Affiliations: Department of Computer Science, University of Wales
Aberystwyth, Penglais, Aberystwyth SY23 3DB, UK
Note: [] Corresponding author: Amanda Clare, Department of Computer
Science, University of Wales Aberystwyth, Penglais, Aber-ystwyth SY23 3DB, UK.
Fax: +44 1970 622455; E-mail: [email protected].
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/.
