Calculate Correlation Statistics Between Module Eigennodes and Traits

getMEtraitCor() calculates correlation coefficients and p-values between eigennode values for all modules and all sample traits and saves it as a .txt file. Correlations are performed using either pearson or bicor methods.

Usage

getMEtraitCor(
  MEs,
  colData,
  corType = c("bicor", "pearson"),
  maxPOutliers = 0.1,
  robustY = FALSE,
  save = TRUE,
  file = "ME_Trait_Correlation_Stats.txt",
  verbose = TRUE
)

Arguments

MEs

A data.frame of module eigennode values, where rows are samples and columns are modules. The row names of MEs must include the same set of values as the row names of colData.

colData

A data.frame whose row names specify samples and whose columns are sample traits with numeric values.

corType

A character(1) indicating which correlation statistic to use in the calculation. Potential values include pearson and bicor.

maxPOutliers

A numeric(1) specifying the maximum percentile that can be considered outliers on each side of the median for the bicor statistic.

robustY

A logical(1) indicating whether to use robust calculation for y for the bicor statistic. FALSE is recommended if y is a binary variable.

save

A logical(1) indicating whether to save the data.frame.

file

A character(1) giving the file name (.txt) for the saved data.frame.

verbose

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

Value

A data.frame giving correlation statistics for each module-trait pair.

Details

getMEtraitCor() is designed to be used in combination with getModules(). The correlation calculations are performed by WGCNA::corAndPvalue() and WGCNA::bicorAndPvalue(). getMEtraitCor() can also be used to calculate pairwise correlation coefficients and p-values between module eigennode values, or between top adjusted PCs and sample traits (see examples).

See also

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

• getCor() to calculate correlation coefficients.

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

• plotMEtraitCor() to visualize ME-trait correlations.

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")

# Examine Correlations between Modules
moduleCorStats <- getMEtraitCor(MEs, colData = MEs, corType = "bicor",
                                robustY = TRUE,
                                file = "Module_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)
}