plotEvalDE | R Documentation |
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)
evalRes |
The output of |
rate |
Character vector defining whether the |
quick |
A logical vector. If |
Annot |
A logical vector. If |
A ggplot object.
Beate Vieth
## Not run:
# 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)
## End(Not run)
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