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signedKME

Signed eigengene-based connectivity

Description

Calculation of (signed) eigengene-based connectivity, also known as module membership.

Usage

signedKME(
  datExpr, 
  datME, 
  exprWeights = NULL,
  MEWeights = NULL,
  outputColumnName = "kME", 
  corFnc = "cor", 
  corOptions = "use = 'p'")

Arguments

datExpr

a data frame containing the gene expression data. Rows correspond to samples and columns to genes. Missing values are allowed and will be ignored.

datME

a data frame containing module eigengenes. Rows correspond to samples and columns to module eigengenes.

exprWeights

optional weight matrix of observation weights for datExpr, of the same dimensions as datExpr. If given, the weights must be non-negative and will be passed on to the correlation function given in argument corFnc as argument weights.x.
MEWeights

optional weight matrix of observation weights for datME, of the same dimensions as datME. If given, the weights must be non-negative and will be passed on to the correlation function given in argument corFnc as argument weights.y.
outputColumnName

a character string specifying the prefix of column names of the output.

corFnc

character string specifying the function to be used to calculate co-expression similarity. Defaults to Pearson correlation. Any function returning values between -1 and 1 can be used.

corOptions

character string specifying additional arguments to be passed to the function given by corFnc. Use "use = 'p', method = 'spearman'" to obtain Spearman correlation.

Details

Signed eigengene-based connectivity of a gene in a module is defined as the correlation of the gene with the corresponding module eigengene. The samples in datExpr and datME must be the same.

Value

A data frame in which rows correspond to input genes and columns to module eigengenes, giving the signed eigengene-based connectivity of each gene with respect to each eigengene.

Author(s)

Steve Horvath

References

Dong J, Horvath S (2007) Understanding Network Concepts in Modules, BMC Systems Biology 2007, 1:24

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

WGCNA

Weighted Correlation Network Analysis

v1.70-3
GPL (>= 2)
Authors
Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang
Initial release
2021-02-17

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