score.multiple.vertex-methods | R Documentation |
Methods to compute score functions for multiple vertices of the graph
## S4 method for signature 'graph' NN.score(RW, x, x.pos, auto = FALSE, norm = TRUE) ## S4 method for signature 'matrix' NN.score(RW, x, x.pos, auto = FALSE, norm = TRUE) ## S4 method for signature 'graph' KNN.score(RW, x, x.pos, k = 3, auto = FALSE, norm = TRUE) ## S4 method for signature 'matrix' KNN.score(RW, x, x.pos, k = 3, auto = FALSE, norm = TRUE) ## S4 method for signature 'graph' eav.score(RW, x, x.pos, auto = FALSE, norm = TRUE) ## S4 method for signature 'matrix' eav.score(RW, x, x.pos, auto = FALSE, norm = TRUE) ## S4 method for signature 'graph' WSLD.score(RW, x, x.pos, d = 2, auto = FALSE, norm = TRUE) ## S4 method for signature 'matrix' WSLD.score(RW, x, x.pos, d = 2, auto = FALSE, norm = TRUE)
RW |
matrix. It must be a kernel matrix or a symmetric matrix expressing the similarity between nodes |
x |
vector of integer. Indices corresponding to the elements 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) |
norm |
boolean. If TRUE (def.) the scores are normalized between 0 and 1. |
The methods compute the scores for multiple vertices according to NN, KNN, Empirical Average or WSLD score (see reference for bibliographic details). Note that the argument x indicates the set of nodes for which the score must be computed. The vector x represents the indices of the rows of the matrix RW corresponding to the vertices for which the scores must be computed. If x = 1:nrow(RW) the scores for all the vertices of the graph are computed.
NN.score
: a numeric vector with the NN scores of the vertices. The names of the vector correspond to the indices x
KNN.score
: a numeric vector with the KNN scores of the vertices. The names of the vector correspond to the indices x
eav.score
: a numeric vector with the Empirical Average score of the vertices. The names of the vector correspond to the indices x
WSLD.score
: a numeric vector with the Weighted Sum with Linear Decay score (WSLD) of the vertices. The names of the vector correspond to the indices x
signature(RW = "graph")
NN.score
computes the NN score for multiple vertices using a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
KNN.score
computes the KNN score for multiple vertices using a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
eav.score
computes the Empirical Average score for multiple verticesusing a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
WSLD.score
computes the Weighted Sum with Linear Decay score for multiple vertices using a graph of class graph
(hence including objects of class graphAM and graphNEL from the package graph)
signature(RW = "matrix")
NN.score
computes the NN score for multiple vertices using a kernel matrix or a symmetric matrix expressing the similarity between nodes
KNN.score
computes the KNN score for multiple vertices using a kernel matrix or a symmetric matrix expressing the similarity between nodes
eav.score
computes the Empirical Average score multiple for vertices using a kernel matrix or a symmetric matrix expressing the similarity between nodes
WSLD.score
computes the Weighted Sum with Linear Decay score for multiple vertices using a kernel matrix or a symmetric matrix expressing the similarity between nodes
Re M, Mesiti M, Valentini G: A fast ranking algorithm for predicting gene functions in biomolecular networks. IEEE ACM Trans Comput Biol Bioinform 2012, 9(6):1812-1818.
Insuk Lee, Bindu Ambaru, Pranjali Thakkar, Edward M. Marcotte, and Seung Y. Rhee. Nature Biotechnology 28, 149-156, 2010
Methods for scoring a single vertex
# 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-scores computed directly on the STRING matrix s <- NN.score(Yeast.STRING.data, 1:n, ind.pos); # NN-scores computed on the 1 step and 2-step random walk kernel matrix K <- rw.kernel(Yeast.STRING.data); sK <- NN.score(K, 1:n, ind.pos); K2 <- p.step.rw.kernel(K, p=2); sK2 <- NN.score(K2, 1:n, ind.pos); # WSLD-scores computed directly on the STRING matrix s <- WSLD.score(Yeast.STRING.data, 1:n, ind.pos); # WSLD-scores computed on the 1 step and 2-step random walk kernel matrix sK <- WSLD.score(K, 1:n, ind.pos); sK2 <- WSLD.score(K2, 1:n, ind.pos);
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