Dinu Irina, Liu Qi, Potter John D, Adewale Adeniyi J, Jhangri Gian S, Mueller Thomas, Einecke Gunilla, Famulsky Konrad, Halloran Philip, Yasui Yutaka
School of Public Health, University of Alberta, 13-106 Clinical Sciences Building, Edmonton, AB, Canada.
Cancer Inform. 2008;6:357-68. doi: 10.4137/cin.s867. Epub 2008 Jun 20.
Gene-set analysis of microarray data evaluates biological pathways, or gene sets, for their differential expression by a phenotype of interest. In contrast to the analysis of individual genes, gene-set analysis utilizes existing biological knowledge of genes and their pathways in assessing differential expression. This paper evaluates the biological performance of five gene-set analysis methods testing "self-contained null hypotheses" via subject sampling, along with the most popular gene-set analysis method, Gene Set Enrichment Analysis (GSEA). We use three real microarray analyses in which differentially expressed gene sets are predictable biologically from the phenotype. Two types of gene sets are considered for this empirical evaluation: one type contains "truly positive" sets that should be identified as differentially expressed; and the other type contains "truly negative" sets that should not be identified as differentially expressed. Our evaluation suggests advantages of SAM-GS, Global, and ANCOVA Global methods over GSEA and the other two methods.
微阵列数据的基因集分析通过感兴趣的表型评估生物途径或基因集的差异表达。与单个基因的分析不同，基因集分析在评估差异表达时利用了基因及其途径的现有生物学知识。本文通过受试者抽样评估了五种检验“自包含零假设”的基因集分析方法的生物学性能，以及最流行的基因集分析方法——基因集富集分析（GSEA）。我们使用了三项真实的微阵列分析，其中差异表达的基因集在生物学上可从表型预测。本次实证评估考虑了两种类型的基因集：一种类型包含应被识别为差异表达的“真正阳性”集；另一种类型包含不应被识别为差异表达的“真正阴性”集。我们的评估表明，与GSEA和其他两种方法相比，SAM-GS、全局法和协方差分析全局法具有优势。
