Visualize a Module Eigennode - Trait Correlation as a Scatter Plot

plotMEtraitScatter() takes a vector of module eigennode values and a vector of continuous sample trait values, generates a scatter plot, and then saves it as a .pdf. ME and trait must be in the same order.

Usage

plotMEtraitScatter(
  ME,
  trait,
  color = "#132B43",
  xlim = NULL,
  ylim = NULL,
  nBreaks = 4,
  point.size = 2.5,
  axis.title.size = 20,
  axis.text.size = 16,
  xlab = "Trait",
  ylab = "Module Eigennode",
  save = TRUE,
  file = "ME_Trait_Scatterplot.pdf",
  width = 6,
  height = 6,
  verbose = TRUE
)

Arguments

ME

A numeric of module eigennode values. ME must be in the same order as trait.

trait

A numeric of continuous sample trait values.

color

A character(1) giving the color of the points.

xlim

A numeric(2) specifying the limits of the x-axis.

ylim

A numeric(2) specifying the limits of the y-axis.

nBreaks

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

point.size

A numeric(1) indicating the size of the points.

axis.title.size

A numeric(1) indicating the size of the title text for both axes.

axis.text.size

A numeric(1) specifying the size of the text for both axes.

xlab

A character(1) giving the x-axis title.

ylab

A character(1) giving the y-axis title.

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

The values in ME and trait are plotted as points along with a smoothed line with a shaded 95% confidence interval. The smoothed line is fit using robust regression as implemented by MASS::rlm(). A ggplot object is produced and can be edited outside of this function if desired.

See also

• getModules() to build a comethylation network and identify modules of comethylated regions.

• getMEtraitCor() and plotMEtraitCor() to calculate and visualize all ME-trait correlations.

• plotMEtraitDot() and plotMethTrait() for other methods to visualize a single ME-trait correlation.

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")
}