Visualize distributional characteristics of RNA-seq experiment

Source: R/Plot.R

This function plots the results of the parameter estimation. This includes the absolute and relative sequencing depth (i.e. library size factor) as well as marginal log mean, log dispersion and dropout. Furthermore, the mean-dispersion relationship with loess fit for simulations is visualized. Lastly, the mean-dropout rate is presented as a smooth scatter plot.

plotParam(estParamRes, Annot=TRUE)

Arguments

estParamRes

The output of estimateParam.
Annot

A logical vector. If TRUE, a short figure legend is included.

Value

A ggplot object.

Examples

if (FALSE) {
# using example data set
data("CELseq2_Gene_UMI_Counts")
data("CELseq2_Gene_Read_Counts")
Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1),
                  stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts))
data("GeneLengths_mm10")
data("CELseq2_SpikeIns_UMI_Counts")
data("CELseq2_SpikeInfo")
# estimation
estparam <-  estimateParam(countData = CELseq2_Gene_UMI_Counts,
readData = CELseq2_Gene_Read_Counts,
batchData = Batches,
spikeData = NULL,
spikeInfo = NULL,
Lengths = GeneLengths_mm10,
MeanFragLengths = NULL,
Distribution = 'NB',
RNAseq = 'singlecell',
Protocol = 'UMI',
Normalisation = 'scran',
GeneFilter = 0.1,
SampleFilter = 3,
sigma = 1.96,
NCores = NULL,
verbose = TRUE)
# plotting
plotParam(estParamRes = estparam, Annot=TRUE)
}