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Normalization of RNA-seq data using factor analysis of control genes or samples

Nature Biotechnology volume 32, pages 896–902 (2014)Cite this article

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Abstract

Normalization of RNA-sequencing (RNA-seq) data has proven essential to ensure accurate inference of expression levels. Here, we show that usual normalization approaches mostly account for sequencing depth and fail to correct for library preparation and other more complex unwanted technical effects. We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and investigate the possibility of using them directly for normalization. We show that the spike-ins are not reliable enough to be used in standard global-scaling or regression-based normalization procedures. We propose a normalization strategy, called remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes (e.g., ERCC spike-ins) or samples (e.g., replicate libraries). Our approach leads to more accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods. In particular, RUV promises to be valuable for large collaborative projects involving multiple laboratories, technicians, and/or sequencing platforms.

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Figure 1: Unwanted variation in the SEQC RNA-seq data set.
Figure 2: Unwanted variation in the zebrafish RNA-seq data set.
Figure 3: RUVg normalization using in silico empirical control genes.
Figure 4: Behavior of the ERCC spike-in controls.
Figure 5: Using the ERCC spike-in controls for normalization, zebrafish data set.
Figure 6: Impact of normalization on differential expression analysis.

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Acknowledgements

We thank the SEQC Consortium for granting us early access to the SEQC pilot data, L. Jacob for his help with the RUV methodology and its software implementation, and J. Gagnon-Bartsch, J. Choi, and W. Shi for helpful discussions. J.N. was supported by a grant from the National Institute on Deafness and Other Communication Disorders. T.P.S. was supported by a National Health and Medical Research Council (NHMRC) Australia Fellowship.

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Authors and Affiliations

  1. Department of Statistics, University of California, Berkeley, Berkeley, California, USA

    Davide Risso, Terence P Speed & Sandrine Dudoit

  2. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, USA

    John Ngai

  3. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA

    John Ngai

  4. Functional Genomics Laboratory, University of California, Berkeley, Berkeley, California, USA

    John Ngai

  5. Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Terence P Speed

  6. Department of Mathematics and Statistics, The University of Melbourne, Victoria, Australia

    Terence P Speed

  7. Division of Biostatistics, University of California, Berkeley, Berkeley, California, USA

    Sandrine Dudoit

Authors
  1. Davide Risso
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  4. Sandrine Dudoit
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Contributions

D.R., S.D. and T.P.S. developed the statistical methods; D.R. and S.D. analyzed the data; J.N. designed the zebrafish experiment; D.R. and S.D. wrote the manuscript; all authors read and approved the manuscript.

Corresponding authors

Correspondence to Davide Risso or Sandrine Dudoit.

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The authors declare no competing financial interests.

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Supplementary Figures 1–20 and Supplementary Table 1 (PDF 3382 kb)

Supplementary Software

RUVSeq_0.1.1.tar.gz (ZIP 135 kb)

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Risso, D., Ngai, J., Speed, T. et al. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol 32, 896–902 (2014). https://doi.org/10.1038/nbt.2931

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