Beyond single p-value cut-offs: Methods to improve decision making in GO enrichment analysis of microarray experiments: Online Journal of Bioinformatics.

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Online Journal of Bioinformatics ©

Volume 11 (1): 1-18, 2010.

Beyond single p-value cut-offs: Methods to improve decision making in GO enrichment analysis of microarray experiments

J.R. de Haan¹, R. Wehrens¹, S. Bauerschmidt^2,4, R.C. van Schaik^2,4, E. Piek³, L.M.C. Buydens^1*

²Schering-Plough, ¹Institute for Molecules and Materials, Analytical Chemistry, ³Department of Applied Biology, ⁴Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen, The Netherlands

ABSTRACT

De Haan JR, Wehrens R, Bauerschmidt S, Van Schaik RC, Piek E, Buydens LMC, Beyond single p-value cut-offs: Methods to improve decision making in GO enrichment analysis of microarray experiments, Online J Bioinformatics, 11 (1): 1-18, 2010. Currently, a large number of tools are available to calculate GO enrichment for gene selections from microarray experiments. It is well known that this leads to conclusions that are dependent on the size of different gene selections. In this paper we will investigate this effect by varying the significance level of both the gene selection cut-off and the GO enrichment cut-off. A number of techniques to visualize the resulting enrichment surface are proposed. Furthermore, ROC plots are used to assess the agreement of the experimental results with current biological knowledge, such as GO annotation. Using these techniques, a stable estimate of association of expression data with GO terms is generated, which is more robust than the results of a single test. The methods introduced in this paper are illustrated by application to a human mesenchymal stem cell data set.

Key-Words: Single p Value, decision making, microarrays

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