adjacency | R Documentation |
Calculates (correlation or distance) network adjacency from given expression data or from a similarity.
adjacency(datExpr,
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)
datExpr |
data frame containing expression data. Columns correspond to genes and rows to samples. |
similarity |
a (signed) similarity matrix: square, symmetric matrix with entries between -1 and 1. |
selectCols |
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. |
type |
network type. Allowed values are (unique abbreviations of) |
power |
soft thresholding power. |
corFnc |
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. |
corOptions |
character string or a list specifying additional arguments to be passed to the function given
by |
weights |
optional observation weights for |
distFnc |
character string specifying the function to be used to calculate co-expression
similarity for distance networks. Defaults to the function |
distOptions |
character string or a list specifying additional arguments to be passed to the function given
by |
weightArgNames |
character vector of length 2 giving the names of the arguments to |
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.
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
.
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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