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

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Dillies, M.-A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., Guernec, G., Jagla, B., Jouneau, L., Laloe, D., Le Gall, C., Schaeffer, B., Le Crom, S., Guedj, M., Jaffrezic, F., on behalf of The French StatOmique Consortium·2013·Briefings in Bioinformatics

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.

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Metadata

Authors
Dillies, M.-A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., Guernec, G., Jagla, B., Jouneau, L., Laloe, D., Le Gall, C., Schaeffer, B., Le Crom, S., Guedj, M., Jaffrezic, F., & on behalf of The French StatOmique Consortium
Venue
Briefings in Bioinformatics
Published
2012-09-17
Volume
14
Issue
6
Pages
671-683
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
OpenAlex
W2165909549
Cited by
1,298
References
20
FWCI
30.44
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. https://app.cochat.ai/papers/10.1093/bib/bbs046
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Dillies, M.-A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., Guernec, G., Jagla, B., Jouneau, L., Laloe, D., Le Gall, C., Schaeffer, B., Le Crom, S., Guedj, M., Jaffrezic, F., & on behalf of The French StatOmique Consortium. (2013). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbs046

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