Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions
By Ciaran Evans, Johanna Hardin, Daniel M. Stoebel. Briefings Bioinform., 2016
297 citations recorded by the source corpus.
&NA; 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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/0b2e9ba6cb4ac337c796f4dbf451fe04aa337334
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In Molecular Biology
Citations in the corpus
- zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, unknown): “Downsampling scRNA-seq library sizes can vary by orders ofmagnitude, which complicates normalization [28, 29].”
- Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols (2020, methodology): “However, after calculating the read counts, data normalization is essential to ensure accurate inference of gene expressions (Dillies et al. 2013; Li et al. 2015; Evans et al. 2018).”