WA.int {NetInt}

Weighted Average (WA) network integration

Description

It performs the WA integration between networks.

Note that this function puts more weight (alpha parameter) for networks with associated larger M. The alphas are computed by averaging across the alpha of each class, and hence a unique integrated network is available for all the considered classes.

Usage

WA.int(M, logint = FALSE, ...)

Arguments

M

a numeric matrix with the values of the metric used to compute the alpha coefficients for each class and for each network. Rows correspond to networks and columns to classes. Element (i,j) of the matrix corresponds to the value of the metric (e.g. AUC) for the ith network and the jth class.

logint

logic. If TRUE the mean values m are log transformed: -log(1-m), otherwise a linear integration is performed (def: FALSE).

...

a list of numeric matrices. These must be named matrices representing adjacency matrices of the networks. Matrices may have different dimensions, but corresponding elements in different matrices must have the same name.

Value

A list with two elements:

• WA : the matrix resulting from WA

• alpha : a numeric vector with the weight coefficients of the networks

Examples

# Create three example networks of different size
set.seed(123);
A1 <- matrix(runif(100, min = 0, max = 1), nrow = 10);
A1[lower.tri(A1)] = t(A1)[lower.tri(A1)];
diag(A1) <- 0;
rownames(A1) <- colnames(A1) <- sample(LETTERS, 10);

A2 <- matrix(runif(49, min = 0, max = 1), nrow = 7);
A2[lower.tri(A2)] = t(A2)[lower.tri(A2)];
diag(A2) <- 0;
rownames(A2) <- colnames(A2) <- rownames(A1)[1:7];

A3 <- matrix(runif(100, min = 0, max = 1), nrow = 10);
A3[lower.tri(A3)] = t(A3)[lower.tri(A3)];
diag(A3) <- 0;
rownames(A3) <- colnames(A3) <- c(rownames(A1)[1:5], c("A", "B", "Z", "K", "Q"));

# Create random matrix of accuracy metrics (considering 3 classes)
M <- matrix(runif(9, min = 0, max = 1), ncol = 3);

# Integrate networks using Weighted Average (WA) method
A_int <- WA.int(M, logint=TRUE, A1, A2, A3);