createStatisticalAdjacencyList {MetNet} | R Documentation

## Create a list of statistical adjacency matrices

### Description

The function infers adjacency matrix topologies from statistical methods and returns matrices of these networks in a list. The function 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). The function returns a list of adjacency matrices that are defined by `model`.

### Usage

    createStatisticalAdjacencyList(x, model, ...)

### Arguments

`x` | matrix that contains intensity values of features/metabolites (rows) per sample (columns).
`model,` | character vector containing the methods that will be used ("lasso", "randomForest", "clr", "aracne", "pearson", "pearson_partial", "pearson_semipartial","spearman", "spearman_partial", "spearman_semipartial", "bayes")
`...` | parameters passed to the functions `lasso`, `randomForest`, `clr`, `aracne`, `correlation` and/or `bayes`

### Details

`createStatisticalAdjacencyList` calls the function `lasso`, `randomForest`, `clr`, `aracne`, `correlation` (for "pearson", "pearson_partial", "pearson_semipartial", "spearman", "spearman_partial", "spearman_semipartial") and/or `bayes` as specified by `model`. It will create adjacency matrices using the specified methods and will return a list containing the unweighted adjacency matrix (if `model` is of length 1) or append these unweighted adjacency matrices to a list (if `model` is of length > 1). Internally `x` will be z-scaled and the z-scaled object will be used in `lasso`, `clr` and/or `aracne`.

### Value

list containing the respective adjacency matrices specified by `model`

### 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)
    createStatisticalAdjacencyList(x, c("pearson", "spearman"))

[Package MetNet version 1.2.0 Index]