Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Research article

Selection of informative clusters from hierarchical cluster tree with gene classes

Petri Toronen

Author Affiliations

A. I. Virtanen Institute for Molecular Sciences, Neulaniementie 2, P.O. Box 1627, FIN-70211 Kuopio, Finland

BMC Bioinformatics 2004, 5:32  doi:10.1186/1471-2105-5-32

Published: 25 March 2004

Abstract

Background

A common clustering method in the analysis of gene expression data has been hierarchical clustering. Usually the analysis involves selection of clusters by cutting the tree at a suitable level and/or analysis of a sorted gene list that is obtained with the tree. Cutting of the hierarchical tree requires the selection of a suitable level and it results in the loss of information on the other level. Sorted gene lists depend on the sorting method of the joined clusters. Author proposes that the clusters should be selected using the gene classifications.

Results

This article presents a simple method for searching for clusters with the strongest enrichment of gene classes from a cluster tree. The clusters found are presented in the estimated order of importance. The method is demonstrated with a yeast gene expression data set and with two database classifications. The obtained clusters demonstrated a very strong enrichment of functional classes. The obtained clusters are also able to present similar gene groups to those that were observed from the data set in the original analysis and also many gene groups that were not reported in the original analysis. Visualization of the results on top of a cluster tree shows that the method finds informative clusters from several levels of the cluster tree and indicates that the clusters found could not have been obtained by simply cutting the cluster tree. Results were also used in the comparison of cluster trees from different clustering methods.

Conclusion

The presented method should facilitate the exploratory analysis of big data sets when the associated categorical data is available.