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.
Description
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. - PubMed - NCBI
%0 Journal Article
%1 Dillies:2013:Brief-Bioinform:22988256
%A Dillies, M A
%A Rau, A
%A Aubert, J
%A Hennequet-Antier, C
%A Jeanmougin, M
%A Servant, N
%A Keime, C
%A Marot, G
%A Castel, D
%A Estelle, J
%A Guernec, G
%A Jagla, B
%A Jouneau, L
%A Laloë, D
%A Le Gall, C
%A Schaëffer, B
%A Le Crom, S
%A Guedj, M
%A Jaffrézic, F
%A French StatOmique Consortium,
%D 2013
%J Brief Bioinform
%K SHOULDREAD fulltext methods normalization rna-seq
%N 6
%P 671-683
%R 10.1093/bib/bbs046
%T A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
%U https://www.ncbi.nlm.nih.gov/pubmed/22988256
%V 14
%X 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.
@article{Dillies:2013:Brief-Bioinform:22988256,
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. },
added-at = {2018-10-10T17:36:36.000+0200},
author = {Dillies, M A and Rau, A and Aubert, J and Hennequet-Antier, C and Jeanmougin, M and Servant, N and Keime, C and Marot, G and Castel, D and Estelle, J and Guernec, G and Jagla, B and Jouneau, L and Lalo{\"e}, D and Le Gall, C and Scha{\"e}ffer, B and Le Crom, S and Guedj, M and Jaffr{\'e}zic, F and {French StatOmique Consortium}},
biburl = {https://www.bibsonomy.org/bibtex/2937cb9766999f1ff93bc9be5bb95be4b/marcsaric},
description = {A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. - PubMed - NCBI},
doi = {10.1093/bib/bbs046},
interhash = {141ee0ff5a631dd4c827b1127b39c659},
intrahash = {937cb9766999f1ff93bc9be5bb95be4b},
journal = {Brief Bioinform},
keywords = {SHOULDREAD fulltext methods normalization rna-seq},
month = nov,
number = 6,
pages = {671-683},
pmid = {22988256},
timestamp = {2018-10-10T17:36:36.000+0200},
title = {A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis},
url = {https://www.ncbi.nlm.nih.gov/pubmed/22988256},
volume = 14,
year = 2013
}