RnaSeqSampleSize
To solve these issues, we developed a sample size estimation method based on the distributions of gene read counts and dispersions from real data. Datasets from the user's preliminary experiments or the Cancer Genome Atlas (TCGA) can be used as reference. The read counts and their related dispersions will be selected randomly from the reference based on their distributions, and from that, the power and sample size will be estimated and summarized.
#Running the following codes in your R
library(devtools)
install_github("slzhao/RnaSeqSampleSizeData")
install_github("slzhao/RnaSeqSampleSize")#Load package
library("RnaSeqSampleSize")
#View vignette
browseVignettes(package="RnaSeqSampleSize")
#View help files
?sample_size
#Examples for sample size or power estimation by single read count and dispersion
sample_size(power=0.8, f=0.01,rho=2, lambda0=5, phi0=0.5)
est_power(n=63, rho=2, lambda0=5, phi0=0.5,f=0.01)
#Examples for power estimation by prior real data, may use 3 minutes
est_power_distribution(n=65,f=0.01,rho=2,distributionObject="TCGA_READ",repNumber=5)
sample_size_distribution(power=0.918,f=0.01,rho=2,distributionObject="TCGA_READ",repNumber=5)
#Examples for power curve generation
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))