Nam Dougu, Kim Seon-Young
Functional Genomics Research Center, KRIBB, 111 Gwahangno, Yuseong-gu, Daejeon 305-806, Korea.
Brief Bioinform. 2008 May;9(3):189-97. doi: 10.1093/bib/bbn001. Epub 2008 Jan 17.
Recently developed gene set analysis methods evaluate differential expression patterns of gene groups instead of those of individual genes. This approach especially targets gene groups whose constituents show subtle but coordinated expression changes, which might not be detected by the usual individual gene analysis. The approach has been quite successful in deriving new information from expression data, and a number of methods and tools have been developed intensively in recent years. We review those methods and currently available tools, classify them according to the statistical methods employed, and discuss their pros and cons. We also discuss several interesting extensions to the methods.
最近开发的基因集分析方法评估的是基因组的差异表达模式，而非单个基因的差异表达模式。这种方法特别针对其组成部分表现出细微但协调的表达变化的基因组，而这些变化可能无法通过常规的单个基因分析检测到。该方法在从表达数据中获取新信息方面相当成功，近年来已经密集开发了许多方法和工具。我们回顾这些方法和当前可用的工具，根据所采用的统计方法对它们进行分类，并讨论它们的优缺点。我们还讨论了这些方法的几个有趣的扩展。
