MWAS_KEGG_pathways {MWASTools} | R Documentation

## Map metabolites into KEGG pathways

### Description

This function allows mapping the metabolites of interest detected by MWAS analysis onto the KEGG pathways. The function also exports a network file and an attribute file which can be imported into Cytoscape to visualize the results as a pathway-based metabolic network.

### Usage

    MWAS_KEGG_pathways(metabolites, MWAS_matrix = NULL, file_name = "KeggPaths")

### Arguments

`metabolites` | character vector containing the KEGG IDs of the metabolites of interest detected by MWAS. The order of the metabolite IDs in this vector must match the order in MWAS_matrix. Compound KEGG IDs can be obtained using the function "MS_keggFinder()" from the MetaboSignal package.
`MWAS_matrix` | numeric matrix generated with the function "MWAS_stats()". It can also be a submatrix containing only the significant metabolites, generated with the function "MWAS_filter()".
`file_name` | character vector that allows customizing the name of the exported files.

### Value

A six-column matrix indicating the KEGG pathways where each metabolite was mapped. The results are formatted as a six-column matrix containing the following information: metabolite KEGG ID (column 1), metabolite name (column 2), pathway KEGG ID (column 3), pathway name (column 4), pathway class (column 5), pathway organism (i.e. "Human"/"Not_human") (column 6).

The function also exports a network file ("KeggPaths_NetworkFile.txt") and an attribute file ("KeggPaths_AttributeFile.txt") that can be imported into Cytoscape to visualize the results as a network. The attribute file allows customizing the metabolites of interest based on a score reflecting the degree of association with the phenotype under study (i.e. log10(pvalue) adjusted for the sign of the association).

### References

Rodriguez-Martinez A, et al. (2017).MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways. Bioinformatics, 33, 773-775.

Shannon P, et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13, 2498-2504.

Tenenbaum D. (2017). KEGGREST: Client-side REST access to KEGG. R package.

### Examples

    ## Test for association between diabetes and target_metabolites
    T2D_model <- MWAS_stats (targetMetabo_SE, disease_id = "T2D",
                             confounder_ids = c("Age", "Gender", "BMI"),
                             assoc_method = "logistic")

    ## Select the metabolites of interest and get their corresponding KEGG IDs
    T2D_model_subset = T2D_model[1:5, ]
    kegg_metabolites = c("cpd:C00186", "cpd:C01089", "cpd:C00123", "cpd:C00183",
                         "cpd:C00407")

    ## Map metabolites into KEGG pathways
    kegg_pathways = MWAS_KEGG_pathways(metabolites = kegg_metabolites,
                                       MWAS_matrix = T2D_model_subset)

[Package MWASTools version 1.6.0 Index]