A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

Soria, Daniele; Garibaldi, Jonathan M.; Ambrogi, Federico; Green, Andrew R.; Powe, Des; Rakha, Emad; Douglas Macmillan, R.; Blamey, Roger W.; Ball, Graham; Lisboa, Paulo J.G.; Etchells, Terence A.; Boracchi, Patrizia; Biganzoli, Elia M.; Ellis, Ian O.

Abstract

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’ by using a range of techniques to reach consensus across several different clustering algorithms, and to ascertain the key characteristics of these classes. We apply the methodology to immunohistochemical data from breast cancer patients. In doing so, we identify six core classes, of which several may be novel sub-groups not previously emphasised in literature.

Citation

Soria, D., Garibaldi, J. M., Ambrogi, F., Green, A. R., Powe, D., Rakha, E., …Ellis, I. O. (2010). A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Computers in Biology and Medicine, 40(3), https://doi.org/10.1016/j.compbiomed.2010.01.003

• Journal Article Type: Article
• Publication Date: Mar 1, 2010
• Deposit Date: Jan 30, 2015
• Publicly Available Date: Jan 30, 2015
• Journal: Computers in biology and medicine
• Print ISSN: 0010-4825
• Electronic ISSN: 1879-0534
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 40
• Issue: 3
• DOI: https://doi.org/10.1016/j.compbiomed.2010.01.003
• Public URL: https://nottingham-repository.worktribe.com/output/1012138
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0010482510000053
• Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Computers in Biology and Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers in Biology and Medicine, 40(3), 2010. doi: 10.1016/j.compbiomed.2010.01.003