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

doi:10.1016/j.compbiomed.2010.01.003

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

Fondazione IRCCS Istituto Nazionale dei Tumori

Research output: Contribution to journal › Article

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 language	English
Pages (from-to)	318-330
Number of pages	13
Journal	Computers in Biology and Medicine
Volume	40
Issue number	3
DOIs	https://doi.org/10.1016/j.compbiomed.2010.01.003
Publication status	Published - 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

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; Ellis, Ian O.

In: Computers in Biology and Medicine, Vol. 40, No. 3, 03.2010, p. 318-330.

Research output: Contribution to journal › Article

Soria, D, Garibaldi, JM, Ambrogi, F, Green, AR, Powe, D, Rakha, E, Douglas Macmillan, R, Blamey, RW, Ball, G, Lisboa, PJG, Etchells, TA, Boracchi, P, Biganzoli, E & Ellis, IO 2010, 'A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients', Computers in Biology and Medicine, vol. 40, no. 3, pp. 318-330. https://doi.org/10.1016/j.compbiomed.2010.01.003

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. / A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. In: Computers in Biology and Medicine. 2010 ; Vol. 40, No. 3. pp. 318-330.

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