# score.multiple.vertex-methods: Multiple vertex score functions In RANKS: Ranking of Nodes with Kernelized Score Functions

## Description

Methods to compute score functions for multiple vertices of the graph

## Usage

 ``` 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) ```

## Arguments

 `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.

## Details

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.

## Value

`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

## Methods

`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

## References

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`
 ``` 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); # NN-scores computed directly on the STRING matrix s <- NN.score(Yeast.STRING.data, 1:n, ind.pos); ## Not run: # 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); ## End(Not run) ```