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

Amanda Clare, Ross Donald King

Research output: Contribution to journalArticlepeer-review

28 Citations (SciVal)


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
Original languageEnglish
Pages (from-to)511-522
JournalIn Silico Biology
Issue number4
Publication statusPublished - 2002


Dive into the research topics of 'How well do we understand the clusters found in microarray data?'. Together they form a unique fingerprint.

Cite this