weighted.score.single.vertex-methods | R Documentation |
Methods to compute weighted score functions applied to a single vertex of the graph
## S4 method for signature 'graph' single.NN.w.score(RW, x, x.pos, w) ## S4 method for signature 'matrix' single.NN.w.score(RW, x, x.pos, w) ## S4 method for signature 'graph' single.KNN.w.score(RW, x, x.pos, w, k = 3) ## S4 method for signature 'matrix' single.KNN.w.score(RW, x, x.pos, w, k = 3) ## S4 method for signature 'graph' single.eav.w.score(RW, x, x.pos, w, auto = FALSE) ## S4 method for signature 'matrix' single.eav.w.score(RW, x, x.pos, w, 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 |
w |
vector of numeric. Its elements represent the initial likelihood that the nodes of the graph belong to the class under study. The elements of w correspond to the columns of RW and the length of w and the number of columns of RW must be equal. |
k |
integer. Number of the k nearest neighbours to be considered |
auto |
boolean. If TRUE the components K(x,x) + K(x_i,x_i) are computed, otherwise are discarded (default) |
single.NN.w.score
computes the weighted NN score for a single vertex:
score(x) = - \min_{x_i \in V_C} -2 K(x,x_i)) * w(x_i)
where V_C is the set of positive vertices, and w(x_i) is the weight associated to the node x_i
single.KNN.w.score
compute the weighted KNN score for a single vertex:
score(x) = ∑_{k \; nearest \; x_i \in V_C} 2 K(x,x_i) * w(x_i)
single.eav.score
computes the weighted Empirical Average score for a single vertex:
score(x) = - K(x,x) * w(x) + \frac{2}{(∑_{x_i \in x.pos} w(x_i))} * ∑_{x_i \in x.pos} K(x,x_i) * w(x_i)
single.NN.w.score
: the weighted NN score of the vertex
single.KNN.w.score
: the weighted KNN score of the vertex
single.eav.w.score
: the weighted Empirical Average score of the vertex
signature(RW = "graph")
single.NN.w.score
computes the weighted 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.w.score
computes the weighted 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.w.score
computes the weighted 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)
signature(RW = "matrix")
single.NN.w.score
computes the weighted NN score for a single vertex using a kernel matrix or a symmetric matrix expressing the similarity between nodes
single.KNN.w.score
computes the weighted KNN score for a single vertex using a kernel matrix or a symmetric matrix expressing the similarity between nodes
single.eav.score
computes the weighted Empirical Average score using a kernel matrix or a symmetric matrix expressing the similarity between nodes
Methods for scoring a single vertex
Methods for scoring multiple vertices - weighted version
# 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.w.score(Yeast.STRING.data, 1, ind.pos, w=labels); # NN-score weighted computed directly on the STRING matrix on the first yeast gene YJR121W, # using this time random weights for the value of positive nodes w <- runif(n); s <- single.NN.w.score(Yeast.STRING.data, 1, ind.pos, w=w); # NN-score weighted computed on the 1 step and 2-step random walk kernel matrix K <- rw.kernel(Yeast.STRING.data); sK <- single.NN.w.score(K, 1, ind.pos, w); K2 <- p.step.rw.kernel(K, p=2); sK2 <- single.NN.w.score(K2, 1, ind.pos, w);
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.