plotSoftPower {RegEnrich} | R Documentation |
Plot soft power and corresponding scale free topology fitting index to find a proper soft power for WGCNA analysis.
plotSoftPower( expr, rowSample = FALSE, weights = NULL, powerVector = c(seq(10), seq(12, 20, by = 2)), RsquaredCut = 0.85, networkType = "unsigned", removeFirst = FALSE, nBreaks = 10, corFnc = WGCNA::cor, corOptions = list(use = "p") )
expr |
Gene expression data, either a matrix or a data frame. By default, each row represents a gene, each column represents a sample. |
rowSample |
logic. If |
weights, |
optional observation weights for |
powerVector |
a vector of soft thresholding powers for which the scale free topology fit indices are to be calculated. |
RsquaredCut |
desired minimum scale free topology fitting index R^2. The default is 0.85. |
networkType |
character, network type. Allowed values are
(unique abbreviations of) "unsigned" (default), "signed", "signed hybrid".
See |
removeFirst, |
should the first bin be removed from the connectivity histogram? The default is FALSE. |
nBreaks, |
number of bins in connectivity histograms. The default is 10. |
corFnc, |
correlation function to be used in adjacency calculation.
The default is the |
corOptions, |
a named list of options to the correlation function specified in corFnc. The default is list(use = "p"). |
a list of three elements: powerEstimate
, fitIndices
,
and plot
.
powerEstimate
is an estimate of an appropriate soft-thresholding
power. fitIndices
is a data frame containing the fit indices for
scale free topology. The plot
is a ggplot object.
data(Lyme_GSE63085) log2FPKM = log2(Lyme_GSE63085$FPKM + 1) log2FPKMhi = log2FPKM[rowMeans(log2FPKM) >= 10^-3, , drop = FALSE] log2FPKMhi = head(log2FPKMhi, 3000) # First 3000 genes for example softP = plotSoftPower(log2FPKMhi, RsquaredCut = 0.85) print(softP)