View source: R/Build_Comethylation_Network.R
getSoftPower | R Documentation |
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
.
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
)
meth |
A |
powerVector |
A |
corType |
A |
maxPOutliers |
A |
RsquaredCut |
A |
blockSize |
A |
gcInterval |
A |
save |
A |
file |
A |
verbose |
A |
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()
.
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.
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.
## Not run:
# 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")
## End(Not run)
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