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

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...

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Bibliographic Details
Published in:Computers in biology and medicine 2010-03, Vol.40 (3), p.318-330
Main Authors: 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, Ellis, Ian O
Format: Article
Language:English
Subjects:
Algorithms
Breast cancer
Breast Neoplasms - metabolism
Cluster Analysis
Clustering methods
Consensus clustering
Female
Humans
Immunohistochemistry
Internal Medicine
Molecular classification
Other
Validity indices
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Summary: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.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2010.01.003