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

• PMID: 15746280
• DOI full text
• PubMed

Reference Type

Evaluation Study | Journal Article

Authors

Busold CH, Winter S, Hauser N, Bauer A, Dippon J, Hoheisel JD, Fellenberg K

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