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A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

Marie-Agnès Dillies 1 Andrea Rau 2 Julie Aubert 3 Christelle Hennequet-Antier 4 Marine Jeanmougin 5 Nicolas Servant 6 Céline Keime 7 Guillemette Marot 8, 9 David Castel 10 Jordi Estellé 11 Grégory Guernec 12 Bernd Jagla 4 Luc Jouneau 13 Denis Laloë 2 Caroline Le Gall 14 Brigitte Schaeffer 13 Stéphane Le Crom 15 Mickaël Guedj 13 Florence Jaffrézic 2
1 PF2 - Puces à ADN (Plate-Forme 2)
2 GABI - Génétique Animale et Biologie Intégrative
3 MIA-Paris - Mathématiques et Informatique Appliquées
4 Centre Médical de l'Institut Pasteur
5 SG - Laboratoire Statistique et Génome
6 Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe
7 Institute of Genetics [Illkirch]
8 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
9 METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694
10 LEFG - Laboratoire d'Exploration Fonctionnelle des Génomes
11 CNAG - Centro Nacional de Análisi Genómico
12 ESIM - Epidémiologie des maladies infectieuses et modélisation
13 INRA
14 Methodomics
15 Plateforme Génomique de l'IBENS
IBENS - Institut de biologie de l'ENS Paris (UMR 8197/1024)
Abstract : During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.
Keywords : RNA-seq normalization differential analysis high-throughput sequencing
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Journal articles
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https://hal.inria.fr/hal-00782486
Contributor : Guillemette Marot <>
Submitted on : Tuesday, January 29, 2013 - 6:18:42 PM
Last modification on : Saturday, January 9, 2021 - 3:38:29 AM

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PASTEUR | CEA | UPMC | AGROPARISTECH | ENS-PARIS | CNRS | INRIA | INRA | PARISTECH | ENSMP_CBIO | ENSMP | CURIE | FNCLCC | INSTITUT-TELECOM | MIA-PARIS | UNIV-LILLE | SORBONNE-UNIVERSITE | UNIV-PARIS7 | USPC | INRAE | UNIV-PARIS | PSL | SU-TI

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Marie-Agnès Dillies, Andrea Rau, Julie Aubert, Christelle Hennequet-Antier, Marine Jeanmougin, et al.. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics, Oxford University Press (OUP), 2012, 14 (6), pp.671-683. ⟨10.1093/bib/bbs046⟩. ⟨hal-00782486⟩

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