, Silvio Bicciato, Martin Dugas. 2011-11-20. Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review. https://doi.org/10.1093/bib/bbs005

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