Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions

Abstract RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalizat...

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Bibliographic Details
Published in:	Briefings in bioinformatics Vol. 19; no. 5; pp. 776 - 792
Main Authors:	Evans, Ciaran, Hardin, Johanna, Stoebel, Daniel M
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 28.09.2018 Oxford Publishing Limited (England)
Subjects:	Computational Biology - methods Computer Simulation Databases, Genetic - statistics & numerical data Gene expression Gene Expression Profiling - methods Gene Expression Profiling - statistics & numerical data gene expression regulation genes High-Throughput Nucleotide Sequencing - methods High-Throughput Nucleotide Sequencing - statistics & numerical data Humans Impact analysis Ribonucleic acid RNA RNA, Messenger - genetics Sampling methods sequence analysis Sequence Analysis, RNA - methods Sequence Analysis, RNA - statistics & numerical data transcriptome size differential expression RNA-Seq normalization assumptions spike-in control
ISSN:	1467-5463, 1477-4054, 1477-4054
Online Access:	Get full text
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Summary:	Abstract RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An underemphasized feature of normalization is the assumptions on which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this article, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1467-5463 1477-4054 1477-4054
DOI:	10.1093/bib/bbx008