MiMIR (version 1.5)

BBMRI_hist_plot: multi_hist

Description

Function to plot the ~60 metabolites used for the metabolomics-based scores and compare them to to their distributions in BBMRI-nl

Usage

BBMRI_hist_plot(
  dat,
  x_name,
  color = MiMIR::c21,
  scaled = FALSE,
  datatype = "metabolite",
  main = "Comparison with the metabolites measures in BBMRI"
)

Value

plotly image with the histogram of the selected variable compared to the distributions in BBMRI-nl

Arguments

dat

data.frame or matrix with the metabolites

x_name

string with the name of the selected variable

color

colors selected for all the variables

scaled

logical to z-scale the variables

datatype

a character vector indicating what data type is being plotted

main

title of the plot

Details

This function plots the distribution of a metabolic feature in the uploaded dataset, compared to their distributions in BBMRI-nl. The selection of features available is done following the metabolic scores features.

References

The selection of metabolic features available is the one selected by the papers: Deelen,J. et al. (2019) A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nature Communications, 10, 1-8, doi:10.1038/s41467-019-11311-9 Ahola-Olli,A.V. et al. (2019) Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts. Diabetologia, 62, 2298-2309, doi:10.1007/s00125-019-05001-w Wurtz,P. et al. (2015) Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation, 131, 774-785, doi:10.1161/CIRCULATIONAHA.114.013116 Bizzarri,D. et al. (2022) 1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints. EBioMedicine, 75, 103764, doi:10.1016/j.ebiom.2021.103764 van den Akker Erik B. et al. (2020) Metabolic Age Based on the BBMRI-NL 1H-NMR Metabolomics Repository as Biomarker of Age-related Disease. Circulation: Genomic and Precision Medicine, 13, 541-547, doi:10.1161/CIRCGEN.119.002610

Examples

library(plotly)
library(MiMIR)

#load the metabolites dataset
metabolic_measures <- synthetic_metabolic_dataset

BBMRI_hist_plot(metabolic_measures, x_name="alb", scaled=TRUE)