plotting: Methimpute plotting functions

In methimpute: Imputation-guided re-construction of complete methylomes from WGBS data

Description

This page provides an overview of all methimpute plotting functions.

Usage

plotHistogram(model, total.counts, binwidth = 1)

plotScatter(model, datapoints = 1000)

plotTransitionProbs(model)

plotConvergence(model)

plotEnrichment(model, annotation, windowsize = 100, insidewindows = 20,
  range = 1000, category.column = NULL, plot = TRUE,
  df.list = NULL)

plotPosteriorDistance(model, datapoints = 1e+06, binwidth = 5,
  max.coverage.y = 0, min.coverage.x = 3, xmax = 200,
  xbreaks.interval = xmax/10, cutoffs = NULL)

Arguments

model	A methimputeBinomialHMM object.
total.counts	The number of total counts for which the histogram is to be plotted.
binwidth	The bin width for the histogram/boxplot.
datapoints	The number of randomly selected datapoints for the plot.
annotation	A GRanges-class object with coordinates for the annotation.
windowsize	Resolution in base-pairs for the curve upstream and downstream of the annotation.
insidewindows	Number of data points for the curve inside the annotation.
range	Distance upstream and downstream for which the enrichment profile is calculated.
category.column	The name of a column in data that will be used for facetting of the plot.
plot	Logical indicating whether a plot or the underlying data.frame is to be returned.
df.list	A list() of data.frames, output from plotEnrichment(..., plot=FALSE). If specified, option data will be ignored.
max.coverage.y	Maximum coverage for positions on the y-axis.
min.coverage.x	Minimum coverage for positions on the x-axis.
xmax	Upper limit for the x-axis.
xbreaks.interval	Interval for breaks on the x-axis.
cutoffs	A vector with values that are plotted as horizontal lines. The names of the vector must match the context levels in data$context.

Value

A ggplot object.

Functions

• plotHistogram: Plot a histogram of count values and fitted distributions.

• plotScatter: Plot a scatter plot of read counts colored by methylation status.

• plotTransitionProbs: Plot a heatmap of transition probabilities.

• plotConvergence: Plot the convergence of the probability parameters.

• plotEnrichment: Plot an enrichment profile around an annotation.

• plotPosteriorDistance: Maximum posterior vs. distance to nearest covered cytosine.

Examples

## Get some toy data
file <- system.file("data","arabidopsis_toydata.RData",
                    package="methimpute")
data <- get(load(file))
print(data)
model <- callMethylation(data)
## Make nice plots
plotHistogram(model, total.counts=5)
plotScatter(model)
plotTransitionProbs(model)
plotConvergence(model)
plotPosteriorDistance(model$data)

## Get annotation data and make an enrichment profile
# Note that this looks a bit ugly because our toy data
# has only 200000 datapoints.
data(arabidopsis_genes)
plotEnrichment(model, annotation=arabidopsis_genes)

methimpute documentation built on Nov. 8, 2020, 5:47 p.m.