Estimate Soft Power Threshold

getSoftPower() analyzes scale-free topology to estimate the best soft-thresholding power from a vector of powers, calculate fit indices, and then saves this as a .rds file. Possible correlation statistics include pearson and bicor.

Usage

getSoftPower(
  meth,
  powerVector = 1:20,
  corType = c("pearson", "bicor"),
  maxPOutliers = 0.1,
  RsquaredCut = 0.8,
  blockSize = 40000,
  gcInterval = blockSize - 1,
  save = TRUE,
  file = "Soft_Power.rds",
  verbose = TRUE
)

Arguments

meth

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

powerVector

A numeric specifying the soft power thresholds to examine for scale-free topology.

corType

A character(1) indicating which correlation statistic to use in the adjacency calculation.

maxPOutliers

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

RsquaredCut

A numeric(1) giving the minimum R-squared value for scale-free topology. Used to choose the best soft-thresholding power.

blockSize

A numeric(1) specifying the number of regions in each block for the connectivity calculation. Decrease this if memory is insufficient.

gcInterval

A numeric(1) indicating the interval for garbage collection.

save

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

file

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

verbose

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

Value

A list with two elements: powerEstimate, which gives the estimated best soft-thresholding power, and fitIndices, which is a data.frame with statistics on scale-free topology, including fit and connectivity, along with network density, centralization, and heterogeneity.

Details

Soft power is estimated by WGCNA::pickSoftThreshold(), with corFnc set to either cor or bicor. Calculations are performed for a signed network in blocks of regions of size blockSize (default = 40000). The best soft power threshold is chosen as the lowest power where fit (R-squared) is greater than RsquaredCut (default = 0.8). More information is given in the documentation for WGCNA::pickSoftThreshold().

See also

• getRegionMeth(), getPCs(), and adjustRegionMeth() to extract methylation data and then adjust it for the top principal components.

• plotSoftPower() to visualize fit and connectivity for soft power estimation.

• 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 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")

# 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")
}