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Authors: Isidora Šašić 1 ; Sanja Brdar 2 ; Tatjana Lončar-Turukalo 1 ; Helena Aidos 3 and Ana Fred 3

Affiliations: 1 University of Novi Sad, Serbia ; 2 BioSense Institute, Serbia ; 3 Instituto Superior Tecnico, Portugal

Keyword(s): Clustering, Consensus Clustering, Cancer Gene Expression.

Abstract: Clustering algorithms are extensively used on patient tissue samples in order to group and visualize the microarray data. The high dimensionality and probe specific noise make the selection of the appropriate clustering algorithm an uneasy task. This study presents a large-scale analysis of three clustering algorithms: k-means, hierarchical clustering (HC) and evidence accumulation clustering (EAC) on thirty-five cancer gene expression data sets selected to benchmark the performance of the clustering algorithms. Separated performance analysis was done on data sets from Affymetrix and cDNA chip platforms to examine the possible influence of the microarray technology. The study revealed no consistent algorithm ranking can be inferred, though in general EAC presented the best compromise of adjusted rand index (ARI) and variance. However, the results indicated that ARI variance under repeated k-means initializations offers useful information on the need to implement more complex clusteri ng techniques. If repeated K-means converges to the same partition, also confirmed by the HC clustering, there is no need to run EAC. However, under moderate or highly variable ARI in repeated K-means, EAC should be used to reduce the uncertainty of clustering and unveil the data structure. (More)

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Paper citation in several formats:
Šašić, I. ; Brdar, S. ; Lončar-Turukalo, T. ; Aidos, H. and Fred, A. (2017). Consensus Clustering for Cancer Gene Expression Data. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS; ISBN 978-989-758-214-1; ISSN 2184-4305, SciTePress, pages 176-183. DOI: 10.5220/0006174501760183

@conference{bioinformatics17,
author={Isidora Šašić and Sanja Brdar and Tatjana Lončar{-}Turukalo and Helena Aidos and Ana Fred},
title={Consensus Clustering for Cancer Gene Expression Data},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS},
year={2017},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006174501760183},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS
TI - Consensus Clustering for Cancer Gene Expression Data
SN - 978-989-758-214-1
IS - 2184-4305
AU - Šašić, I.
AU - Brdar, S.
AU - Lončar-Turukalo, T.
AU - Aidos, H.
AU - Fred, A.
PY - 2017
SP - 176
EP - 183
DO - 10.5220/0006174501760183
PB - SciTePress