综合分析mRNA表达和DNA拷贝数数据确定癌症基因优先顺序的比较研究
Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review
摘要：
各种全基因组分析技术可用于探测基因组结构和功能的互补方面。异构数据源的集成分析可以揭示基于单个观察无法检测到的更高级别的交互。癌症研究中的标准整合任务是基于全基因组基因表达和拷贝数分析测量的联合分析来鉴定诱导相关基因表达变化的改变的基因组区域。在这篇综述中，我们提供了各种建模程序之间的比较，整合基因拷贝数和转录改变的全基因组分析数据，并强调基因组数据整合的常见方法。一个透明的基准程序，引入定量比较癌症基因优先级的替代方法的性能。基准测试算法和数据集可在http://intcomp.r-forge.r-project.org上获得
英文摘要：
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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