score.single.vertex-methods {RANKS}

Single vertex score functions

Description

Methods to compute score functions applied to a single vertex of the graph

Usage

## 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)

Arguments

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)

Details

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) = - \sum_{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|} * \sum_{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) + \sum_{k=2}^m [(1/(d * (k-1))) * K(x, x_{jk})]

Value

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

Methods

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

See Also

Methods for scoring multiple vertices

Examples

# 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);