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.

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

GALDI, PAOLA;SERRA, ANGELA;TAGLIAFERRI, Roberto
2017-01-01

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.
978-3-319-52961-5
978-3-319-52962-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4695690
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