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In Silico Biology 2, 0046 (2002); ©2002, Bioinformation Systems e.V.  



How well do we understand the clusters found in microarray data?

Amanda Clare* and Ross D. King

Department of Computer Science, University of Wales Aberystwyth, Penglais, Aberystwyth SY23 3DB
Fax: +44-1970-622455
E-mail: ajc99@aber.ac.uk

* corresponding author


Edited by H. Michael; received June 26, 2002; revised and accepted August 29, 2002; published September 20, 2002


Abstract

We wished to quantify the state-of-the-art of our understanding of clusters in microarray data. To do this we systematically compared the clusters produced on sets of microarray data using a representative set of clustering algorithms (hierarchical, k-means, and a modified version of QT_CLUST) with the annotation schemes MIPS, GeneOntology and GenProtEC. We assumed that if a cluster reflected known biology its members would share related ontological annotations. This assumption is the basis of "guilt-by-association" and is commonly used to assign the putative function of proteins. To statistically measure the relationship between cluster and annotation we developed a new predictive discriminatory measure.

We found that the clusters found in microarray data do not in general agree with functional annotation classes. Although many statistically significant relationships can be found, the majority of clusters are not related to known biology (as described in annotation ontologies). This implies that use of guilt-by-association is not supported by annotation ontologies. Depending on the estimate of the amount of noise in the data, our results suggest that bioinformatics has only codified a small proportion of the biological knowledge required to understand microarray data.

The annotated clusters can be found at http://www.aber.ac.uk/compsci/Research/bio/dss/gba/.


Key words: Ontology, statistics, machine-learning, transcriptome, microarray data.