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.
P. Galdi and A. Serra—Equal contribution.
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Acknowledgments
We would like to thank Teresa Savino and Luca Puglia for the helpful discussions.
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Galdi, P., Serra, A., Tagliaferri, R. (2017). Rotation Clustering: A Consensus Clustering Approach to Cluster Gene Expression Data. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_20
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