RNA-seq Evaluating Several Custom Microarrays Background Correction and Gene Expression Data Normalization Systems | Chapter 09 | Advances and Trends in Biotechnology and Genetics Vol. 2
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.
View Volume: https://doi.org/10.9734/bpi/atbg/v2
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