plotting: Methimpute plotting functions

Description Usage Arguments Value Functions Examples

Description

This page provides an overview of all methimpute plotting functions.

Usage

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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

Examples

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## 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)

ataudt/popmeth documentation built on May 10, 2019, 2:07 p.m.