NDLI: Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments

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Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments

Content Provider	Taylor & Francis Online
Author	Maza, Elie Frasse, Pierre Bouzayen, Mondher Zouine, Mohamed Senin, Pavel
Abstract	In recent years, RNA-Seq technologies became a powerful tool for transcriptome studies. However, computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. In particular, it is known that the choice of a normalization procedure leads to a great variability in results of differential gene expression analysis. The present study compares the most widespread normalization procedures and proposes a novel one aiming at removing an inherent bias of studied transcriptomes related to their relative size. Comparisons of the normalization procedures are performed on real and simulated data sets. Real RNA-Seq data sets analyses, performed with all the different normalization methods, show that only 50% of significantly differentially expressed genes are common. This result highlights the influence of the normalization step on the differential expression analysis. Real and simulated data sets analyses give similar results showing 3 different groups of procedures having the same behavior. The group including the novel method named “Median Ratio Normalization” (MRN) gives the lower number of false discoveries. Within this group the MRN method is less sensitive to the modification of parameters related to the relative size of transcriptomes such as the number of down- and upregulated genes and the gene expression levels. The newly proposed MRN method efficiently deals with intrinsic bias resulting from relative size of studied transcriptomes. Validation with real and simulated data sets confirmed that MRN is more consistent and robust than existing methods.
File Format	PDF HTM / HTML
ISSN	19420889
DOI	10.4161/cib.25849
Journal	Communicative & Integrative Biology
Volume Number	6
Issue Number	6
Language	English
Publisher	Taylor & Francis
Publisher Date	2013-07-30
Access Restriction	Open
Subject Keyword	Computational methods Differential gene expression analysis High throughput sequencing Normalization methods Relative transcriptome size RNA-Seq data
Content Type	Text
Subject	Agricultural and Biological Sciences

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