Experimental Design and Power Calculation for RNA-seq Experiments

Zhijin Wu; Hao Wu

doi:10.1007/978-1-4939-3578-9_18

Experimental Design and Power Calculation for RNA-seq Experiments

Methods Mol Biol. 2016:1418:379-90. doi: 10.1007/978-1-4939-3578-9_18.

Authors

Zhijin Wu¹, Hao Wu²

Affiliations

¹ Department of Biostatistics, Brown University, Providence, RI, USA.
² Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.

PMID: 27008024
DOI: 10.1007/978-1-4939-3578-9_18

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.

Keywords: Experimental design; Gene expression; RNA-Seq; Sample size; Statistical power.

MeSH terms

Computational Biology / methods
Gene Expression
Gene Expression Profiling / methods
Genomics / methods
High-Throughput Nucleotide Sequencing* / methods
Research Design*
Sequence Analysis, RNA* / methods
Software*
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