This site needs JavaScript to work properly. Please enable it to take advantage of the complete set of features!
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log in
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
doi: 10.1186/gb-2010年11月3日-r25. Epub 2010 Mar 2.

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

Affiliations

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

Mark D Robinson et al. Genome Biol. 2010.

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.

PubMed Disclaimer

Figures

Figure 1
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 2
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 3
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.

References

    1. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. Alternative isoform regulation in human tissue transcriptomes. Nature. 2008;456:470–476. doi: 10.1038/nature07509. - DOI - PMC - PubMed
    1. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O'Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo ML. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science. 2008;321:956–960. doi: 10.1126/science.1160342. - DOI - PubMed
    1. Wang X, Sun Q, McGrath SD, Mardis ER, Soloway PD, Clark AG. Transcriptome-wide identification of novel imprinted genes in neonatal mouse brain. PLoS One. 2008;3:e3839. doi: 10.1371/journal.pone.0003839. - DOI - PMC - PubMed
    1. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19:185–193. doi: 10.1093/bioinformatics/19.2.185. - DOI - PubMed
    1. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63. doi: 10.1038/nrg2484. - DOI - PMC - PubMed

Publication types

Full text links
BioMed Central full text link BioMed Central Free PMC article
Cite

AltStyle によって変換されたページ (->オリジナル) /