structural: Create adjacency matrix based on m/z (molecular weight)...

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

Create adjacency matrix based on m/z (molecular weight) difference

Description

The function 'structural' infers an unweighted adjacency matrix using differences in m/z values that are matched against a 'data.frame' ('transformation' of calculated theoretical differences of loss/addition of functional groups. 'structural' returns an 'AdjacencyMatrix' object containing the unweighted 'numeric' 'matrix' (assay 'binary'), together with one or multiple 'character' matrices containing e.g. the type of loss/addition or the mass differences. The creation of the additional 'character' matrices is controlled by the 'var' argument that specifies the column in 'transformation' as the data source for the adjacency matrices.

Usage

structural(x, transformation, var = character(), ppm = 5, directed = FALSE)

Arguments

x	'matrix' or 'data.frame', where columns are the samples and the rows are features (metabolites), cell entries are intensity values. 'x' contains the column '"mz"' that has the m/z information (numerical values) for the calculation of mass differences between features
transformation	'data.frame', containing the columns '"group"', and '"mass"' that will be used for detection of transformation of (functional) groups
var	'character' corresponding to column names in 'transformation'
ppm	'numeric(1)', mass accuracy of m/z features in parts per million (ppm)
directed	'logical(1)', if 'TRUE', absolute values of m/z differences will be taken to query against 'transformation' (irrespective the sign of 'mass') and undirected adjacency matrices will be returned as the respective assays. This means, if there is a negative mass in 'transformation[, "mass"]', this negative mass will not be reported. If 'FALSE', directed adjacency matrices will be returned with links reported that match the transformations defined in 'transformation' (respecting the sign of 'mass'). The 'directed' slot of the returned 'AdjacencyMatrix' object will contain the information on 'directed'.

Details

'structural' accesses the column '"mz"' of 'x' to infer structural topologies based on the functional groups defined by 'transformation'. To account for the mass accuracy of the dataset 'x', the user can specify the accuracy of m/z features in parts per million (ppm) by the 'ppm' argument. The m/z values in the '"mz"' column of 'x'" will be converted to m/z ranges according to the 'ppm' argument (default 'ppm = 5').

The returned 'AdjacencyMatrix' object contains the assays 'binary' and additional adjacency matrices depending on the 'var' parameter. The assay 'binary' stores the 'numeric' 'matrix' with binary edges inferred from mass differences. The 'var' parameter has to be set according to the column names in 'transformation'. E.g. if the 'transformation' object contains a '"group"' column that stores the name of the transformation, setting 'var = "group"' will create an assay '"group"' that contains the adjacency matrices where the entries correspond to the '"group"' information for the respective feature pairs.

The 'type' slot is set to 'structural'. The 'directed' slot is set accordingly to the 'directed' argument of the function 'structural'. The 'thresholded' slot is set to 'FALSE'.

Value

'AdjacencyMatrix' object. The object will store the adjacency matrix/matrices in the assay slot/slots. The numerical (unweighted) adjacency matrix inferred from mass differences is stored as the assay '"binary"'. Depending on the 'var' argument, there are additional adjacency matrices stored in the assay slot.

Author(s)

Thomas Naake, [e-mail] and Liesa Salzer, [e-mail]

Examples

data("x_test", package = "MetNet")
transformation <- rbind(
    c("Monosaccharide (-H2O)", "C6H10O5", "162.0528234315"),
    c("Disaccharide (-H2O)", "C12H20O11", "340.1005614851"),
    c("Trisaccharide (-H2O)", "C18H30O15", "486.1584702945"))
transformation <- data.frame(group = transformation[, 1],
                                formula = transformation[, 2],
                                mass = as.numeric(transformation[, 3]))
am_struct <- structural(x_test, transformation, var = c("group", "mass"),
    ppm = 10, directed = TRUE)

tnaake/MetNet documentation built on Feb. 21, 2023, 12:20 p.m.