MWAS_network {MWASTools} | R Documentation

## Visualize MWAS results in a correlation-based metabolic network

### Description

This function allows visualizing MWAS results in a correlation-based metabolic network. The network is an undirected graph where the nodes represent the metabolites, and the edges represent a co-abundance relationship between pairs of nodes. Different node parameters (e.g. color, size) can be customized based on MWAS results.

### Usage

    MWAS_network (metabo_SE, MWAS_matrix, alpha_th = 0.05, cor_th = 0.25,
                  file_name = "Correlation", res_cor = 2)

### Arguments

`metabo_SE` | SummarizedExperiment object. See "MWAS_SummarizedExperiment()".
`MWAS_matrix` | numeric matrix generated by the function "MWAS_stats()".
`alpha_th` | numeric value indicating MWAS significance threshold.
`cor_th` | numeric value indicating the co-abundance similarity threshold. Thus, two metabolites will be linked in the network if the absolute correlation (Pearson) between them exceeds cor_th.
`file_name` | character string indicating the name given to the cytoscape files that will be exported to the working directory.
`res_cor` | numeric value restricting the number of decimals of the correlation of coefficients used to build the edges of the network.

### Value

A correlation based-metabolic network formalized as a weigthed igraph object. This igraph object contains two node attributes: "score" and "color". "score" is a vector containing the MWAS score (-log10(pvalue)*estimate sign) of each metabolite. "color" is a vector indicating the color of each node based on MWAS results ("cornflowerblue": "downregulation", "gray":"no change", "firebrick1":"upregulation"). These attributes can be used to customize node parameters based on MWAS results. The function also exports a network file ("Correlation_NetworkFile.txt") and an attribute file ("Correlation_AttributeFile.txt") of MWAS scores, which can be imported into cytoscape to visualize the network.

### References

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

### Examples

    ## Load data
    data(targetMetabo_SE)

    ## 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")

    ## Build correlation-based metabolic network
    net_T2D <- MWAS_network(targetMetabo_SE, T2D_model, file_name = "MWAS_T2D",
                            cor_th = 0.30)

    ## Visualize network using the igraph package
    # library(igraph)
    # plot(net_T2D, vertex.size = abs(V(net_T2D)$score*6)) # node size based on scores

    # plot(net_T2D, vertex.size = abs(V(net_T2D)$score*6),
    #      edge.label = E(net_T2D)$weight) # show edge labels

[Package MWASTools version 1.16.0 Index]