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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis

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Author(s):
Jaskowiak, Pablo A. [1] ; Campello, Ricardo J. G. B. [1] ; Costa, Ivan G. [2, 3]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Univ Fed Pernambuco, Ctr Informat, Recife, PE - Brazil
[3] Univ Aachen, Sch Med, Rhein Westfal TH Aachen, IZKF Computat Biol Res Grp, Inst Biomed Engn, Aachen - Germany
Total Affiliations: 3
Document type: Journal article
Source: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS; v. 10, n. 4, p. 845-857, JUL-AUG 2013.
Web of Science Citations: 18
Abstract

Cluster analysis is usually the first step adopted to unveil information from gene expression microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity measure (similarity or distance) is of great importance to achieve satisfactory clustering results. Nevertheless, up to date, there are no comprehensive guidelines concerning how to choose proximity measures for clustering microarray data. Pearson is the most used proximity measure, whereas characteristics of other ones remain unexplored. In this paper, we investigate the choice of proximity measures for the clustering of microarray data by evaluating the performance of 16 proximity measures in 52 data sets from time course and cancer experiments. Our results support that measures rarely employed in the gene expression literature can provide better results than commonly employed ones, such as Pearson, Spearman, and euclidean distance. Given that different measures stood out for time course and cancer data evaluations, their choice should be specific to each scenario. To evaluate measures on time-course data, we preprocessed and compiled 17 data sets from the microarray literature in a benchmark along with a new methodology, called Intrinsic Biological Separation Ability (IBSA). Both can be employed in future research to assess the effectiveness of new measures for gene time-course data. (AU)

FAPESP's process: 11/04247-5 - Gene Selection and Clustering Validation in Gene Expression Data
Grantee:Pablo Andretta Jaskowiak
Support Opportunities: Scholarships in Brazil - Doctorate