AdjacencyMatrix {MetNet} | R Documentation

## Wrapper to create an instance of S4 class AdjacencyMatrix

### Description

The function 'AdjacencyMatrix' will create an object of type 'AdjacencyMatrix'.

### Usage

    AdjacencyMatrix(
      adj_l,
      rowData,
      type = c("structural", "statistical", "combine"),
      directed = c(TRUE, FALSE),
      thresholded = c(TRUE, FALSE)
    )

### Arguments

`adj_l` | 'list' of adjacency matrices
`rowData` | 'data.frame', containing information on the features
`type` | 'character', either '"structural"', '"statistical"', or '"combine"'
`directed` | 'logical', if the adjacency matrix underlying the graph is directed or undirected
`thresholded` | 'logical', if the functions 'rtCorrection' or 'threshold' were applied on the 'structural' or 'statistical' 'AdjacencyMatrix' objects

### Details

'adj_l' is a list of adjacency matrices. The adjacency matrices have identical dimensions and 'dimnames' and each adjacency matrix has the same number of columns and rows and identical 'rownames' and 'colnames'. 'rowData' will be also used for the 'colData' slot (since the 'rownames' and 'colnames' are identical).

### Value

object of S4 class 'AdjacencyMatrix'

### Accessors

- The 'AdjacencyMatrix' class extends the [SummarizedExperiment::SummarizedExperiment] class and inherits all its accessors and replacement methods.

- The 'type' accessor returns the 'type' ('"structural"', '"statistical"', '"combine"') slot.

- The 'directed' accessor returns the 'directed' ('logical' of length 1) slot.

- The 'thresholded' accessor returns the 'thresholded' ('logical' of length 1) slot.

### Author(s)

Thomas Naake, thomasnaake@googlemail.com

### Examples

    binary <- matrix(0, ncol = 10, nrow = 10)
    transformation <- matrix("", ncol = 10, nrow = 10)
    mass_difference <- matrix("", ncol = 10, nrow = 10)

    rownames(binary) <- rownames(transformation) <- rownames(mass_difference) <- paste("feature", 1:10)
    colnames(binary) <- rownames(transformation) <- rownames(mass_difference) <- paste("feature", 1:10)

    binary[5, 4] <- 1
    transformation[5, 4] <- "glucose addition"
    mass_difference[5, 4] <- "162"

    ## create adj_l and rowData
    adj_l <- list(binary = binary, transformation = transformation,
        mass_difference = mass_difference)
    rowData <- DataFrame(features = rownames(binary),
        row.names = rownames(binary))

    AdjacencyMatrix(adj_l = adj_l, rowData = rowData, type = "structural",
        directed = TRUE, thresholded = FALSE)

[Package MetNet version 1.12.2 Index]