estimatePower: Estimate Power of the Actual Data
In PowerExplorer: Power Estimation Tool for RNA-Seq and proteomics data

Description Usage Arguments Value See Also Examples

Estimate power of comparison between each two groups based on the data simulated from estimated normal distributions of entrys in the entire dataset

estimatePower(inputObject, groupVec, isLogTransformed = FALSE,
  dataType = c("RNASeq", "Proteomics"), minLFC = 0.5, alpha = 0.05,
  ST = 100, seed = 123, enableROTS = FALSE, paraROTS = list(B = 1000, K
  = NULL, paired = FALSE, a1 = NULL, a2 = NULL, progress = FALSE),
  showProcess = FALSE, saveResultData = FALSE, parallel = FALSE,
  BPPARAM = bpparam())

`inputObject`	a numeric raw data matrix or SummarizedExperiment object
`groupVec`	a vector indicating the grouping of samples
`isLogTransformed`	logical; set to `TRUE`, if the input data is log transformed.
`dataType`	"RNASeq" or "Proteomics" indictes the data type of the input data matrix.
`minLFC`	the threshold for log2 fold change, entrys with lower LFC are not included in the power calculation, set to 0 if no threshold is needed.
`alpha`	controlled false positive rate.
`ST`	the number of simulations of abundance data generation and repeated times of statistical test for each entry (>=100 recommended).
`seed`	an integer seed for the random number generator.
`enableROTS`	logical; if `TRUE`, Reproducibility-Optimized Test Statistic (ROTS) will be used as the statistical model. used as the statistical model.
`paraROTS`	a `list` object containing addtional parameters passed to ROTS (if enabled), see `ROTS`.
`showProcess`	logical; if `TRUE`, show the detailed information of each simulation, used for debugging only.
`saveResultData`	logical; if `TRUE`, save the simulated data into RData with name pattern "simulated_Data_numRep_X_numSim_XXX_XXXXX.RData"
`parallel`	logical; if `FALSE` parallelization is disabled; if `TRUE`, parallelize calculations using `BiocParallel`.
`BPPARAM`	an optional argument object passed `bplapply` to indicate the registered cores, if `parallel=TRUE`.

a list of power estimates grouped in comparisons between each two groups

predictPower predict power with incresing sample sizes

# Example 1: a random generated Proteomics dataset (10 DE, 100 non-DE)
# Note: Simulation times(ST) is specified as 10 for shorter example runtime,
# ST > 50 is recommended
data(exampleProteomicsData)
dataMatrix <- exampleProteomicsData$dataMatrix
groupVec <- exampleProteomicsData$groupVec

# Run estimation without LFC filtration
resObject <- estimatePower(dataMatrix, groupVec,
                           dataType="Proteomics",
                           isLogTransformed=FALSE,
                           minLFC=0, alpha=0.05,
                           ST=10, seed=123)