MWAS_heatmap: Visualize MWAS results as a multiple-phenotype heatmap

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

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)

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