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 › peer-review

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

Access to Document

10.1016/j.compbiomed.2010.01.003

Cite this

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

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

@article{441902975e10426c85425ef888d67f3d,

title = "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 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.",

keywords = "Breast cancer, Clustering methods, Consensus clustering, Molecular classification, Validity indices",

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

year = "2010",

month = mar,

doi = "10.1016/j.compbiomed.2010.01.003",

language = "English",

volume = "40",

pages = "318--330",

journal = "Computers in Biology and Medicine",

issn = "0010-4825",

publisher = "Elsevier Limited",

number = "3",

}

TY - JOUR

T1 - A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

AU - Soria, Daniele

AU - Garibaldi, Jonathan M.

AU - Ambrogi, Federico

AU - Green, Andrew R.

AU - Powe, Des

AU - Rakha, Emad

AU - Douglas Macmillan, R.

AU - Blamey, Roger W.

AU - Ball, Graham

AU - Lisboa, Paulo J G

AU - Etchells, Terence A.

AU - Boracchi, Patrizia

AU - Biganzoli, Elia

AU - Ellis, Ian O.

PY - 2010/3

Y1 - 2010/3

N2 - 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.

AB - 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.

KW - Breast cancer

KW - Clustering methods

KW - Consensus clustering

KW - Molecular classification

KW - Validity indices

UR - http://www.scopus.com/inward/record.url?scp=77549085806&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77549085806&partnerID=8YFLogxK

U2 - 10.1016/j.compbiomed.2010.01.003

DO - 10.1016/j.compbiomed.2010.01.003

M3 - Article

C2 - 20106472

AN - SCOPUS:77549085806

VL - 40

SP - 318

EP - 330

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

IS - 3

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this