MWAS_KEGG_shortestpaths {MWASTools} | R Documentation

## Build a shortest-path metabolic subnetwork

### Description

This function allows calculating the shortest paths between the metabolites of interest detected by MWAS analysis, and representing them as a network. The function also generates a network file and an attribute file, which can be easily imported into Cytoscape to visualize the network.

### Usage

    MWAS_KEGG_shortestpaths(network_table, metabolites, MWAS_matrix = NULL,
                            type = "all", distance_th = "Inf", names = TRUE,
                            file_name = "KeggSP")

### Arguments

`network_table` | four-column matrix where each row represents an edge between two nodes. See function "MWAS_KEGG_network()".
`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()".
`type` | character constant indicating whether all shortest paths (type = "all") or a single shortest path (type = "first") will be considered when there are several shortest paths between a given source metabolite and a given target metabolite.
`distance_th` | establishes a shortest path length threshold. Only shortest paths with length below this threshold will be included in the network.
`names` | logical scalar indicating whether the metabolite KEGG IDs will be transformed into common metabolite names.
`file_name` | character vector that allows customizing the name of the exported files.

### Value

A four-column matrix where each row represents an edge connecting two metabolites (from metabolite in column 1 to metabolite in column 2). The reactions involved in each metabolic conversion as well as the reaction type (i.e. reversible or irreversible) are reported in the third and fourth columns, respectively. This network can be visualized in R using the igraph package or similar packages.

The function also exports a network file ("KeggSP_NetworkFile.txt") and an attribute file ("KeggSP_AttributeFile.txt"), which can be easily imported into Cytoscape to visualize the 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

Csardi G, Nepusz T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.

Posma JM, et al. (2014). MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG. Bioinformatics, 30, 893-895.

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.

### Examples

    ## Build metabolic network: might take few minutes
    metabolic_network = MWAS_KEGG_network(kegg_paths = KEGG_metabolic_paths[[1]][, 1])

    ## 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")

    ## Build shortest-path subnetwork
    keggSP_subnetwork = MWAS_KEGG_shortestpaths(network_table = metabolic_network,
                                                metabolites = kegg_metabolites,
                                                MWAS_matrix = T2D_model_subset)

[Package MWASTools version 1.4.0 Index]