Wageningen University, Laboratory of Microbiology, 6703HB Wageningen, Netherlands.
Brief Bioinform. 2013 Jan;14(1):27-35. doi: 10.1093/bib/bbs005. Epub 2012 Mar 22.
A variety of genome-wide profiling techniques are available to investigate 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 highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.
有多种全基因组分析技术可用于研究基因组结构和功能的互补方面。异质数据源的综合分析可以揭示基于单个观察无法检测到的更高层次的相互作用。癌症研究中的一个标准整合任务是识别改变基因组区域，这些区域根据全基因组基因表达和拷贝数谱测量的联合分析导致相关基因表达的变化。在这篇综述中，我们强调了基因组数据整合的常见方法，并提供了一种透明的基准测试程序，以定量比较癌症基因优先级排序中方法的性能。算法、数据集和基准测试结果可在 http://intcomp.r-forge.r-project.org 获得。
