plotMEtraitCor: Plot a Heatmap of Correlations Between Module Eigennodes and...

In cemordaunt/comethyl: An R Package for Weighted Region Comethylation Network Analysis

Plot a Heatmap of Correlations Between Module Eigennodes and Traits

Description

plotMEtraitCor() takes a data.frame of correlation statistics for module eigennodes and traits from getMEtraitCor(), plots it as a heatmap, and saves it as a .pdf.

Usage

plotMEtraitCor(
  MEtraitCor,
  moduleOrder = 1:length(unique(MEtraitCor$module)),
  traitOrder = 1:length(unique(MEtraitCor$trait)),
  topOnly = FALSE,
  nTop = 15,
  p = 0.05,
  label.type = c("star", "p"),
  label.size = 8,
  label.nudge_y = -0.38,
  colors = blueWhiteRed(100, gamma = 0.9),
  limit = NULL,
  axis.text.size = 12,
  legend.position = c(1.08, 0.915),
  legend.text.size = 12,
  legend.title.size = 16,
  colColorMargins = c(-0.7, 4.21, 1.2, 11.07),
  save = TRUE,
  file = "ME_Trait_Correlation_Heatmap.pdf",
  width = 11,
  height = 9.5,
  verbose = TRUE
)

Arguments

MEtraitCor	A data.frame of correlation statistics for module-trait pairs, typically generated by getMEtraitCor().
moduleOrder	A numeric specifying the order of modules in the heatmap. This must be the same length as the number of unique modules in MEtraitCor.
traitOrder	A numeric specifying the order of traits in the heatmap. This must be the same length as the number of unique traits in MEtraitCor.
topOnly	A logical(1) indicating whether to plot only modules and traits with the most significant correlations, ranked by p-value.
nTop	A numeric(1) specifying the number of top module-trait associations to include.
p	A numeric(1) giving the threshold for the p-value to assign a significant correlation.
label.type	A character(1) giving the type of label (star or p) to use to annotate significant correlations.
label.size	A numeric(1) specifying the size of the label text for annotating significant correlations.
label.nudge_y	A numeric(1) giving the amount to adjust the position of the label text on the y-axis.
colors	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.
axis.text.size	A numeric(1) specifying the size of the text for the y-axis.
legend.position	A numeric(2) with the position of the legend, as x-axis, y-axis. May also be a character(1) indicating "none", "left", "right", "bottom", or "top".
legend.text.size	A numeric(1) specifying the size of the text for the legend axis.
legend.title.size	A numeric(1) giving the size of the text for the legend title.
colColorMargins	A numeric(4) giving the width of the margins for the column (horizontal) 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.

Details

plotMEtraitCor() is designed to be used in combination with getMEtraitCor(). Stars or p-values are used to annotate significant correlations, as defined by the p-value threshold. The heatmap can optionally be filtered to include only modules and traits with the most significant associations, ranked by p-value. A ggplot object is produced and can be edited outside of this function if desired.

Value

A ggplot object.

See Also

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

• getMEtraitCor() to calculate ME-trait correlations.

• getCor() to calculate correlation coefficients.

• getDendro() and plotDendro() to generate and visualize dendrograms.

Examples

## Not run: 

# 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")

# Compare Top PCs to Sample Traits
MEtraitCor <- getMEtraitCor(PCs, colData = colData, corType = "bicor",
                            file = "PC_Trait_Correlation_Stats.txt")
PCdendro <- getDendro(PCs, distance = "bicor")
PCtraitDendro <- getCor(PCs, y = colData, corType = "bicor", robustY = FALSE) %>%
        getDendro(transpose = TRUE)
plotMEtraitCor(PCtraitCor, moduleOrder = PCdendro$order,
               traitOrder = PCtraitDendro$order,
               file = "PC_Trait_Correlation_Heatmap.pdf")

# Examine Correlations between Sample Traits
traitDendro <- getCor(MEs, y = colData, corType = "bicor",
                      robustY = FALSE) %>%
        getDendro(transpose = TRUE)
plotDendro(traitDendro, labelSize = 3.5, expandY = c(0.65,0.08),
           file = "Trait_Dendrogram.pdf")

# Visualize Correlations between Module Eigennodes and Sample Traits
moduleDendro <- getDendro(MEs, distance = "bicor")
plotMEtraitCor(MEtraitCor, moduleOrder = moduleDendro$order,
               traitOrder = traitDendro$order,
               file = "ME_Trait_Correlation_Heatmap.pdf")
plotMEtraitCor(MEtraitCor, moduleOrder = moduleDendro$order,
               traitOrder = traitDendro$order, topOnly = TRUE,
               label.type = "p", label.size = 4, label.nudge_y = 0,
               legend.position = c(1.11, 0.795),
               colColorMargins = c(-1,4.75,0.5,10.1),
               file = "Top_ME_Trait_Correlation_Heatmap.pdf", width = 8.5,
               height = 4.25)

## End(Not run)

cemordaunt/comethyl documentation built on Oct. 20, 2023, 5:47 p.m.