ssizeRNA: Sample Size Calculation for RNA-Seq Experimental Design

Description Usage Arguments Value Author(s) References Examples

For the limma/voom RNAseq analysis pipeline, when we control false discovery rate by using the Benjamini and Hochberg step-up procedure (1995) and/or Storey and Tibshirani's q-value procedure (Storey et al, 2004), check.power calculates average power and true FDR for given sample size, user-specified proportions of non-differentially expressed genes, number of iterations, FDR level to control, mean counts in control group, dispersion, and fold change.

1 2	check.power(nGenes = 10000, pi0 = 0.8, m, mu, disp, fc, up = 0.5, replace = TRUE, fdr = 0.05, sims = 100)

`nGenes`	total number of genes, the default value is `10000`.
`pi0`	proportion of non-differentially expressed genes, the default value is `0.8`.
`m`	sample size per treatment group.
`mu`	a vector (or scalar) of mean counts in control group from which to simulate.
`disp`	a vector (or scalar) of dispersion parameter from which to simulate.
`fc`	a vector (or scalar, or a function that takes an integer n and generates a vector of length n) of fold change for differentially expressed (DE) genes.
`up`	proportion of up-regulated genes among all DE genes, the default value is `0.5`.
`replace`	sample with or without replacement from given parameters. See Details for more information.
`fdr`	the false discovery rate to be controlled.
`sims`	number of simulations to run when computing power and FDR.

`pow_bh_ave`	average power when controlling FDR by Benjamini and Hochberg (1995) method.
`fdr_bh_ave`	true false discovery rate when controlling FDR by Benjamini and Hochberg (1995) method.
`pow_bh_ave`	average power when controlling FDR by q-value procedure (Storey et al., 2004).
`fdr_bh_ave`	true false discovery rate when controlling FDR by q-value procedure (Storey et al., 2004).

Ran Bi biran@iastate.edu, Peng Liu pliu@iastate.edu

Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B, 57, 289-300.

Storey, J. D., Taylor, J. E. and Siegmund, D. (2004) Strong control, conservative point estimation and simultaneous rates: a unified approach. J. R. Stat. Soc. B, 66, 187- 205.

library(limma)
library(qvalue)
m <- 14                      ## sample size per treatment group
mu <- 10                     ## mean read counts in control group
disp <- 0.1                  ## dispersion for all genes
fc <- 2                      ## 2-fold change for DE genes

check.power(m = m, mu = mu, disp = disp, fc = fc, sims = 2)