plotting {methimpute} | R Documentation |
This page provides an overview of all methimpute plotting functions.
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)
model |
A |
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 |
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 |
plot |
Logical indicating whether a plot or the underlying data.frame is to be returned. |
df.list |
A list() of data.frames, output from |
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 |
A ggplot
object.
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.
## 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)