Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review
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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Cite
@article{arxiv.1111.4639,
title = {Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review},
author = {Leo Lahti and Martin Schäfer and Hans-Ulrich Klein and Silvio Bicciato and Martin Dugas},
journal= {arXiv preprint arXiv:1111.4639},
year = {2012}
}
Comments
PDF file including supplementary material. 9 pages. Preprint