Literature DB >> 18203773

Multiple testing on the directed acyclic graph of gene ontology.

Jelle J Goeman1, Ulrich Mansmann.   

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.

Mesh:

Year:  2008        PMID: 18203773     DOI: 10.1093/bioinformatics/btm628

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  24 in total

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4.  Assessing the functional coherence of gene sets with metrics based on the Gene Ontology graph.

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Journal:  Bioinformatics       Date:  2010-06-15       Impact factor: 6.937

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7.  GOGrapher: A Python library for GO graph representation and analysis.

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8.  Association between a prognostic gene signature and functional gene sets.

Authors:  Manuela Hummel; Klaus H Metzeler; Christian Buske; Stefan K Bohlander; Ulrich Mansmann
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9.  From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations.

Authors:  Pan Du; Gang Feng; Jared Flatow; Jie Song; Michelle Holko; Warren A Kibbe; Simon M Lin
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

10.  Incorporating pathway information into boosting estimation of high-dimensional risk prediction models.

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Journal:  BMC Bioinformatics       Date:  2009-01-13       Impact factor: 3.169

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