Experimental Design and Power Calculation for RNA-seq Experiments

(RNA-seq) has become a routine technique in transcriptome analysis, where identifying differential expression (DE) remains a major task. As expression is a stochastic process in nature, the technological improvement in RNA-seq cannot bypass the presence of biological variability. It has been well recognized that replication is still necessary in making reliable statistical inference of DE. The determination, or choice, of the number of replicates becomes a natural question in experimental design.

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.

Classical power calculation that deals with a single hypothesis takes a few simple assumptions. These include:

  1.  the effect size, representing the minimum difference that is scientifically meaningful between groups in comparison;
  2. within-group variation, representing natural variation in observations regardless of between-group difference;
  3. an acceptable type I error rate, usually in the form of p-value; and
  4. the sample size.

With these values set, one can calculate statistical power, the probability of rejecting the null hypothesis when the effect is as large as assumed. If a certain power level is desired, one can also do a reverse calculation to determine the minimum sample size to achieve the desired power while controlling the type I error rate.

In DE analysis for RNA-seq experiments, we consider similar factors with more complexity since it is a high throughput experiment querying all transcripts simultaneously, and these transcripts are not exchangeable (read more…)

Comprehensive visualization of stratified power, generated by the function plotAll


Availability – PROspective Power Evaluation for RNAseq (PROPER) is avialable at: https://www.bioconductor.org/packages/release/bioc/html/PROPER.html

Wu Z, Wu H (2016) Experimental Design and Power Calculation for RNA-seq Experiments. Methods Mol Biol 1418:379-90. [abstract]

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