Statistics Department, University of Barcelona, Barcelona, Spain.
BMC Bioinformatics. 2011 Oct 16;12:401. doi: 10.1186/1471-2105-12-401.
How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example.
A new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/release/bioc/html/goProfiles.html.
The method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods.
如何根据生物学意义比较研究是高通量基因组学中的一个核心问题。许多用于执行此类比较的方法都基于功能注释数据库中的信息，例如构成基因本体论 (GO) 的数据库。通常，它们包括以类为单位，逐个分析某些预定义 GO 类中的基因注释频率，然后对多重检验进行 p 值调整。富集分析是比较基因列表与更广泛基因集合的最常见示例。
提出了一种新的全局测试程序和一种包含该程序的方法。新方法不是为每个 GO 类分别进行测试，而是对所有考虑的类进行单一的全局测试。该测试基于功能谱之间的距离，功能谱定义为在给定的 GO 类集合中注释的联合频率。这些类可以在一个或多个 GO 级别上选择。与通常的类为单位的方法相比，新的全局测试在控制第一类错误方面更强大和准确。当应用于一些真实数据集时，结果表明，如果某些类中的基因计数稀疏，该方法还可以提供有用的信息，补充类为单位的方法进行的测试。R 库 goProfiles 实现了这些方法，并可从 Bioconductor 获得，网址为 http://bioconductor.org/packages/release/bioc/html/goProfiles.html。
该方法为判断两个列表是否在功能上不同提供了推理基础。对于全局比较，它优于全局同质性卡方检验。此外，如果与类为单位的方法结合使用，它可能会提供额外的信息。
