#data normalisation (dn)

Microarray gene expression technologies represents a widely used tool in transcriptomics and genomics studies worldwide. This technology is stable with the purpose of gene expression differential analysis because of their well-established biostatistics and bioinformatics analysis schemes. However, microarray reliability with regard that analysis typology, depend on probe specificity as well as applied data normalisation and/or background correction procedures. Then, we assessed the performance of 20 different microarrays background correction / gene expression data normalisation combination procedures from “linear models for microarray and RNA-Seq data analysis” package (limma), by comparing significantly differentially expressed genes detected by several custom microarray design strategies, depending on microarray probe size as well as probe set number per transcript model by assuming RNA-Seq approach as benchmark. Basing exclusively on a multivariate statistical clustering surveys, in R programing environment, we showed the pre-eminence of data normalisation (DN) as opposed to noise background correction/subtraction (BS) in microarray expression analysis. Although the combination between (i) gene expression data normalization and (ii) background subtraction procedures (BS+DN), improves the agreement between heterogenic microarray platforms as well as RNA-Seq platform in calling significantly modulated genes, quantile normalisation system combined with all processed background correction procedures has been discriminated as exhibiting highest sensitivity with RNA-Seq (p < 0.05). In conclusion we showed the pre-eminence of microarray data pre-processing step in gene expression differential analysis by according a priority to data normalisation procedure especially to quantile normalisation system contributing in stabilizing gene expression differential analysis results with regard heterogenic custom microarray design strategies (heterogenic microarray platforms).

Author(s) Details

Noel Dougba Dago, Msc., Ph.D Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Département de Biochimie-Génétique, Université Peleforo Gon Coulibaly BP1328 Korhogo, Côte d’Ivoire. Laboratory of Functional Genomic, Department of Biotechnology, University of Verona, Strada Le Grazie 15 CàVignal 1, 37134, Verona, Italy.

Dr. Martial Didier Yao Saraka Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Département de Biochimie-Génétique, Université Peleforo Gon Coulibaly BP1328 Korhogo, Côte d’Ivoire.

Dr. Nafan Diarrassouba Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Département de Biochimie-Génétique, Université Peleforo Gon Coulibaly BP1328 Korhogo, Côte d’Ivoire.

Antonio Mori Department of Neurological, Biomedical and Movement Sciences, University of Verona, Strada Le Grazie 8, 37134, Verona, Italy.

Dr. Hermann-Désiré Lallié Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Département de Biochimie-Génétique, Université Peleforo Gon Coulibaly BP1328 Korhogo, Côte d’Ivoire.

Prof. Professor Lamine Baba-Moussa Laboratoire de Biologie et de Typage Moléculaire en Microbiologie, Faculté des Sciences et Techniques, Université d’Abomey-Calavi, Cotonou, Benin.

Dr. Massimo Delledonne Laboratory of Functional Genomic, Department of Biotechnology, University of Verona, Strada Le Grazie 15 CàVignal 1, 37134, Verona, Italy.

Giovanni Malerba Department of Neurological, Biomedical and Movement Sciences, University of Verona, Strada Le Grazie 8, 37134, Verona, Italy.

Edouard Kouamé N’Goran Unité de Formation et de Recherche (UFR) des Sciences Biologiques, Département de Biochimie-Génétique, Université Peleforo Gon Coulibaly BP1328 Korhogo, Côte d’Ivoire.

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