Rotation clustering: A consensus clustering approach to cluster gene expression data

Abstract

In this work we present Rotation clustering, a novel method for consensus clustering inspired by the classifier ensemble model Rotation Forest. We demonstrate the effectiveness of our method in a real world application, the identification of enriched gene sets in a TCGA dataset derived from a clinical study on Glioblastoma multiforme. The proposed approach is compared with a classical clustering algorithm and with two other consensus methods. Our results show that this method has been effective in finding significant gene groups that show a common behaviour in terms of expression patterns.