Calculate Top Principal Components

getPCs() calculates the top principal components for region methylation data, and then saves it as a .rds file.

Usage

getPCs(
  meth,
  mod = matrix(1, nrow = ncol(meth), ncol = 1),
  save = TRUE,
  file = "Top_Principal_Components.rds",
  verbose = TRUE
)

Arguments

meth

A numeric matrix, where each row is a region and each column is a sample. This is typically obtained from getRegionMeth().

mod

A matrix giving the model matrix being used to fit the data. See below for an example.

save

A logical(1) indicating whether to save the matrix.

file

A character(1) giving the file name (.rds) for the saved matrix.

verbose

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

Value

A numeric matrix, where each row is a sample and each column is a principal component.

Details

getPCs() uses sva::num.sv() to identify the number of top principal components and then svd() to calculate them, after centering methylation values within each gene. This is the same approach used by sva::sva_network(). More information on the function and approach is given in the documentation and publications related to the sva package.

See also

• getRegionMeth() to extract region methylation values.

• adjustRegionMeth() to adjust region methylation values using these top PCs.

• getMEtraitCor() to compare these top PCs to sample traits.

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

• getSoftPower() and plotSoftPower() to estimate the best soft-thresholding power and visualize scale-free topology fit and connectivity.

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

Examples

if (FALSE) {

# Get Methylation Data
meth <- getRegionMeth(regions, bs = bs, file = "Region_Methylation.rds")

# Adjust Methylation Data for Top PCs
mod <- model.matrix(~1, data = pData(bs))
PCs <- getPCs(meth, mod = mod, file = "Top_Principal_Components.rds")
methAdj <- adjustRegionMeth(meth, PCs = PCs,
                            file = "Adjusted_Region_Methylation.rds")

# 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")

# Assess Sample Similarity
getDendro(methAdj, distance = "euclidean") %>%
         plotDendro(file = "Sample_Dendrogram.pdf", expandY = c(0.25,0.08))

# Select Soft Power Threshold
sft <- getSoftPower(methAdj, corType = "pearson", file = "Soft_Power.rds")
plotSoftPower(sft, file = "Soft_Power_Plots.pdf")

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