plotTime | R Documentation |
This function plots the computational running time of the simulations.
plotTime(evalRes, Table=TRUE, Annot=TRUE)
evalRes |
The output of |
Table |
A logical vector. If |
Annot |
A logical vector. If |
A ggplot object.
Beate Vieth
## 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)
plotTime(evalRes = evalsimres, Annot = TRUE)
## End(Not run)
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