predictPower {PowerExplorer} | R Documentation |
Simlilar to estimatePower
,
power estimations are performed under multiple increasing sample sizes
predictPower(inputObject, groupVec, isLogTransformed = FALSE, dataType = c("RNASeq", "Proteomics"), enableROTS = FALSE, paraROTS = list(B = 1000, K = NULL, paired = FALSE, a1 = NULL, a2 = NULL, progress = FALSE), minLFC = 0.5, rangeSimNumRep = NA, alpha = 0.05, ST = 100, seed = 123, parallel = FALSE, BPPARAM = bpparam(), showProcess = FALSE, saveResultData = FALSE)
inputObject |
a numeric raw Proteomics abundance data matrix, in which rows correspond to proteins and columns correspond to samples. |
groupVec |
a vector indicating the grouping of samples |
isLogTransformed |
logical; logical; set to |
dataType |
"RNASeq" or "Proteomics" indictes the data type of the input data matrix. |
enableROTS |
logical; if |
paraROTS |
a |
minLFC |
LFC threshold |
rangeSimNumRep |
a vector of sample sizes under which power will be estimated |
alpha |
controlled false positive rate. |
ST |
the number of simulations of abundance data generation and repeated times of statistical test for each protein (>=100 recommended). |
seed |
an integer seed for the random number generator. |
parallel |
logical; if |
BPPARAM |
an optional argument object passed |
showProcess |
logical; if |
saveResultData |
logical; if |
a list of power predictions for each sample size, grouped in comparisons between each two groups
estimatePower
estimate power based on
actual data
# Example 1: a random generated Proteomics dataset (10 DE, 100 non-DE) data(exampleProteomicsData) dataMatrix <- exampleProteomicsData$dataMatrix groupVec <- exampleProteomicsData$groupVec # Run estimation # Note: Simulation times(ST) is specified as 5 for shorter example runtime # For better performence, ST > 50 is recommended predictedPower <- predictPower(dataMatrix, groupVec, isLogTransformed=FALSE, dataType="Proteomics", minLFC=0, rangeSimNumRep=c(5, 10, 15), alpha=0.05, ST=5, seed=123)