plotParam: Visualize distributional characteristics of RNA-seq...

In bvieth/powsimR: Power Simulations for RNA-sequencing

Visualize distributional characteristics of RNA-seq experiment

Description

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.

Usage

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.

Author(s)

Beate Vieth

Examples

## Not run: 
# 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)

## End(Not run)

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