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Bioinformatics Advance Access published online on March 3, 2005
Bioinformatics, 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@oupjournals.org
Received August 31, 2004
Revised February 1, 2005
Accepted February 28, 2005

Article

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

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

* To whom correspondence should be addressed.
Christian H. Busold, E-mail: c.busold{at}dkfz.de


  Abstract

Motivation: The functional interpretation of microarray datasets still represents a time consuming and challenging task. Up to now functional categories being relevant for one or more experimental context(s) are 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 functional interpretation of clusters of genes or experimental conditions without comparison of 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 via http://www.mchips.org.


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