gms | German Medical Science

51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (gmds)

10. - 14.09.2006, Leipzig

Ontologic Analysis: Challenges for Global Testing

Meeting Abstract

Suche in Medline nach

  • Manuela Hummel - Ludwig-Maximilians-Universität München, München

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (gmds). 51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Leipzig, 10.-14.09.2006. Düsseldorf, Köln: German Medical Science; 2006. Doc06gmds012

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2006/06gmds092.shtml

Veröffentlicht: 1. September 2006

© 2006 Hummel.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction

This talk introduces an ANOVA based global test for differential gene expression between different clinical outcomes. It will be shown how this test offers an alternative for gene set enrichment on the Gene Ontology (GO) graph. Special attention will be given to the adjustment for multiple testing.

Material and Methods

The ANOVA based global test for differential gene expression in origin was designed for the comparison of two clinical groups. The approach can easily be extended for the analysis of more complex tasks like differential expression in various groups or differential co-expression of genes.

The method is used to quantify the differential gene expression between phenotype groups for the genes associated with a specific GO category. It is of interest to identify the nodes of the GO tree with differential gene expression between the groups. These nodes may help to deepen insights into biological backgrounds. The strategy of applying global tests on the GO graph offers an alternative for gene set enrichment.

The basic problem is the adjustment for multiplicity within this extensive statistical testing problem. We propose an approach to the multiplicity problem which uses a stochastic process on the GO tree induced by the null hypothesis of no differential expression. The algorithm needs an ordering of GO nodes. We present a first natural ordering idea and discuss its optimality.

Results

It will be shown that our approach improves the use of information on changed gene function compared to the strategy of gene set enrichment.

Discussion

Various methods of adjustment for multiple testing will be compared and discussed.


References

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