Oligonucleotide microarrays data pre-processing procedures impacting gene expression differential survey performances were fully evoked. RNA-Seq tool exhibited high performances (sensitivity) as opposed to microarrays in transcriptomic as well as genomic studies. The aim of this study is to evaluate microarrays data pre-processing dynamism on gene expression differential analysis outcomes, assuming RNA-Seq approach as reference. For this purpose, significantly differentially expressed genes (DEGs) candidate by processing two Vitis vinifera development stages (veraison and repining), from previous comparative transcriptomic analysis, between RNA-Seq and our own developed custom microarrays designs submitted to 20 different data pre-processing procedures combination schemes in terms of expressed genes signal normalization (DN) and background subtraction (BS) functions developed in R limma package, were structured in nine (9) blocks, depending on microarrays DN+BS and as well BS+DN arrangements, and considered for multivariate statistical analysis. In total, 17,446 genes were common across all microarrays by processing the above mentioned V. vinifera differential analysis and were detected for the subsequent survey. Findings, although recognizing data pre-processing practices as a necessary step for improving microarrays performances suggested background correction procedure (BS+DN) as promoting DEGs data variability by contrast to genes signal normalization pattern (DN+BS). Also, results revealed DN+BS microarray data pre-processing procedure as enhancing oligonucleotide microarrays positive predictive value as well as sensitivity performances. In conclusion, the present survey highlighted the strong impact of microarray data pre-processing procedures (BS+DN and/or DN+BS) on gene expression differential analysis outcome and as well confirmed RNA-Seq as an acceptable approach in assessing oligonucleotide microarray performances in transcriptomic surveys.
Key words: Microarrays, RNA-Seq, Background subtraction (BS), expressed genes signal normalization (DN), Differential analysis, Vitis vinifera.
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