score.single.vertex-methods | R Documentation |
Methods to compute score functions applied to a single vertex of the graph
## S4 method for signature 'graph' single.NN.score(RW, x, x.pos, auto = FALSE) ## S4 method for signature 'matrix' single.NN.score(RW, x, x.pos, auto = FALSE) ## S4 method for signature 'graph' single.KNN.score(RW, x, x.pos, k = 3, auto = FALSE) ## S4 method for signature 'matrix' single.KNN.score(RW, x, x.pos, k = 3, auto = FALSE) ## S4 method for signature 'graph' single.eav.score(RW, x, x.pos, auto = FALSE) ## S4 method for signature 'matrix' single.eav.score(RW, x, x.pos, auto = FALSE) ## S4 method for signature 'graph' single.WSLD.score(RW, x, x.pos, d = 2, auto = FALSE) ## S4 method for signature 'matrix' single.WSLD.score(RW, x, x.pos, d = 2, auto = FALSE)
RW |
matrix. It must be a kernel matrix or a symmetric matrix expressing the similarity between nodes |
x |
integer. Index corresponding to the element of the RW matrix for which the score must be computed |
x.pos |
vector of integer. Indices of the positive elements of the RW matrix |
k |
integer. Number of the k nearest neighbours to be considered |
d |
integer. Coefficient of linear decay (def. 2) |
auto |
boolean. If TRUE the components K(x,x) + K(x_i,x_i) are computed, otherwise are discarded (default) |
single.NN.score
computes the NN score for a single vertex:
score(x) = - \min_{x_i \in V_C} ( K(x,x) + K(x_i,x_i) -2 K(x,x_i))
where V_C is the set of positive vertices.
single.KNN.score
compute KNN score for a single vertex:
score(x) = - ∑_{k \; nearest \; x_i \in V_C} ( K(x,x) + K(x_i,x_i) - 2 K(x,x_i))
single.eav.score
computes the Empirical Average score for a single vertex:
score(x) = - K(x,x) + \frac{2}{|V_C|} * ∑_{x_i \in V_C} K(x,x_i)
single.WSLD.score
computes the WSLD score for a single vertex:
Let K(x, x_{jk}) be the kth rank order index w.r.t. x_j \in V_C, and m=|V_C|, then:
score(x) = \max_{x_i \in V_C} K(x,x_i) + ∑_{k=2}^m [(1/(d * (k-1))) * K(x, x_{jk})]
single.NN.score
: the NN score of the vertex
single.KNN.score
: the KNN score of the vertex
single.eav.score
: the Empirical Average score of the vertex
single.WSLD.score
: the Weighted Sum with Linear Decay score (WSLD) of the vertex
signature(RW = "graph")
single.NN.score
computes the NN score for a single vertex using a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
single.KNN.score
computes the KNN score for a single vertex using a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
single.eav.score
computes the Empirical Average score for a single vertex using a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
single.WSLD.score
computes the Weighted Sum with Linear Decay score for a single vertex using a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
signature(RW = "matrix")
single.NN.score
computes the NN score for a single vertex using a kernel matrix or a symmetric matrix expressing the similarity between nodes
single.KNN.score
computes the KNN score for a single vertex using a kernel matrix or a symmetric matrix expressing the similarity between nodes
single.eav.score
computes the Empirical Average score using a kernel matrix or a symmetric matrix expressing the similarity between nodes
single.WSLD.score
computes the Weighted Sum with Linear Decay score for a single vertex using a kernel matrix or a symmetric matrix expressing the similarity between nodes
Methods for scoring multiple vertices
# Computation of scores using STRING data with respect to # the FunCat category 11.02.01 rRNA synthesis library(bionetdata); data(Yeast.STRING.data); data(Yeast.STRING.FunCat); labels <- Yeast.STRING.FunCat[,"11.02.01"]; n <- length(labels); ind.pos <- which(labels==1); # NN-score computed directly on the STRING matrix on the first yeast gene YJR121W s <- single.NN.score(Yeast.STRING.data, 1, ind.pos); # NN-score computed on the 1 step and 2-step random walk kernel matrix K <- rw.kernel(Yeast.STRING.data); sK <- single.NN.score(K, 1, ind.pos); K2 <- p.step.rw.kernel(K, p=2); sK2 <- single.NN.score(K2, 1, ind.pos); # WSLD-score computed directly on the STRING matrix on the first yeast gene YJR121W s <- single.WSLD.score(Yeast.STRING.data, 1, ind.pos); # WSLD-scores computed on the 1 step and 2-step random walk kernel matrix sK <- single.WSLD.score(K, 1, ind.pos); sK2 <- single.WSLD.score(K2, 1, ind.pos);
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