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Identification of Robust Clustering Methods in Gene Expression Data Analysis

[ Vol. 12 , Issue. 6 ]

Author(s):

Md. Bipul Hossen* and Md. Siraj-Ud-Doulah   Pages 558 - 562 ( 5 )

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.

Affiliation:

Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur-5400, Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur-5400

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