MWAS_KEGG_pathways: Map metabolites into KEGG pathways

In MWASTools: MWASTools: an integrated pipeline to perform metabolome-wide association studies

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)

MWASTools documentation built on Nov. 8, 2020, 5:07 p.m.