Methods to compute score functions for multiple vertices of the graph
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## 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):18121818.
Insuk Lee, Bindu Ambaru, Pranjali Thakkar, Edward M. Marcotte, and Seung Y. Rhee. Nature Biotechnology 28, 149156, 2010
Methods for scoring a single vertex
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  # 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);
# NNscores computed directly on the STRING matrix
s < NN.score(Yeast.STRING.data, 1:n, ind.pos);
## Not run:
# NNscores computed on the 1 step and 2step 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);
# WSLDscores computed directly on the STRING matrix
s < WSLD.score(Yeast.STRING.data, 1:n, ind.pos);
# WSLDscores computed on the 1 step and 2step random walk kernel matrix
sK < WSLD.score(K, 1:n, ind.pos);
sK2 < WSLD.score(K2, 1:n, ind.pos);
## End(Not run)

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