Using functional annotation to improve clusterings of gene expression patterns

Authors:
Per Jonsson;Kim Laurio;Zelmina Lubovac;Björn Olsson;Magnus L. Andersson
Affiliations:
CMB-Genetics, University of Gothenburg, 405 30 Gothenburg, Sweden;Department of Computer Science, Bioinformatics Research Group, University of Skövde, Box 408, 541 28 Skövde, Sweden;Department of Computer Science, Bioinformatics Research Group, University of Skövde, Box 408, 541 28 Skövde, Sweden;Department of Computer Science, Bioinformatics Research Group, University of Skövde, Box 408, 541 28 Skövde, Sweden;Molecular Biology, AstraZeneca R&D Möölndal, 431 83 Mölndal, Sweden
Venue:
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Year:
2002

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Abstract

The goal of many gene expression experiments is to discover genes that are functionally related by clustering expression levels sampled over some time interval, with the hope that co-regulated genes also are functionally related. However, it is not necessarily always true that co-regulated genes are functionally related, or vice versa, and therefore this paper investigates the value of including gene annotation in the clustering process. Results suggest that clusters formed by a clustering of a combination of expression data and annotation in the form of enzyme classification can give results that have higher correlation with known biological data (functional and metabolic pathway) not included in the clustering process. The results show that the same is true even in a situation with only 10% of the dataset annotated, which is an estimate of the amount of enzymatic annotation available today and a sign that the inclusion of added data helps in the clustering of genes without any explicit annotation.