Zhong Sheng, Tian Lu, Li Cheng, Storch Kai-Florian, Wong Wing H
Department of Biostatistics, Harvard University, USA.
Proc IEEE Comput Syst Bioinform Conf. 2004:425-35. doi: 10.1109/csb.2004.1332455.
The Gene Ontology (GO) resource can be used as a powerful tool to uncover the properties shared among, and specific to, a list of genes produced by high-throughput functional genomics studies, such as microarray studies. In the comparative analysis of several gene lists, researchers maybe interested in knowing which GO terms are enriched in one list of genes but relatively depleted in another. Statistical tests such as Fisher's exact test or Chi-square test can be performed to search for such GO terms. However, because multiple GO terms are tested simultaneously, individual p-values from individual tests do not serve as good indicators for picking GO terms. Furthermore, these multiple tests are highly correlated, usual multiple testing procedures that work under an independence assumption are not applicable. In this paper we introduce a procedure, based on False Discovery Rate (FDR), to treat this correlated multiple testing problem. This procedure calculates a moderately conserved estimator of q-value for every GO term. We identify the GO terms with q-values that satisfy a desired level as the significant GO terms. This procedure has been implemented into the GoSurfer software. GoSurfer is a windows based graphical data mining tool. It is freely available at http://www.gosurfer.org.
基因本体论（GO）资源可作为一种强大的工具，用于揭示高通量功能基因组学研究（如微阵列研究）产生的一系列基因所共有的特性以及特定于这些基因的特性。在对多个基因列表进行比较分析时，研究人员可能有兴趣了解哪些GO术语在一个基因列表中富集，但在另一个基因列表中相对匮乏。可以进行诸如Fisher精确检验或卡方检验等统计测试来搜索此类GO术语。然而，由于同时测试了多个GO术语，单个测试的单个p值不能很好地作为选择GO术语的指标。此外，这些多重测试高度相关，通常在独立性假设下有效的多重测试程序并不适用。在本文中，我们引入了一种基于错误发现率（FDR）的程序来处理这种相关的多重测试问题。该程序为每个GO术语计算一个适度保守的q值估计量。我们将q值满足期望水平的GO术语识别为显著的GO术语。此程序已在GoSurfer软件中实现。GoSurfer是一个基于Windows的图形化数据挖掘工具。可从http://www.gosurfer.org免费获取。
