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

Authors:
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
Affiliations:
School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK;School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK;Institute of Medical Statistics and Biometry, University of Milan, Via Venezian 1, 20133 Milan, Italy;School of Molecular Medical Sciences, Nottingham University Hospitals and University of Nottingham, Queens Medical Centre, Derby Road, Nottingham NG7 2UH, UK;School of Molecular Medical Sciences, Nottingham University Hospitals and University of Nottingham, Queens Medical Centre, Derby Road, Nottingham NG7 2UH, UK;School of Molecular Medical Sciences, Nottingham University Hospitals and University of Nottingham, Queens Medical Centre, Derby Road, Nottingham NG7 2UH, UK;The Breast Institute, Nottingham City Hospital, Hucknall Road, Nottingham NG5 1PB, UK;The Breast Institute, Nottingham City Hospital, Hucknall Road, Nottingham NG5 1PB, UK;School of Science and Technology, Nottingham Trent University, Clifton Campus, Clifton Lane, Nottingham NG11 8NS, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK;Institute of Medical Statistics and Biometry, University of Milan, Via Venezian 1, 20133 Milan, Italy;Institute of Medical Statistics and Biometry, University of Milan, Via Venezian 1, 20133 Milan, Italy;School of Molecular Medical Sciences, Nottingham University Hospitals and University of Nottingham, Queens Medical Centre, Derby Road, Nottingham NG7 2UH, UK
Venue:
Computers in Biology and Medicine
Year:
2010

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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.