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

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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)318-330
Number of pages13
JournalComputers in Biology and Medicine
Volume40
Issue number3
DOIs
Publication statusPublished - Mar 2010

Keywords

  • Breast cancer
  • Clustering methods
  • Consensus clustering
  • Molecular classification
  • Validity indices

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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    Soria, D., Garibaldi, J. M., Ambrogi, F., Green, A. R., Powe, D., Rakha, E., Douglas Macmillan, R., Blamey, R. W., Ball, G., Lisboa, P. J. G., Etchells, T. A., Boracchi, P., Biganzoli, 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), 318-330. https://doi.org/10.1016/j.compbiomed.2010.01.003