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Statistical Analysis of Next Generation Sequencing Data pp 169–190Cite as

The Role of Spike-In Standards in the Normalization of RNA-seq

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Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

Normalization of RNA-seq data is essential to ensure accurate inference of expression levels, by adjusting for sequencing depth and other more complex nuisance effects, both within and between samples. Recently, the External RNA Control Consortium (ERCC) developed a set of 92 synthetic spike-in standards that are commercially available and relatively easy to add to a typical library preparation. In this chapter, we compare the performance of several state-of-the-art normalization methods, including adaptations that directly use spike-in sequences as controls. We show that although the ERCC spike-ins could in principle be valuable for assessing accuracy in RNA-seq experiments, their read counts are not stable enough to be used for normalization purposes. We propose a novel approach to normalization that can successfully make use of control sequences to remove unwanted effects and lead to accurate estimation of expression fold-changes and tests of differential expression.

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Notes

  1. 1.

    Throughout this chapter, we shall use the term sample to refer to an observational unit of interest, i.e., a set of reads from a given lane for a particular library. Thus, as indicated in Fig. 9.1b, there are 128 samples in total for the SEQC dataset, 64 of the reference Sample A type and 64 of the reference Sample B type.

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Acknowledgements

We thank Leming Shi for providing the SEQC pilot data and Laurent Jacob for his help with the software implementation of the RUV method.

Author information

Authors and Affiliations

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

    Davide Risso & Terence P. Speed

  2. Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, and Functional Genomics Laboratory, University of California, Berkeley, CA, USA

    John Ngai

  3. Bioinformatics Division, Walter and Eliza Hall Institute, Melbourne, VIC, Australia

    Terence P. Speed

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

    Terence P. Speed

  5. Division of Biostatistics and Department of Statistics, University of California, Berkeley, CA, USA

    Sandrine Dudoit

Authors
  1. Davide Risso
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  2. John Ngai
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  3. Terence P. Speed
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  4. Sandrine Dudoit
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Corresponding author

Correspondence to Davide Risso .

Editor information

Editors and Affiliations

  1. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky, USA

    Somnath Datta

  2. Department of Statistics, Iowa State University, Ames, Iowa, USA

    Dan Nettleton

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Risso, D., Ngai, J., Speed, T.P., Dudoit, S. (2014). The Role of Spike-In Standards in the Normalization of RNA-seq. In: Datta, S., Nettleton, D. (eds) Statistical Analysis of Next Generation Sequencing Data. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-07212-8_9

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