MetNet-package {MetNet} | R Documentation

## Inferring metabolic networks from untargeted high-resolution mass spectrometry data

### Description

Inferring metabolic networks from untargeted high-resolution mass spectrometry data.

### Details

The package infers network topologies from quantitative data (intensity values) and structural data (m/z values of mass features). `MetNet` combines these two data sources to a consensus matrix.

### Author(s)

Author: NA Maintainer: NA

### References

Breitling, R. et al. Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data. 2006. Metabolomics 2: 155–164. 10.1007/s11306-006-0029-z

### 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"),
        adjust_correlation = "bonferroni")
    cons_adj <- combineStructuralStatistical(struct_adj[[1]], stat_adj)

[Package MetNet version 1.2.0 Index]