WGCNA: pickHardThreshold – R documentation

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pickHardThreshold

Analysis of scale free topology for hard-thresholding.

Description

Analysis of scale free topology for multiple hard thresholds. The aim is to help the user pick an appropriate threshold for network construction.

Usage

pickHardThreshold(
  data, 
  dataIsExpr,
  RsquaredCut = 0.85, 
  cutVector = seq(0.1, 0.9, by = 0.05), 
  moreNetworkConcepts = FALSE,
  removeFirst = FALSE, nBreaks = 10, 
  corFnc = "cor", corOptions = "use = 'p'")

pickHardThreshold.fromSimilarity(
    similarity,
    RsquaredCut = 0.85, 
    cutVector = seq(0.1, 0.9, by = 0.05),
    moreNetworkConcepts=FALSE, 
    removeFirst = FALSE, nBreaks = 10)

Arguments

`data`	expression data in a matrix or data frame. Rows correspond to samples and columns to genes.
`dataIsExpr`	logical: should the data be interpreted as expression (or other numeric) data, or as a similarity matrix of network nodes?
`similarity`	similarity matrix: a symmetric matrix with entries between -1 and 1 and unit diagonal.
`RsquaredCut`	desired minimum scale free topology fitting index R^2.
`cutVector`	a vector of hard threshold cuts for which the scale free topology fit indices are to be calculated.
`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 network concepts, see Horvath and Dong (2008). PloS Comp Biol.
`removeFirst`	should the first bin be removed from the connectivity histogram?
`nBreaks`	number of bins in connectivity histograms
`corFnc`	a character string giving the correlation function to be used in adjacency calculation.
`corOptions`	further options to the correlation function specified in `corFnc`.

Details

The function calculates unsigned networks by thresholding the correlation matrix using thresholds given in cutVector. For each power the scale free topology fit index is calculated and returned along with other information on connectivity.

Value

A list with the following components:

cutEstimate

estimate of an appropriate hard-thresholding cut: the lowest cut for which the scale free topology fit R^2 exceeds RsquaredCut. If R^2 is below RsquaredCut for all cuts, NA is returned.

fitIndices

a data frame containing the fit indices for scale free topology. The columns contain the hard threshold, Student p-value for the correlation threshold, adjusted R^2 for the linear fit, the linear coefficient, adjusted R^2 for a more complicated fit models, mean connectivity, median connectivity and maximum connectivity. If input moreNetworkConcepts is TRUE, 3 additional columns containing network density, centralization, and heterogeneity.

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

Horvath S, Dong J (2008) Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol 4(8): e1000117