estimatePower: Estimate Power of the Actual Data
In xuqiao93/PowerExplorer: Power Estimation Tool for RNA-Seq and proteomics data

Description Usage Arguments Value See Also Examples

View source: R/estimatePower.R

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)