Description Usage Arguments Details Value Examples
Take a counts matrix and design matrix and return a power analysis using the RNASeqPower package. The counts matrix should be prefiltered to remove nonexpressed genes using an appropriate filtering criteria. The design matrix should describe the major sources of variation so the procedure can dial out those known effects for the power calculations.
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countsMatrix 
A counts matrix or dataframe of numeric data. (Required) 
designMatrix 
A design matrix or dataframe of numeric data. (Required) 
depth 
A set of depth to use in the calculations. The default depths of c(10, 100, 1000) respectively represent a detection limit, below average expression, and median expression levels, expressed in read count units. 
N 
A set of N value to report power for. (Default = c(3, 6, 10, 20)) 
FDR 
FDR thresholds to filter for for FDR vs. Power graph. (Default = c(0.05, 0.1)) 
effectSize 
A set of fold change values to test. (Default = c(1.2, 1.5, 2)) 
includePlots 
controls adding tow plots to the returned dataframe (Default = FALSE). The two plots are; a ROC curve (FDR vs. Power) and a plot of N vs. Power. Possible values to pass:

If includePlots = FALSE (the default) or NULL, the function will return a tall skinny dataframe of power calculations for various requested combinations of N and significance thresholds.
If includePlots = TRUE, "canvasXpress" or "ggplot", a list is returned with an additional two "canvasXpress" or ggplots (plots) to the dataframe.
a dataframe of power calculations or a list of the dataframe and defined plots as defined by the "includePlots" argument.
1 2 3 4 5 6 7 8 9 10  dgeObj < readRDS(system.file("exampleObj.RDS", package = "DGEobj"))
counts < dgeObj$counts
dm < DGEobj::getType(dgeObj, type = "designMatrix")[[1]]
resultList < runPower(countsMatrix = counts,
designMatrix = dm,
includePlots = TRUE)
head(resultList[[1]]) # dataframe
resultList[[2]] # ROC Curves Plot
resultList[[3]] # N vs Power Plot

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