Multiple testing on the directed acyclic graph of gene ontology
Open Access
- 18 January 2008
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 24 (4) , 537-544
- https://doi.org/10.1093/bioinformatics/btm628
Abstract
Motivation: Current methods for multiplicity adjustment do not make use of the graph structure of Gene Ontology (GO) when testing for association of expression profiles of GO terms with a response variable. Results: We propose a multiple testing method, called the focus level procedure, that preserves the graph structure of Gene Ontology (GO). The procedure is constructed as a combination of a Closed Testing procedure with Holm's method. It requires a user to choose a ‘focus level’ in the GO graph, which reflects the level of specificity of terms in which the user is most interested. This choice also determines the level in the GO graph at which the procedure has most power. We prove that the procedure strongly controls the family-wise error rate without any additional assumptions on the joint distribution of the test statistics used. We also present an algorithm to calculate multiplicity-adjusted P-values. Because the focus level procedure preserves the structure of the GO graph, it does not generally preserve the ordering of the raw P-values in the adjusted P-values. Availability: The focus level procedure has been implemented in the globaltest and GlobalAncova packages, both of which are available on www.bioconductor.org. Contact:j.j.goeman@lumc.nl Supplementary information: Supplementary data are available at Bioinformatics online.Keywords
This publication has 19 references indexed in Scilit:
- Improving gene set analysis of microarray data by SAM-GSBMC Bioinformatics, 2007
- Analyzing gene expression data in terms of gene sets: methodological issuesBioinformatics, 2007
- Improved scoring of functional groups from gene expression data by decorrelating GO graph structureBioinformatics, 2006
- Ontological analysis of gene expression data: current tools, limitations, and open problemsBioinformatics, 2005
- Testing association of a pathway with survival using gene expression dataBioinformatics, 2005
- Testing Differential Gene Expression in Functional GroupsMethods of Information in Medicine, 2005
- A global test for groups of genes: testing association with a clinical outcomeBioinformatics, 2004
- Multiple Hypothesis Testing in Microarray ExperimentsStatistical Science, 2003
- Gene Ontology: tool for the unification of biologyNature Genetics, 2000
- On closed testing procedures with special reference to ordered analysis of varianceBiometrika, 1976