Calculates (correlation or distance) network adjacency from given expression data or from a similarity.
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data frame containing expression data. Columns correspond to genes and rows to samples.
a (signed) similarity matrix: square, symmetric matrix with entries between -1 and 1.
for correlation networks only (see below); can be used to select genes whose adjacencies will be calculated. Should be either a numeric vector giving the indices of the genes to be used, or a boolean vector indicating which genes are to be used.
network type. Allowed values are (unique abbreviations of)
soft thresholding power.
character string specifying the function to be used to calculate co-expression similarity for correlation networks. Defaults to Pearson correlation. Any function returning values between -1 and 1 can be used.
character string or a llist specifying additional arguments to be passed to the function given
character string specifying the function to be used to calculate co-expression
similarity for distance networks. Defaults to the function
character string or a list specifying additional arguments to be passed to the function given
type determines whether a correlation (
type one of
"signed hybrid"), or a distance network (
be calculated. In correlation networks the adajcency is constructed from correlations (values between -1 and
1, with high numbers meaning high similarity). In distance networks, the adjacency is constructed from
distances (non-negative values, high values mean low similarity).
The function calculates the similarity of columns (genes) in
datExpr by calling the function
corFnc (for correlation networks) or
distFnc (for distance networks),
transforms the similarity according to
type and raises it to
resulting in a weighted network adjacency matrix. If
selectCols is given, the
will be given arguments
(datExpr, datExpr[selectCols], ...); hence the returned adjacency will have
rows corresponding to all genes and columns corresponding to genes selected by
Correlation and distance are transformed as follows: for
type = "unsigned", adjacency = |cor|^power;
type = "signed", adjacency = (0.5 * (1+cor) )^power; for
type = "signed hybrid", adjacency
= cor^power if cor>0 and 0 otherwise; and for
type = "distance", adjacency =
adjacency.fromSimilarity inputs a similarity matrix, that is it skips the correlation
calculation step but is otherwise identical.
Adjacency matrix of dimensions
ncol(datExpr) (or the same dimensions
given, the number of columns will be the length (if numeric) or sum (if boolean) of
When calculated from the
datExpr, the network is always calculated among the columns of
datExpr irrespective of whether a correlation or a distance network is requested.
Peter Langfelder and Steve Horvath
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
Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54
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