Given expression data from several sets and basic network parameters, the function calculates connectivity of each gene to a given number of nearest neighbors in each set.
nearestNeighborConnectivityMS(multiExpr, nNeighbors = 50, power = 6,
type = "unsigned", corFnc = "cor", corOptions = "use = 'p'",
blockSize = 1000,
sampleLinks = NULL, nLinks = 5000, setSeed = 36492,
verbose = 1, indent = 0)expression data in multi-set format. A vector of lists, one list per set. In each list
there must be a component named data whose content
is a matrix or dataframe or array of dimension 2 containing the expression data. Rows correspond to
samples and columns to genes (probes).
number of nearest neighbors to use.
soft thresholding power for network construction. Should be a number greater than 1.
a character string encoding network type. Recognized values are (unique abbreviations of)
"unsigned", "signed", and "signed hybrid".
character string containing the name of the function to calculate correlation. Suggested
functions include "cor" and "bicor".
further argument to the correlation function.
correlation calculations will be split into square blocks of this size, to prevent running out of memory for large gene sets.
logical: should network connections be sampled (TRUE) or should all
connections be used systematically (FALSE)?
number of links to be sampled. Should be set such that nLinks * nNeighbors be
several times larger than the number of genes.
seed to be used for sampling, for repeatability. If a seed already exists, it is saved before the sampling starts and restored after.
integer controlling the level of verbosity. 0 means silent.
integer controlling indentation of output. Each unit above 0 adds two spaces.
A matrix in which columns correspond to sets and rows to genes; each entry contains the nearest neighbor connectivity of the corresponding gene.
Connectivity of gene i is the sum of adjacency strengths between gene i
and other genes; in
this case we take the nNeighbors nodes with the highest connection strength to gene i. The
adjacency strengths are calculated by correlating the given expression data using the function supplied
in corFNC and transforming them into adjacency according to the given network type and
power.