Genes selection comparative study in microarray data analysis.
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.
LTI Laboratory, ENSA, Adbelmalek Essaadi University, Tangier, Morocco.
Ouafae
O
Kaissi
LabTIC Laboratory, ENSA, Abdelmalek Essaadi University, Tangier, Morocco.
Eric
E
Nimpaye
Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan, H.P, India.
Tiratha Raj
TR
Singh
Research Group 2RTC, University of Poitiers, France.
Brigitte
B
Vannier
Medical Biotechnology Laboratory, FMP, Mohammed V Suissi University, Rabat, Morocco.
Azeddine
A
Ibrahimi
LTI Laboratory, ENSA, Adbelmalek Essaadi University, Tangier, Morocco.
Abdellatif Amrani
AA
Ghacham
LabTIC Laboratory, ENSA, Abdelmalek Essaadi University, Tangier, Morocco.
Ahmed
A
Moussa
Bioinformatics Matlab Toolbox; Comparative Study; Gene selection; Microarray data; R/BioConductor