combineStructuralStatistical {MetNet} | R Documentation

## Combine structural and statistical adjacency matrix

### Description

The function `combineStructuralStatistical` takes as input the structural and statistical adjacency matrix, created in former steps, adds them together and will report a connection between metabolites in the returned when the sum exceeds the `threshold` . `combineStructuralStatistical` returns this consensus matrix supported by the structural and statistical adjacency matrices.

### Usage

    combineStructuralStatistical(structure, statistical, threshold=1)

### Arguments

`structure` | matrix containing structural adjacency matrix
`statistical` | matrix containing statistical adjacency matrix
`threshold` | numeric, threshold value to be applied to define a connection as present

### Details

The matrices will be added and a unweighted connection will be reported when the value exceeds a certain value.

### Value

a matrix containing the consensus adjacency matrix as described above harbouring connections reported by the structual and statistcal adjacency matrices.

### Author(s)

Thomas Naake, thomasnaake@googlemail.com

### Examples

    data("x_test", package="MetNet")
    x_test <- as.matrix(x_test)
    functional_groups <- rbind(
        c("Hydroxylation (-H)", "O", "15.9949146221"),
        c("Malonyl group (-H2O)", "C3H2O3", "86.0003939305"),
        c("C6H10O6", "C6H10O6", "178.0477380536"),
        c("D-ribose (-H2O) (ribosylation)", "C5H8O4", "132.0422587452"),
        c("Disaccharide (-H2O)", "C12H20O11", "340.1005614851"),
        c("Glucuronic acid (-H2O)", "C6H8O6", "176.0320879894"),
        c("Monosaccharide (-H2O)", "C6H10O5", "162.0528234315"),
        c("Trisaccharide (-H2O)", "C18H30O15", "486.1584702945"))
    functional_groups <- data.frame(group=functional_groups[,1],
                                    formula=functional_groups[,2],
                                    mass=as.numeric(functional_groups[,3]))
    struct_adj <- createStructuralAdjacency(x_test, functional_groups, ppm=5)
    stat_adj <- createStatisticalAdjacency(x_test,
        model=c("pearson", "spearman","bayes"),
        correlation_adjust="bonferroni")
    combineStructuralStatistical(struct_adj[[1]], stat_adj)

[Package MetNet version 1.2.0 Index]