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

By Marie-Agnès Dillies, A. Rau, J. Aubert, C. Hennequet-Antier, M. Jeanmougin, N. Servant, C. Keime, G. Marot, D. Castel, J. Estellé and 9 more. Briefings Bioinform., 2013

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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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b695cb5a9718ca530aba8b2a2174657d4996d4e8

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Citations in the corpus

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, background): “…with fewer than five replicates per condition (Marioni et al. 2008; Bullard et al. 2010; Kvam et al. 2012; Li et al. 2012; Busby et al. 2013; Dillies et al. 2013; Rapaport et al. 2013; SEQC/MAQC-III Consortium 2014; Soneson 2014), limiting their ability to compare the performance of DGE…”
Normalization and microbial differential abundance strategies depend upon data characteristics (2017, background): “For DESeq/DESeq2, poor performance may be due to the model’s assumption that differentially abundant OTUs are not a large portion of the population [29] or the model’s overdispersion estimates [4].”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “DESeq’s medianofratio and edgeR’s trimmed mean of Mvalues (TMM, where Mvalues denote empirical fold changes between two samples) method are the most popular normalization approaches [119] .”
Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols (2020, methodology): “RNA-seq normalization plays a crucial role to ensure the validity of gene counts for downstream differential analysis (Dillies et al. 2013; Costa-Silva et al. 2017).”
SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data (2015, methodology): “• normalization: this section uses the default normalization associated with each package, as they have been shown to perform equally well [11].”
Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions (2016, methodology): “As shown in Table 1, in many comparisons Total Count and RPKM/FPKM perform worse than all other methods, and several authors expressly recommend against its use [9].”
Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data (2015, background): “For DESeq/DESeq2, poor 580 performance may be due to the model’s assumption that differentially abundant OTUs are not a 581 large portion of the population (Dillies et al. 2013), or the model’s overdispersion estimates 582 (Paulson et al. 2013).”