Visualize power assessment — plotEvalDE • powsimR

This function plots the results of evaluateDE for assessing the error rates and sample size requirements.

plotEvalDE(evalRes, rate=c('marginal', 'conditional'),
                   quick=TRUE, Annot=TRUE)

Arguments

evalRes	The output of `evaluateDE`.
rate	Character vector defining whether the `"marginal"` or `"conditional"` rates should be plotted. Conditional depends on the choice of stratify.by in `evaluateDE`.
quick	A logical vector. If `TRUE`, the TPR and FDR are only plotted. If `FALSE`, then all rates are plotted.
Annot	A logical vector. If `TRUE`, a short figure legend under the plot is included.

Value

A ggplot object.

Examples

if (FALSE) {
# estimate gene parameters
data("Bulk_Read_Counts")
data("GeneLengths_hg19")
estparam_gene <- estimateParam(countData = Bulk_Read_Counts,
                               readData = NULL,
                               batchData = NULL,
                               spikeData = NULL, spikeInfo = NULL,
                               Lengths = GeneLengths_hg19, MeanFragLengths = NULL,
                               RNAseq = 'bulk', Protocol = 'Read',
                               Distribution = 'NB', Normalisation = "MR",
                               GeneFilter = 0.25, SampleFilter = 3,
                               sigma = 1.96, NCores = NULL, verbose = TRUE)
# define log fold change
p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 2, rate = 2)
# set up simulations
setupres <- Setup(ngenes = 10000, nsims = 10,
                  p.DE = 0.1, pLFC = p.lfc,
                  n1 = c(3,6,12), n2 = c(3,6,12),
                  Thinning = c(1,0.9,0.8), LibSize = 'given',
                  estParamRes = estparam_gene,
                  estSpikeRes = NULL,
                  DropGenes = FALSE,
                  sim.seed = 4379, verbose = TRUE)
# run simulation
simres <- simulateDE(SetupRes = setupres,
                     Prefilter = NULL, Imputation = NULL,
                     Normalisation = 'MR', Label = 'none',
                     DEmethod = "limma-trend", DEFilter = FALSE,
                     NCores = NULL, verbose = TRUE)
# DE evaluation
evalderes <- evaluateDE(simRes = simres, alpha.type="adjusted",
                        MTC='BH', alpha.nominal=0.05,
                        stratify.by = "mean", filter.by = "none")
plotEvalDE(evalderes, rate = "marginal", quick = FALSE, Annot = TRUE)
plotEvalDE(evalderes, rate = "conditional", quick = FALSE, Annot = TRUE)
}