Usage
pickSoftThreshold(
datExpr,
RsquaredCut = 0.85,
powerVector = c(seq(1, 10, by = 1), seq(12, 20, by = 2)),
removeFirst = FALSE, nBreaks = 10, blockSize = 1000,
corFnc = "cor", corOptions = "use = 'p'",
networkType = "unsigned",
moreNetworkConcepts = FALSE,
verbose = 0, indent = 0)
pickSoftThreshold.fromSimilarity(
similarity,
RsquaredCut = 0.85, powerVector = c(seq(1, 10, by = 1), seq(12, 20, by = 2)),
removeFirst = FALSE, nBreaks = 10, blockSize = 1000,
networkType = "unsigned", moreNetworkConcepts=FALSE, verbose = 0, indent = 0)Arguments
datExpr
expression data in a matrix or data frame. Rows correspond to samples and columns to
genes.
similarity
a symmetric matrix with entries between -1 and 1 and unit diagonal.
RsquaredCut
desired minimum scale free topology fitting index $R^2$.
powerVector
a vector of soft thresholding powers for which the scale free topology fit indices
are to be calculated.
removeFirst
should the first bin be removed from the connectivity histogram?
nBreaks
number of bins in connectivity histograms
blockSize
block size into which the calculation of connectivity should be broken up. If R runs
into memory problems, decrease this value.
corFnc
a character string giving the correlation function to be used in adjacency calculation.
corOptions
further options to the correlation function specified in corFnc.
networkType
network type. Allowed values are (unique abbreviations of) "unsigned",
"signed", "signed hybrid". See adjacency. moreNetworkConcepts
logical: should additional network concepts be calculated? If TRUE, the
function will calculate how the network density, the network heterogeneity, and the network centralization
depend on the power. For the definition of these additional n
verbose
integer level of verbosity. Zero means silent, higher values make the output
progressively more and more verbose.
indent
indentation for diagnostic messages. Zero means no indentation, each unit adds
two spaces.