## ----style-knitr, eval=TRUE, echo=FALSE, results="asis"------------------ BiocStyle::latex() ## ----prepareData,echo=T,cache=F------------------------------------------ library(RnaSeqSampleSize) ## ----singlePower,echo=TRUE,tidy=TRUE,cache=T----------------------------- example(est_power) ## ----singleSampleSize,each=TRUE,tidy=TRUE,cache=T------------------------ example(sample_size) ## ----showData,echo=F,cache=F--------------------------------------------- data(package="RnaSeqSampleSizeData")$results[,"Item"] ## ----distributionPower1,echo=TRUE,tidy=FALSE,cache=TRUE------------------ est_power_distribution(n=65,f=0.01,rho=2, distributionObject="TCGA_READ",repNumber=5) ## ----distributionPower2,echo=TRUE,tidy=FALSE,cache=TRUE------------------ #Power estimation based on some interested genes. #We use storeProcess=TRUE to return the details for all selected genes. selectedGenes<-names(TCGA_READ$pseudo.counts.mean)[c(1,3,5,7,9,12:30)] powerDistribution<-est_power_distribution(n=65,f=0.01,rho=2, distributionObject="TCGA_READ", selectedGenes=selectedGenes, storeProcess=TRUE) str(powerDistribution) mean(powerDistribution$power) ## ----distributionPower3,echo=TRUE,tidy=FALSE,cache=T--------------------- powerDistribution<-est_power_distribution(n=65,f=0.01,rho=2, distributionObject="TCGA_READ",pathway="00010", minAveCount=1,storeProcess=TRUE) mean(powerDistribution$power) ## ----distributionSampleSize,echo=TRUE,tidy=FALSE,cache=T----------------- sample_size_distribution(power=0.8,f=0.01,distributionObject="TCGA_READ", repNumber=5,showMessage=TRUE) ## ----generateUserData,echo=TRUE,tidy=TRUE,cache=T------------------------ #Generate a 10000*10 RNA-seq data as prior dataset set.seed(123) dataMatrix<-matrix(sample(0:3000,100000,replace=TRUE),nrow=10000,ncol=10) colnames(dataMatrix)<-c(paste0("Control",1:5),paste0("Treatment",1:5)) row.names(dataMatrix)<-paste0("gene",1:10000) head(dataMatrix) ## ----userDataSampleSize,echo=TRUE,tidy=FALSE,cache=TRUE------------------ #Estitamete the gene read count and dispersion distribution dataMatrixDistribution<-est_count_dispersion(dataMatrix, group=c(rep(0,5),rep(1,5))) #Power estimation by read count and dispersion distribution est_power_distribution(n=65,f=0.01,rho=2, distributionObject=dataMatrixDistribution,repNumber=5) ## ----singlePowerCurves,echo=TRUE,tidy=TRUE,cache=T----------------------- result1<-est_power_curve(n=63, f=0.01, rho=2, lambda0=5, phi0=0.5) result2<-est_power_curve(n=63, f=0.05, rho=2, lambda0=5, phi0=0.5) plot_power_curve(list(result1,result2)) ## ----optimazation,echo=TRUE,tidy=FALSE,cache=T--------------------------- result<-optimize_parameter(fun=est_power,opt1="n", opt2="lambda0",opt1Value=c(3,5,10,15,20), opt2Value=c(1:5,10,20))