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

Paola Galdi*, Angela Serra, Roberto Tagliaferri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)229-238
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10147 LNAI
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Clustering
  • Consensus clustering
  • Gene set enrichment
  • Glioblastoma
  • Pathways
  • Rotation forest

Fingerprint

Dive into the research topics of 'Rotation clustering: A consensus clustering approach to cluster gene expression data'. Together they form a unique fingerprint.

Cite this