MiMIR (Metabolomics-based Models for Imputing Risk), is a a unique graphical user interface that provides an intuitive framework for ad-hoc statistical analysis of 1H-NMR metabolomics by Nightingale Health. It allows to easily explore new metabolomics measurements assayed by Nightingale Health; project previously published metabolic scores; and calibrate the metabolic surrogate values to a desired dataset.
To have a detail description of all the possible analyses available in MiMIR, please take a look at the Manual:https://github.com/DanieleBizzarri/MiMIR/blob/main/man/MANUAL.pdf Please refer to our manuscripts when using these metabolic biomarkers in your works: - mortality score: J. Deelen et al., ‘A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals’, Nat. Commun., vol. 10, no. 1, pp. 1–8, Aug. 2019, doi: 10.1038/s41467-019-11311-9 - MetaboAge: van den Akker Erik B. et al., ‘Metabolic Age Based on the BBMRI-NL 1H-NMR Metabolomics Repository as Biomarker of Age-related Disease’, Circ. Genomic Precis. Med., vol. 13, no. 5, pp. 541–547, Oct. 2020, doi: 10.1161/CIRCGEN.119.002610. - surrogate clinical variables: D. Bizzarri, M. J. T. Reinders, M. Beekman, P. E. Slagboom, Bbmri-nl, and E. B. van den Akker, ‘1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints’, EBioMedicine, vol. 75, p. 103764, Jan. 2022, doi: 10.1016/j.ebiom.2021.103764. - COVID-severity score: Nightingale Health UK Biobank Initiative, H. Julkunen, A. Cichońska, P. E. Slagboom, and P. Würtz, ‘Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population’, eLife, vol. 10, p. e63033, May 2021, doi: 10.7554/eLife.63033. - Type-2 diabetes score: A. V. Ahola-Olli et al., ‘Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts’, Diabetologia, vol. 62, no. 12, pp. 2298–2309, 2019, doi: 10.1007/s00125-019-05001-w. - Cardiovascular event risk score: P. Würtz et al., ‘Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts’, Circulation, vol. 131, no. 9, pp. 774–785, Mar. 2015, doi: 10.1161/CIRCULATIONAHA.114.013116.
install.packages("devtools")library("devtools")
devtools::install_github("DanieleBizzarri/MiMIR")library("MiMIR")
MiMIR::startApp()Note: By pressing the button “Dowload example” you can download a .zip file, containing 2 files: the metabolic synthetic dataset, the phenotypic synthetic dataset. These example dataset can be used to test the App and to understand how the variables in your own dataset should be named.
R version: 3.6+
If you have problems in installing the applicationn, you can try installing these packages manually:
## Shiny environment if (!require(“shiny”)) install.packages(“shiny”) if (!require(“shinydashboard”)) install.packages(“shinydashboard”) if (!require(“shinyWidgets”)) install.packages(“shinyWidgets”) if (!require(“shinycssloaders”)) install.packages(“shinycssloaders”) if (!require(“shinyjs”)) install.packages(“shinyjs”) if (!require(“shinyFiles”)) install.packages(“shinyFiles”)
#Statistics libraries if (!require(“DT”)) install.packages(“DT”) if (!require(“foreach”)) install.packages(“foreach”) if (!require(“matrixStats”)) install.packages(“matrixStats”) if (!require(“dplyr”)) install.packages(“dplyr”) if (!require(“plyr”)) install.packages(“plyr”) if (!require(“stats”)) install.packages(“stats”) if (!require(“caret”)) install.packages(“caret”) if (!require(“purrr”)) install.packages(“purrr”) if (!require(“rmarkdown”)) install.packages(“rmarkdown”)
#Imaging libraries if (!require(“pROC”)) install.packages(“pROC”) if (!require(“plotly”)) install.packages(“plotly”) if (!require(“heatmaply”)) install.packages(“heatmaply”) if (!require(“ggplot2”)) install.packages(“ggplot2”) if (!require(“ggfortify”)) install.packages(“ggfortify”) if (!require(“survival”)) install.packages(“survival”) if (!require(“survminer”)) install.packages(“survminer”)