Calculate Correlations

getCor() calculates correlation coefficients using either pearson or bicor methods. Calculations can be done between columns of a single matrix or between two vectors or matrices.

Usage

getCor(
  x,
  y = NULL,
  transpose = FALSE,
  corType = c("bicor", "pearson"),
  maxPOutliers = 0.1,
  robustY = TRUE,
  verbose = TRUE
)

Arguments

x

A numeric vector or matrix. x must be a matrix if y is null.

y

A numeric vector or matrix. If null, correlations will be calculated for columns of x.

transpose

A logical(1) specifying whether to transpose the matrix.

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.

verbose

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

Value

A numeric matrix.

Details

The first input argument can be optionally transposed. The correlation calculations are performed by WGCNA::cor() and WGCNA::bicor().

See also

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

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

• plotHeatmap() to visualize correlations between samples and modules.

• getMEtraitCor() to calculate pairwise correlation coefficients and p-values between module eigennode values.

Examples

if (FALSE) {

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

# Examine Correlations between Modules
MEs <- modules$MEs
moduleDendro <- getDendro(MEs, distance = "bicor")
plotDendro(moduleDendro, labelSize = 4, nBreaks = 5,
           file = "Module_ME_Dendrogram.pdf")
moduleCor <- getCor(MEs, corType = "bicor")
plotHeatmap(moduleCor, rowDendro = moduleDendro, colDendro = moduleDendro,
            file = "Module_Correlation_Heatmap.pdf")
moduleCorStats <- getMEtraitCor(MEs, colData = MEs, corType = "bicor",
                                robustY = TRUE,
                                file = "Module_Correlation_Stats.txt")

# Examine Correlations between Samples
sampleDendro <- getDendro(MEs, transpose = TRUE, distance = "bicor")
plotDendro(sampleDendro, labelSize = 3, nBreaks = 5,
           file = "Sample_ME_Dendrogram.pdf")
sampleCor <- getCor(MEs, transpose = TRUE, corType = "bicor")
plotHeatmap(sampleCor, rowDendro = sampleDendro, colDendro = sampleDendro,
            file = "Sample_Correlation_Heatmap.pdf")

# Visualize Module Eigennode Values
plotHeatmap(MEs, rowDendro = sampleDendro, colDendro = moduleDendro,
            legend.title = "Module\nEigennode",
            legend.position = c(0.37,0.89),
            file = "Sample_ME_Heatmap.pdf")
}