Evaluation of clustering algorithms for gene expression data

Background: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist. Results: In this paper, we propose two validation measures each with two parts: one measuring the statistical consistency (stability) of the clusters produced and the other representing their biological functional congruence. Smaller values of these indices indicate better performance for a clustering algorithm. We illustrate this approach using two case studies with publicly available gene expression data sets: one involving a SAGE data of breast cancer patients and the other in...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Susmita Datta, Somnath Datta
• Keywords: BMCBI 2006, Clustering Algorithm, Gene Expression Data, Numerous Clustering Algorithms