MWAS_heatmap {MWASTools} | R Documentation |
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.
MWAS_heatmap (metabo_SE, MWAS_list, alpha_th = 0.05, display_all = TRUE, ncut = 3, ...)
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. |
A heatmap showing MWAS results generated with multiple phenotypes. The function also returns a matrix indicating the metabolic clusters.
Gu Z, et al. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 32, 2847-2849.
## 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)