Plot Heatmaps of Region Standard Deviation vs Features

plotSDstats() takes a set of regions from getRegions(), generates heatmaps of methylation standard deviation against region features, and saves it as a pdf. Compared features include number of CpGs, minimum coverage, mean coverage, and mean methylation.

Usage

plotSDstats(
  regions,
  maxQuantile = 1,
  bins = 30,
  nBreaks = 4,
  legend.position = c(1.09, 0.9),
  save = TRUE,
  file = "SD_Plots.pdf",
  width = 8.5,
  height = 8.5,
  verbose = TRUE
)

Arguments

regions

A data.frame output from getRegions() giving the set of regions and statistics for each region.

maxQuantile

A numeric(1) giving the maximum quantile of each feature to plot.

bins

A numeric(1) specifying the number of bins for both axes in each heatmap.

nBreaks

A numeric(1) specifying the number of breaks for both axes.

legend.position

A numeric(2) specifying the position of the legend, as x-axis, y-axis. May also be a character(1) indicating "none", "left", "right", "bottom", or "top".

save

A logical(1) indicating whether to save the plot.

file

A character(1) giving the file name (.pdf) for the plot.

width

A numeric(1) specifying the width in inches of the saved plot.

height

A numeric(1) specifying the height in inches of the saved plot.

verbose

A logical(1) indicating whether messages should be printed.

Value

A ggplot object.

Details

It's recommended examine these plots before and after filtering to ensure removal of regions with high variability due to insufficient data. Plots are heatmaps of 2D bin counts, with the color indicating the number of regions in that bin on the log10 scale. A ggplot object is produced and can be edited outside of this function if desired.

See also

• getRegions() to generate the set of regions.

• plotRegionStats(), getRegionTotals(), and plotRegionTotals() for more help visualizing region characteristics and setting cutoffs for filtering.

• filterRegions() for filtering regions by minimum coverage and methylation standard deviation.

Examples

if (FALSE) {

# Call Regions
regions <- getRegions(bs, file = "Unfiltered_Regions.txt")
plotRegionStats(regions, maxQuantile = 0.99,
                file = "Unfiltered_Region_Plots.pdf")
plotSDstats(regions, maxQuantile = 0.99,
            file = "Unfiltered_SD_Plots.pdf")

# Examine Region Totals at Different Cutoffs
regionTotals <- getRegionTotals(regions, file = "Region_Totals.txt")
plotRegionTotals(regionTotals, file = "Region_Totals.pdf")

# Filter Regions
regions <- filterRegions(regions, covMin = 10, methSD = 0.05,
                         file = "Filtered_Regions.txt")
plotRegionStats(regions, maxQuantile = 0.99,
                file = "Filtered_Region_Plots.pdf")
plotSDstats(regions, maxQuantile = 0.99,
            file = "Filtered_SD_Plots.pdf")
}