WGCNA (version 1.68)

adjacency: Calculate network adjacency


Calculates (correlation or distance) network adjacency from given expression data or from a similarity.


          selectCols = NULL, 
          type = "unsigned", 
          power = if (type=="distance") 1 else 6,
          corFnc = "cor", corOptions = list(use = "p"),
          weights = NULL,
          distFnc = "dist", distOptions = "method = 'euclidean'",
          weightArgNames = c("weights.x", "weights.y"))

adjacency.fromSimilarity(similarity, type = "unsigned", power = if (type=="distance") 1 else 6)



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) "unsigned", "signed", "signed hybrid", "distance".


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 list specifying additional arguments to be passed to the function given by corFnc. Use "use = 'p', method = 'spearman'" or, equivalently, list(use = 'p', method = 'spearman') to obtain Spearman correlation.


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.


character string specifying the function to be used to calculate co-expression similarity for distance networks. Defaults to the function dist. Any function returning non-negative values can be used.


character string or a list specifying additional arguments to be passed to the function given by distFnc. For example, when the function dist is used, the argument method can be used to specify various ways of computing the distance.


character vector of length 2 giving the names of the arguments to corFnc that represent weights for variable x and y. Only used if weights are non-NULL.


Adjacency matrix of dimensions ncol(datExpr) times ncol(datExpr) (or the same dimensions as similarity). If selectCols was given, the number of columns will be the length (if numeric) or sum (if boolean) of selectCols.


The argument type determines whether a correlation (type one of "unsigned", "signed", "signed hybrid"), or a distance network (type equal "distance") will 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 given in corFnc (for correlation networks) or distFnc (for distance networks), transforms the similarity according to type and raises it to power, resulting in a weighted network adjacency matrix. If selectCols is given, the corFnc function 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 selectCols.

Correlation and distance are transformed as follows: for type = "unsigned", adjacency = |cor|^power; for 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 = (1-(dist/max(dist))^2)^power.

The function adjacency.fromSimilarity inputs a similarity matrix, that is it skips the correlation calculation step but is otherwise identical.


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