Plot Module Methylation Values By a Sample Trait

plotMethTrait() extracts the methylation values for regions in a given module, plots it against a sample trait in a heatmap, and then saves it as a .pdf. trait must be in the same order as the columns in meth.

Usage

plotMethTrait(
  module,
  regions,
  meth,
  trait,
  discrete = NULL,
  traitCode = NULL,
  traitColors = NULL,
  heatmapColors = blueWhiteRed(100, gamma = 0.3),
  limit = NULL,
  expandY = 0.05,
  axis.text.size = 11,
  heatmap.legend.position = c(1.1, 0.743),
  trait.legend.position = c(1.017, 4.39),
  heatmap.legend.title = "Relative\nMethylation (%)",
  trait.legend.title = "Trait",
  legend.text.size = 11,
  legend.title.size = 14,
  heatmapMargins = c(1, 8, 0, 1),
  traitMargins = c(0, 6, 1, 5.15),
  save = TRUE,
  file = "Module_Methylation_Trait_Heatmap.pdf",
  width = 11,
  height = 4,
  verbose = TRUE
)

Arguments

module: A character(1) giving the name of the module to plot.
regions: A data.frame of regions with module assignments, typically obtained from getModules().
meth: A numeric matrix, where each row is a region and each column is a sample. This is typically obtained from getRegionMeth().
trait: A numeric of sample trait values.
discrete: A logical(1) identifying trait as a discrete variable or not. If null, plotMethTrait() will guess if the trait is discrete (<= 5 unique values).
traitCode: A named numeric vector matching each trait level to a numeric value. Example: c("Control" = 0, "Treatment" = 1).
traitColors: A named character vector matching each trait level to a color. Example: c("Control" = "blue", "Treatment" = "red").
heatmapColors: A character giving a vector of colors to use for the gradient on the heatmap. The default uses WGCNA::blueWhiteRed() to generate these colors.
limit: A numeric(1) giving the maximum value (symmetric) for the heatmap color scale.
expandY: A numeric specifying the multiplicative range expansion factors. Can be given as the symmetric lower and upper limit expansions or separately as a vector of length 2.
axis.text.size: A numeric(1) giving the size of the text on the y-axis.
heatmap.legend.position: A numeric(2) with the position of the heatmap legend, as x-axis, y-axis. May also be a character(1) indicating "none", "left", "right", "bottom", or "top".
trait.legend.position: A numeric(2) with the position of the color bar legend, as x-axis, y-axis. May also be a character(1) indicating "none", "left", "right", "bottom", or "top".
heatmap.legend.title: A character(1) giving the title for the heatmap legend.
trait.legend.title: A character(1) giving the title for the color bar legend.
legend.text.size: A numeric(1) indicating the size the text in both legends.
legend.title.size: A numeric(1) specifying the size of the text for both legend titles.
heatmapMargins: A numeric(4) giving the width of the margins for the heatmap as top, right, bottom, and left.
traitMargins: A numeric(4) giving the width of the margins for the color bar as top, right, bottom, and left.
save: A logical(1) indicating whether to save the plot.
file: A character(1) giving the file name (.pdf) for the saved 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

NA values in the trait are removed if present, along with corresponding ME values. If discrete is not provided plotMethTrait() will guess if the trait is discrete (<= 5 unique values) and plot the trait color as a discrete scale rather than a continuous one. Samples are ordered by trait value in ascending order. Methylation values are plotted relative to the mean methylation in that region.

Examples

if (FALSE) {

# Get Comethylation Modules
modules <- getModules(methAdj, power = sft$powerEstimate, regions = regions,
                      corType = "pearson", file = "Modules.rds")

# Test Correlations between Module Eigennodes and Sample Traits
MEs <- modules$MEs
MEtraitCor <- getMEtraitCor(MEs, colData = colData, corType = "bicor",
                            file = "ME_Trait_Correlation_Stats.txt")
plotMEtraitCor(MEtraitCor, moduleOrder = moduleDendro$order,
               traitOrder = traitDendro$order,
               file = "ME_Trait_Correlation_Heatmap.pdf")

# Explore Individual ME-Trait Correlations
plotMEtraitDot(MEs$bisque4, trait = colData$Diagnosis_ASD,
               traitCode = c("TD" = 0, "ASD" = 1),
               colors = c("TD" = "#3366CC", "ASD" = "#FF3366"),
               ylim = c(-0.2,0.2), xlab = "Diagnosis",
               ylab = "Bisque 4 Module Eigennode",
               file = "bisque4_ME_Diagnosis_Dotplot.pdf")
plotMEtraitScatter(MEs$paleturquoise, trait = colData$Gran,
                   ylim = c(-0.15,0.15), xlab = "Granulocytes",
                   ylab = "Pale Turquoise Module Eigennode",
                   file = "paleturquoise_ME_Granulocytes_Scatterplot.pdf")
regions <- modules$regions
plotMethTrait("bisque4", regions = regions, meth = meth,
              trait = colData$Diagnosis_ASD,
              traitCode = c("TD" = 0, "ASD" = 1),
              traitColors = c("TD" = "#3366CC", "ASD" = "#FF3366"),
              trait.legend.title = "Diagnosis",
              file = "bisque4_Module_Methylation_Diagnosis_Heatmap.pdf")
}