A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients.
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.
Soria
Daniele
D
School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, UK.
Garibaldi
Jonathan M
JM
Ambrogi
Federico
F
Green
Andrew R
AR
Powe
Des
D
Rakha
Emad
E
Macmillan
R Douglas
RD
Blamey
Roger W
RW
Ball
Graham
G
Lisboa
Paulo J G
PJ
Etchells
Terence A
TA
Boracchi
Patrizia
P
Biganzoli
Elia
E
Ellis
Ian O
IO
D000465:Algorithms; D001943:Breast Neoplasms / Q000378:metabolism*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007150:Immunohistochemistry