estimatePower: Estimate Power of the Actual Data

Description Usage Arguments Value See Also Examples

View source: R/estimatePower.R

Description

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

Usage

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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())

Arguments

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.

Value

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

See Also

predictPower predict power with incresing sample sizes

Examples

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# 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)

xuqiao93/PowerExplorer documentation built on May 16, 2019, 9:13 p.m.