Cited by 7,876Gene expression and cancer classificationGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

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A scaling normalization method for differential expression analysis of RNA-seq data

Open access
Robinson, Mark D, Oshlack, Alicia·2010·Genome Biology

Abstract

The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.

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Registry

Crossref · 10.1186/gb-2010-11-3-r25

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25 of 25 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 Normalization is required for RNA-seq data. Data from [6] comparing log ratios of (a) technical replicates and (b) liver versus kidney expression levels, after adjusting for the total number of reads in each sample. The green line shows the smoothed distribution of log-fold-changes of the housekeeping genes. (c) An M versus A plot comparing liver and kidney shows a clear offset from zero. Green points indicate 545 housekeeping genes, while the green line signifies the median log-ratio of the housekeeping genes. The red line shows the estimated TMM normalization factor. The smear of orange points highlights the genes that were observed in only one of the liver or kidney tissues. The black arrow highlights the set of prominent genes that are largely attributable for the overall bias in log-fold-changes.
Figure 1 Normalization is required for RNA-seq data. Data from [6] comparing log ratios of (a) technical replicates and (b) liver versus kidney expression levels, after adjusting for the total number of reads in each sample. The green line shows the smoothed distribution of log-fold-changes of the housekeeping genes. (c) An M versus A plot comparing liver and kidney shows a clear offset from zero. Green points indicate 545 housekeeping genes, while the green line signifies the median log-ratio of the housekeeping genes. The red line shows the estimated TMM normalization factor. The smear of orange points highlights the genes that were observed in only one of the liver or kidney tissues. The black arrow highlights the set of prominent genes that are largely attributable for the overall bias in log-fold-changes.CC BY · PubMed Central
Figure 2 Simulations show TMM normalization is robust and outperforms library size normalization. (a) An example of the simulation results showing the need for normalization due to genes expressed uniquely in one sample (orange dots) and asymmetric DE (blue dots). (b) A lower false positive rate is observed using TMM normalization compared with standard normalization.
Figure 2 Simulations show TMM normalization is robust and outperforms library size normalization. (a) An example of the simulation results showing the need for normalization due to genes expressed uniquely in one sample (orange dots) and asymmetric DE (blue dots). (b) A lower false positive rate is observed using TMM normalization compared with standard normalization.CC BY · PubMed Central
Figure 3 False discovery plots comparing several published methods. The red line depicts the length-normalized moderated t-statistic analysis. The solid and dashed lines show the library size normalized and TMM normalized Poisson model analysis, respectively. The blue and black lines represent the LR test and exact test, respectively. It can be seen that the use of TMM normalization results in a much lower false discovery rate.
Figure 3 False discovery plots comparing several published methods. The red line depicts the length-normalized moderated t-statistic analysis. The solid and dashed lines show the library size normalized and TMM normalized Poisson model analysis, respectively. The blue and black lines represent the LR test and exact test, respectively. It can be seen that the use of TMM normalization results in a much lower false discovery rate.CC BY · PubMed Central

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Citations

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Research works printed but not linked: 3

  1. Bullard JH, Purdom EA, Hansen KD, Durinck S, Dudoit S: Statistical inference in mRNA-Seq: exploratory data analysis and differential expression. UC Berkeley Division of Biostatistics Working Paper Series. 2009, paper 247
  2. Casella G, Berger RL: Statistical Inference. 2002, Pacific Grove, CA: Duxbury Press
  3. Team RDC: R: A Language and Environment for Statistical Computing. 2009

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Metadata

Authors
Robinson, M. D., & Oshlack, A.
Affiliations
  • Garvan Institute of Medical Research (AU)
  • Walter and Eliza Hall Institute of Medical Research (AU)
Corresponding author
  • Robinson, Mark D
  • Oshlack, Alicia
Venue
Genome Biology
Published
2010-03-02
Volume
11
Issue
3
Pages
R25-R25
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
OpenAlex
W2107018762
Cited by
7,876
References
25
Citations by year
2026: 452 · 2025: 604 · 2024: 832 · 2023: 700 · 2022: 773 · 2021: 1010 · 2020: 730 · 2019: 610 · 2018: 540 · 2017: 440 · 2016: 348 · 2015: 285 · 2014: 222 · 2013: 153 · 2012: 100
FWCI
41.77
Type
journal article
Language
en
Open access
Locations
  • Genome biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders
  • Medical Research Council
  • National Health and Medical Research Council (490037)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
RNA-seq, normalization, differential gene expression, transcriptome, gene expression, read count normalization
MeSH terms
Base Sequence, Computer Simulation, RNA, Models, Statistical, Gene Library, Gene Expression Profiling

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