Usage
softConnectivity(
datExpr,
corFnc = "cor", corOptions = "use = 'p'",
type = "unsigned",
power = if (type == "signed") 15 else 6,
blockSize = 1500,
minNSamples = NULL,
verbose = 2, indent = 0)
softConnectivity.fromSimilarity(
similarity,
type = "unsigned",
power = if (type == "signed") 15 else 6,
blockSize = 1500,
verbose = 2, indent = 0)
Arguments
datExpr
a data frame containing the expression data, with rows corresponding to samples and
columns to genes.
similarity
a similarity matrix: a square symmetric matrix with entries between -1 and 1.
corFnc
character string giving the correlation function to be used for the adjacency
calculation. Recommended choices are "cor" and "bicor", but other functions can be used as
well.
corOptions
character string giving further options to be passed to the correlation function.
type
network type. Allowed values are (unique abbreviations of) "unsigned",
"signed", "signed hybrid".
power
soft thresholding power.
blockSize
block size in which adjacency is to be calculated. Too low (say below 100) may make
the calculation inefficient, while too high may cause R to run out of physical memory and slow down the
computer. Should be chosen such that an array of doubles of size
minNSamples
minimum number of samples available for the calculation of adjacency for the
adjacency to be considered valid. If not given, defaults to the greater of ..minNSamples
(currently 4) and number of samples divided by 3. If the number of sampl
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.