©1996-2019 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.
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. Bauerschmidt2,4, R.C. van Schaik2,4, E. Piek3, L.M.C. Buydens1*
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
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