Calculation of (signed) eigengene-based connectivity, also known as module membership.
signedKME(
datExpr,
datME,
exprWeights = NULL,
MEWeights = NULL,
outputColumnName = "kME",
corFnc = "cor",
corOptions = "use = 'p'")a data frame containing the gene expression data. Rows correspond to samples and columns to genes. Missing values are allowed and will be ignored.
a data frame containing module eigengenes. Rows correspond to samples and columns to module eigengenes.
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.
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.
a character string specifying the prefix of column names of the output.
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.
character string specifying additional arguments to be passed to the function given
by corFnc. Use "use = 'p', method = 'spearman'" to obtain Spearman correlation.
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.
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.
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