## ----knitr, echo=FALSE, results="hide"----------------------------------- library("knitr") opts_chunk$set( message=FALSE) ## ----loadpackage, echo=FALSE--------------------------------------------- library(Biobase) library(edgeR) ## ------------------------------------------------------------------------ library(ssizeRNA) ## ----sim1_size, fig.height=4.5, fig.width=6------------------------------ set.seed(2016) size1 <- ssizeRNA_single(nGenes = 10000, pi0 = 0.8, m = 200, mu = 10, disp = 0.1, fc = 2, fdr = 0.05, power = 0.8, maxN = 20) size1$ssize ## ----sim1_power---------------------------------------------------------- check.power(m = 14, mu = 10, disp = 0.1, fc = 2, sims = 10) ## ----sim2_para----------------------------------------------------------- data(hammer.eset) counts <- exprs(hammer.eset)[, phenoData(hammer.eset)$Time == "2 weeks"] counts <- counts[rowSums(counts) > 0,] ## filter zero count genes trt <- hammer.eset$protocol[which(hammer.eset$Time == "2 weeks")] ## average read count in control group for each gene mu <- apply(counts[, trt == "control"], 1, mean) ## dispersion for each gene d <- DGEList(counts) d <- calcNormFactors(d) d <- estimateCommonDisp(d) d <- estimateTagwiseDisp(d) disp <- d$tagwise.dispersion ## ----sim2_size, fig.height=4.5, fig.width=6------------------------------ set.seed(2016) size2 <- ssizeRNA_vary(nGenes = 10000, pi0 = 0.8, m = 200, mu = mu, disp = disp, fc = 2, fdr = 0.05, power = 0.8, maxN = 15, replace = FALSE) size2$ssize ## ----sim3_size, fig.height=4.5, fig.width=6------------------------------ set.seed(2016) fc <- function(x){exp(rnorm(x, log(2), 0.5*log(2)))} size3 <- ssizeRNA_vary(nGenes = 10000, pi0 = 0.8, m = 200, mu = mu, disp = disp, fc = fc, fdr = 0.05, power = 0.8, maxN = 20, replace = FALSE) size3$ssize ## ----sim3_power---------------------------------------------------------- check.power(m = 14, mu = mu, disp = disp, fc = fc, sims = 10, replace = FALSE) ## ----echo=TRUE, result=TRUE---------------------------------------------- sessionInfo()