Identification of Robust Clustering Methods in Gene Expression Data Analysis

Author(s): Md. Bipul Hossen*, Md. Siraj-Ud-Doulah

Journal Name: Current Bioinformatics

Volume 12 , Issue 6 , 2017

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Graphical Abstract:


Background: Cluster analysis techniques of gene expression microarray data is of increasing interest in the field of current bioinformatics. One of the reasons for this is the need for molecular-based refinement of broadly defined biological classes, with implications in cancer diagnosis, prognosis and treatment. And many algorithms have been developed for this problem.

Objective: However microarray data frequently include outliers, and how to treat these outlier's effects in the subsequent analysis-clustering.

Method: In this paper, we present the large-scale analysis of seven different agglomerative hierarchical clustering methods and five proximity measures for the analysis of 33 cancer gene expression datasets. As a case study, we used two experimental datasets: Affymetrix and cDNA, and different percent outliers were artificially added to these datasets.

Results: We found that ward method gives the highest corrected Rand index value with respect to the spearman proximity measures when datasets contain with and without outliers.

Conclusion: This study proves that ward method is more robust clustering methods in gene expression data analysis among other methods.

Keywords: Agglomerative hierarchical clustering, corrected rand index, microarray gene expressions data, outlier, proximity measures.

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Article Details

Year: 2017
Published on: 26 December, 2017
Page: [558 - 562]
Pages: 5
DOI: 10.2174/1574893611666160610103926
Price: $65

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