statistical: Create an 'AdjacencyMatrix' object containing assays of...

In tnaake/MetNet: Inferring metabolic networks from untargeted high-resolution mass spectrometry data

Create an 'AdjacencyMatrix' object containing assays of adjacency matrices from statistical methods

Description

The function 'statitical' infers adjacency matrix topologies from statistical methods and returns matrices of these networks in an 'AdjacencyMatrix' object. The function includes functionality to calculate 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), Spearman correlation (also partial) and score-based structure learning (Bayes). The function returns an 'AdjacencyMatrix' object of adjacency matrices that are defined by 'model'.

Usage

statistical(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"', '"spearman"', '"spearman_partial"', 'ggm', '"bayes"')
...	parameters passed to the functions 'lasso', 'randomForest', 'clr', 'aracne', 'correlation' and/or 'bayes'

Details

The function 'statistical' includes functionality to calculate 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), Spearman correlation (also partial) and Constraint-based structure learning (Bayes).

'statistical' calls the function 'lasso', 'randomForest', 'clr', 'aracne', 'correlation' (for '"pearson"', '"pearson_partial"', '"spearman"', '"spearman_partial"', '"ggm"') and/or 'bayes' as specified by 'model'. It will create adjacency matrices using the specified methods and will return an 'AdjacencyMatrix' containing the weighted adjacency matrices in the 'assays' slot.

Internally 'x' will be z-scaled and the z-scaled object will be used in 'lasso', 'clr' and/or 'aracne'.

The slot 'type' is set to 'statistical'. The slot 'directed' is set to 'TRUE' if the methods '"lasso"', '"randomForest"', or '"bayes"' were used, otherwise 'directed' is set to 'FALSE'. The slot 'threshold' is set to 'FALSE'.

Value

'AdjacencyMatrix' containing the respective adjacency matrices in the 'assay' slot as specified by 'model'

Author(s)

Thomas Naake, [e-mail]

Examples

data("x_test", package = "MetNet")
x <- x_test[1:10, 3:ncol(x_test)]
x <- as.matrix(x)
statistical(x = x, model = c("pearson", "spearman"))
statistical(x = x, model = c("pearson", "spearman"), p.adjust = "BH")

tnaake/MetNet documentation built on Nov. 4, 2024, 4:45 a.m.