createStatisticalAdjacency {MetNet}R Documentation

Create statistical adjacency matrix

Description

createStatisticalAdjacency creates a consensus adjacency matrix given the models to use.

Usage

createStatisticalAdjacency(x, model, threshold=1, ...)

Arguments

x

matrix that contains intensity values of features/metabolites (rows) per sample (columns).

model,

character, vector containing the model that will be used ("lasso", "randomForest", "clr", "aracne", "pearson", "pearson_partial", "pearson_semipartial","spearman", "spearman_partial", "spearman_semipartial", "bayes")

threshold

numeric, when combining the adjacency matrices the threshold parameter defines if an edge is reported or not. For method="central.graph" threshold is set to 1 by default. For other values of method, the value should be carefully defined by the user. If threshold is set to NULL (default), it will be set to 1 internally.

...

parameters passed to the functions lasso, randomForest, clr, aracne, correlation, bayes and/or consensusAdjacency

Details

createStatisticalAdjacency is a wrapper function for the functions createStatisticalAdjacencyList and consensusAdjacency. See ?createStatisticalAdjacencyList and ?consensusAdjacency for further details. The function createStatisticalAdjacencyList includes functionality to caluclate adjacency matrices based on LASSO (L1 norm)-regression, random forests, context likelihood of relatedness (CLR), the algorithm for the reconstruction of accurate cellular networks (ARACNE), Pearson correlation (also partial and semipartial), Spearman correlation (also partial and semipartial) and Constraint-based structure learning (Bayes).

Value

matrix, containing binary values if a connection is present or not

Author(s)

Thomas Naake, thomasnaake@googlemail.com

Examples

data("x_test", package="MetNet")
x <- x_test[, 3:dim(x_test)[2]]
x <- as.matrix(x)
createStatisticalAdjacency(x, c("pearson", "spearman"))

[Package MetNet version 1.2.0 Index]