The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
A scaling normalization method for differential expression analysis of RNA-seq data
Open accessAbstract
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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![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.](/paper-figures/6150d0d1547c4f7c9fd279ea42d71474/a-scaling-normalization-method-for-differential-expression-analysis-of-rna-seq-figure-1.jpg)
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“Alternatively, voom has the ability to respect normalization factors computed outside of limma by methods such as trimmed mean of M-values (41) or conditional quantile normalization (42).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [41]
In the citing paper
“Normalization methods that take this into account are TMM [47], DESeq [48], PoissonSeq [49] and UpperQuartile [45], which ignore highly variable and/or highly expressed features.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [47]
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Cited by
Sorted by citations
limma powers differential expression analyses for RNA-sequencing and microarray studies
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Self-citationvoom: precision weights unlock linear model analysis tools for RNA-seq read counts
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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
- PMC
- PMC2864565
- 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
- gold
- doi.org
- 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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