plotEvalSim: Visualize power assessment

In bvieth/powsimR: Power Simulations for RNA-sequencing

Visualize power assessment

Description

This function plots the results of evaluateSim for assessing the setup performance, i.e. normalisation method performance.

Usage

plotEvalSim(evalRes, Annot=TRUE)

Arguments

evalRes	The output of evaluateSim.
Annot	A logical vector. If TRUE, a short figure legend under the plot is included.

Value

A ggplot object.

Author(s)

Beate Vieth

Examples

## Not run: 
# estimate gene parameters
data("SmartSeq2_Gene_Read_Counts")
Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_Gene_Read_Counts), "_"), "[[", 1),
                     stringsAsFactors = F,
                     row.names = colnames(SmartSeq2_Gene_Read_Counts))
data("GeneLengths_mm10")
estparam_gene <- estimateParam(countData = SmartSeq2_Gene_Read_Counts,
                               readData = NULL,
                               batchData = Batches,
                               spikeData = NULL, spikeInfo = NULL,
                               Lengths = GeneLengths_mm10, MeanFragLengths = NULL,
                               RNAseq = 'singlecell', Protocol = 'Read',
                               Distribution = 'ZINB', Normalisation = "scran",
                               GeneFilter = 0.1, 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 = 1, rate = 2)
# set up simulations
setupres <- Setup(ngenes = 10000, nsims = 10,
                  p.DE = 0.1, pLFC = p.lfc,
                  n1 = c(20,50,100), n2 = c(30,60,120),
                  Thinning = c(1,0.9,0.8), LibSize = 'given',
                  estParamRes = estparam_gene,
                  estSpikeRes = NULL,
                  DropGenes = FALSE,
                  sim.seed = 66437, verbose = TRUE)
# run simulation
simres <- simulateDE(SetupRes = setupres,
                     Prefilter = "FreqFilter",
                     Imputation = NULL,
                     Normalisation = 'scran', Label = 'none',
                     DEmethod = "limma-trend", DEFilter = FALSE,
                     NCores = NULL, verbose = TRUE)
# evaluation
evalsimres <- evaluateSim(simRes = simres)
plotEvalSim(evalRes = evalsimres, Annot = TRUE)

## End(Not run)

bvieth/powsimR documentation built on Aug. 19, 2023, 7:48 p.m.