On the selection of appropriate distances for gene expression data clustering

Jaskowiak, Pablo A., Campello, Ricardo J.G.B., and Costa, Ivan G. (2014) On the selection of appropriate distances for gene expression data clustering. BMC Bioinformatics, 15 (Suppl 2). pp. 1-17.

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Abstract

Background: Clustering is crucial for gene expression data analysis. As an unsupervised exploratory procedure its results can help researchers to gain insights and formulate new hypothesis about biological data from microarrays. Given different settings of microarray experiments, clustering proves itself as a versatile exploratory tool. It can help to unveil new cancer subtypes or to identify groups of genes that respond similarly to a specific experimental condition. In order to obtain useful clustering results, however, different parameters of the clustering procedure must be properly tuned. Besides the selection of the clustering method itself, determining which distance is going to be employed between data objects is probably one of the most difficult decisions.

Results and conclusions: We analyze how different distances and clustering methods interact regarding their ability to cluster gene expression, i.e., microarray data. We study 15 distances along with four common clustering methods from the literature on a total of 52 gene expression microarray datasets. Distances are evaluated on a number of different scenarios including clustering of cancer tissues and genes from short time-series expression data, the two main clustering applications in gene expression. Our results support that the selection of an appropriate distance depends on the scenario in hand. Moreover, in each scenario, given the very same clustering method, significant differences in quality may arise from the selection of distinct distance measures. In fact, the selection of an appropriate distance measure can make the difference between meaningful and poor clustering outcomes, even for a suitable clustering method.

Item ID: 47068
Item Type: Article (Research - C1)
ISSN: 1471-2105
Keywords: distance, similarity, dissimilarity, correlation, proximity, clustering, gene expression, microarray, cancer, time-series
Additional Information:

Proceedings from The Twelfth Asia Pacific Bioinformatics Conference (APBC 2014). Shanghai, China. 17-19 January 2014.

© 2014 Jaskowiak et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Funders: Coordenadoria de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação do Amparo a Ciência e Tecnologia de Pernambuco (FACEPE), São Paulo Research Foundation (FAPESP), German federal and state governments (GFSG), German Research Foundation (GRS), Interdisciplinary Centre for Clinical Research within the faculty of Medicine at RWTH Aachen University (IZKF Aachen)
Projects and Grants: CAPES/CNPq/FACEPE/FAPESP Process #2011/02247-5, CAPES/CNPq/FACEPE/FAPESP Process #2012/15751-9, GFSG/GRS Excellence Initiative Grant GSC 111
Date Deposited: 04 Jan 2017 08:04
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010499 Statistics not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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