Genes Selection Comparative Study in Microarray Data Analysis

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Title	Genes Selection Comparative Study in Microarray Data Analysis
Authors	Ouafae Kaissi¹, Eric Nimpaye², Tiratha Raj Singh³, Brigitte Vannier⁴, Azeddine Ibrahimi⁵, Abdellatif Amrani Ghacham¹ & Ahmed Moussa²*
Affiliation	¹LTI Laboratory, ENSA, Adbelmalek Essaadi University, Tangier, Morocco; ²LabTIC Laboratory, ENSA, Abdelmalek Essaadi University, Tangier, Morocco; ³Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan, H.P, India; ⁴Research Group 2RTC, University of Poitiers, France; ⁵Medical Biotechnology Laboratory, FMP, Mohammed V Suissi University, Rabat, Morocco
Email	amoussa@uae.ac.ma; *Corresponding author
Article Type	Hypothesis
Date	Received December 10, 2013; Accepted December 16, 2013; Published December 27, 2013
Abstract	In response to the rapid development of DNA Microarray Technologies, many differentially expressed genes selection algorithms have been developed, and different comparison studies of these algorithms have been done. However, it is not clear how these methods compare with each other, especially when we used different developments tools. Here, we considered three commonly used differentially expressed genes selection approaches, namely: Fold Change, T-test and SAM, using Bioinformatics Matlab Toolbox and R/BioConductor. We used two datasets, issued from the affymetrix technology, to present results of used methods and software’s in gene selection process. The results, in terms of sensitivity and specificity, indicate that the behavior of SAM is better compared to Fold Change and T-test using R/BioConductor. While, no practical differences were observed between the three gene selection methods when using Bioinformatics Matlab Toolbox. In face of our result, the ROC curve shows that: on the one hand R/BioConductor using SAM is favored for microarray selection compared to the other methods. And, on the other hand, results of the three studied gene selection methods using Bioinformatics Matlab Toolbox are still comparable for the two datasets used.
Keywords	Microarray data, Gene selection, R/BioConductor, Bioinformatics Matlab Toolbox, Comparative Study.
Citation	Kaissi et al. Bioinformation 9(20): 1019-1022 (2013)
Edited by	P Kangueane
ISSN	0973-2063
Publisher	Biomedical Informatics
License	This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.