MWAS_heatmap {MWASTools} | R Documentation

## Visualize MWAS results as a multiple-phenotype heatmap

### Description

This function allows visualizing MWAS results generated using multiple phenotypes as a heatmap. The values of the heatmap are the individual MWAS scores: -log10 p-values (corrected for multiple-testing) adjusted for the direction of the association. The metabolites are ordered based on hierarchical cluster analysis of the auto-correlation metabolic matrix.

### Usage

    MWAS_heatmap (metabo_SE, MWAS_list, alpha_th = 0.05, display_all = TRUE, ncut = 3, ...)

### Arguments

`metabo_SE` | SummarizedExperiment object. See "MWAS_SummarizedExperiment()".
`MWAS_list` | list of matrices generated with the function "MWAS_stats()". The names of the individual matrices must correspond to the phenotype names. The dimensions of all matrices must be the same, and consistent with metabo_SE dimensions.
`alpha_th` | numeric value indicating MWAS significance threshold. Metabolites with p-value (corrected for multiple-testing) above alpha_th will have a MWAS score of 0.
`display_all` | logical constant indicating whether all metabolites from metabo_SE will be shown in the heatmap, or only the ones significantly associated with at least one phenotype.
`ncut` | numeric value indicating where the tree will be cut.
`...` | other arguments passed to the function "Heatmap()" from the ComplexHeatmap package.

### Value

A heatmap showing MWAS results generated with multiple phenotypes. The function also returns a matrix indicating the metabolic clusters.

### References

Gu Z, et al. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 32, 2847-2849.

### 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")

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

    ## Generate MWAS_list: do not forget the names!
    MWAS_list <- list(T2D = T2D_model, BMI = BMI_model)

    ## Generate heatmap
    MWAS_heatmap (targetMetabo_SE, MWAS_list, alpha_th = 0.05)

[Package MWASTools version 1.4.0 Index]