Comparison of global tests for functional gene sets in two-group designs and selection of potentially effect-causing genes
Comparison of global tests for functional gene sets in two-group designs and selection of potentially effect-causing genes
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DOI：
10.1093/bioinformatics/btr152
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发表时间：
2011-05-15
期刊：
影响因子：
5.8
通讯作者：
Beissbarth, Tim
中科院分区：
文献类型：
--
作者：
Jung, Klaus;Becker, Benjamin;Beissbarth, Tim
动机：高通量基因组数据分析的一个重要目标是找到功能基因集的表达谱与群体反应的不同水平之间的关联。全局测试通常用于检测整个基因组中的群体效应，而不是针对单个基因的多个测试程序。在模拟研究中，我们比较了四种不同的全局测试方法的功率和计算时间。首次证明了这些方法之一对基因表达数据的适用性。此外，我们提出了一种算法来检测可能导致群体效应的基因。结果：我们可以检测到这三种方法的功效在许多设置中相当，但在计算时间上检测到相当大的差异。当许多基因被改变且影响较小时，我们提出的基因选择算法能够以高功效检测人工集中的潜在影响基因，而当少数基因被改变而产生大影响时，经典的多重测试更强大。
Motivation: An important object in the analysis of high-throughput genomic data is to find an association between the expression profile of functional gene sets and the different levels of a group response. Instead of multiple testing procedures which focus on single genes, global tests are usually used to detect a group effect in an entire gene set. In a simulation study, we compare the power and computation times of four different approaches for global testing. The applicability of one of these methods to gene expression data is demonstrated for the first time. In addition, we propose an algorithm for the detection of those genes which might be responsible for a group effect.Results: We could detect that the power of three of the approaches is comparable in many settings but considerable differences were detected in the computation times. Our proposed gene selection algorithm was able to detect potentially effect-causing genes in artificial sets with high power when many genes were altered with a small effect, while classical multiple testing was more powerful when few genes were altered with a large effect.