softConnectivity: Calculates connectivity of a weighted network.

In WGCNA: Weighted Correlation Network Analysis

Calculates connectivity of a weighted network.

Description

Given expression data or a similarity, the function constructs the adjacency matrix and for each node calculates its connectivity, that is the sum of the adjacency to the other nodes.

Usage

softConnectivity(
  datExpr, 
  corFnc = "cor", corOptions = "use = 'p'", 
  weights = NULL,
  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.
weights	optional observation weights for datExpr to be used in correlation calculation. A matrix of the same dimensions as datExpr, containing non-negative weights. Only used with Pearson correlation.
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 (number of genes) * (block size) fits into available physical memory.
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 samples falls below this threshold, the connectivity of the corresponding gene will be returned as NA.
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.

Value

A vector with one entry per gene giving the connectivity of each gene in the weighted network.

Author(s)

Steve Horvath

References

Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17

See Also

adjacency

WGCNA documentation built on Sept. 18, 2024, 5:08 p.m.