TreeHugger: A New Test for Enrichment of Gene Ontology Terms
INFORMS Journal on Computing
Computing gene functional similarity using combined graphs
Proceedings of the 27th Annual ACM Symposium on Applied Computing
| Hi-index | 3.84 |
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.