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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.