Abstract
Power calculation is a critical component of RNA-seq experimental design. The flexibility of RNA-seq experiment and the wide dynamic range of transcription it measures make it an attractive technology for whole transcriptome analysis. These features, in addition to the high dimensionality of RNA-seq data, bring complexity in experimental design, making an analytical power calculation no longer realistic. In this chapter we review the major factors that influence the statistical power of detecting differential expression, and give examples of power assessment using the R package PROPER.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsSpringer Nature is developing a new tool to find and evaluate Protocols. Learn more
Notes
- 1.
ExpressionSet is a basic class of object used in Bioconductor. See http://www.bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf for more details
References
Hansen KD, Wu Z, Irizarry RA, Leek JT (2011) Sequencing technology does not eliminate biological variability. Nat Biotechnol 29(7):572–573
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80
Wu H, Wang C, Wu Z (2013) A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics 14(2):232–243
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140
McCarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40(10):4288–4297
Fang Z, Cui X (2011) Design and validation issues in RNA-seq experiments. Brief Bioinform 12(3):280–287
Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher J-P (2013) Calculating sample size estimates for RNA sequencing data. J Comput Biol 20(12):970–978
Li C-I, Su P-F, Shyr Y (2013) Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data. BMC Bioinformatics 14(1):357
Li C-I, Su P-F, Guo Y, Shyr Y (2013) Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution. Int J Comput Biol Drug Des 6(4):358–375
Frazee AC, Langmead B, Leek JT (2011) ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics 12(1):449
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Wu, Z., Wu, H. (2016). Experimental Design and Power Calculation for RNA-seq Experiments. In: Mathé, E., Davis, S. (eds) Statistical Genomics. Methods in Molecular Biology, vol 1418. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3578-9_18
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3578-9_18
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3576-5
Online ISBN: 978-1-4939-3578-9
eBook Packages: Springer Protocols