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

  • Davide RissoEmail author
  • John Ngai
  • Terence P. Speed
  • Sandrine Dudoit
Part of the Frontiers in Probability and the Statistical Sciences book series (FROPROSTAS)


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.


Read Count Empirical Cumulative Distribution Function Unwanted Variation Loess Normalization Negative Control Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Davide Risso
    • 1
    Email author
  • John Ngai
    • 2
  • Terence P. Speed
    • 1
    • 3
    • 4
  • Sandrine Dudoit
    • 5
  1. 1.Department of StatisticsUniversity of CaliforniaBerkeleyUSA
  2. 2.Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, and Functional Genomics LaboratoryUniversity of CaliforniaBerkeleyUSA
  3. 3.Bioinformatics DivisionWalter and Eliza Hall InstituteMelbourneAustralia
  4. 4.Department of Mathematics and StatisticsThe University of MelbourneVictoriaAustralia
  5. 5.Division of Biostatistics and Department of StatisticsUniversity of CaliforniaBerkeleyUSA

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