Comparative Analysis between Soft and Tools in Microarray Data Processing

Ouafae Kaissi, Ahmed Moussa, Brigitte Vannier, and Abdellatif Ghacham


Microarray Data Analysis, Gene Selection, Comparative Analysis.


In the analysis of experiments that involves the high density of oligonucleotide chips, it is important to generate list of genes or ‘targets’ from the genome wide data set that contains a lot of information. Gene selection is a process that seeks to identify the most significant genes which reveal large expressions changes between the baseline experiments and the conditions. Even though, several algorithms like T-test and other derived statistical algorithms were used for that selection process, the suitable Pvalue Cutoff remains difficult to choose. Therefore, one solution consists of using a False Discovery Rate (FDR) control. The Significance Analysis of Microarray (SAM) and the T-test Benjamini & Hochberg (BH) algorithms have been successfully used in such way. However, the reproductivity of results and their impact on the genes and/or experiments classification, while using different soft tools remain a subject of discussion.

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