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Bioinformatics Advance Access originally published online on March 3, 2005
Bioinformatics 2005 21(10):2424-2429; doi:10.1093/bioinformatics/bti367
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Integration of GO annotations in Correspondence Analysis: facilitating the interpretation of microarray data

Christian H. Busold 1,*, Stefan Winter 2, Nicole Hauser 3, Andrea Bauer 1, Jürgen Dippon 2, Jörg D. Hoheisel 1 and Kurt Fellenberg 1

1Division of Functional Genome Analysis, Deutsches Krebsforschungszentrum (DKFZ) Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany
2Institut für Stochastik und Anwendungen, Universität Stuttgart Pfaffenwaldring 57, D-70569 Stuttgart, Germany
3Genomics—Proteomics—Screening (GPS), Fraunhofer-Institut für Grenzflächen- und Bioverfahrenstechnik (IGB) Nobelstrasse 12, D-70569 Stuttgart, Germany

*To whom correspondence should be addressed.

Motivation: The functional interpretation of microarray datasets still represents a time-consuming and challenging task. Up to now functional categories that are relevant for one or more experimental context(s) have been commonly extracted from a set of regulated genes and presented in long lists.

Results: To facilitate interpretation, we integrated Gene Ontology (GO) annotations into Correspondence Analysis to display genes, experimental conditions and gene-annotations in a single plot. The position of the annotations in these plots can be directly used for the functional interpretation of clusters of genes or experimental conditions without the need for comparing long lists of annotations. Correspondence Analysis is not limited in the number of experimental conditions that can be compared simultaneously, allowing an easy identification of characterizing annotations even in complex experimental settings. Due to the rapidly increasing amount of annotation data available, we apply an annotation filter. Hereby the number of displayed annotations can be significantly reduced to a set of descriptive ones, further enhancing the interpretability of the plot. We validated the method on transcription data from Saccharomyces cerevisiae and human pancreatic adenocarcinomas.

Availability: The M-CHiPS software is accessible for collaborators at http://www.mchips.org

Contact: c.busold{at}dkfz.de

Supplementary information: http://www.dkfz.de/mchips/supplements/supplement_busold_bioinf_OS.pdf


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