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

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ID: 295344
2012
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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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openalex_W2165909549 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Marie‐Agnès Dillies, Andréa Rau, Julie Aubert, Christelle Hennequet‐Antier, Marine Jeanmougin, Nicolas Servant, Céline Keime, Guillemette Marot, David Castel, Jordi Estellé, Grégory Guernec, Bernd Jagla, Luc Jouneau, Dénis Laloë, Caroline Le Gall, Brigitte Schaeffer, Stéphane Le Crom, Mickaël Guedj, Florence Jaffrézic
Journal Briefings in bioinformatics
Year 2012
DOI
10.1093/bib/bbs046
URL
https://academic.oup.com/bib/article-pdf/14/6/671/467651/bbs046.pdf
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