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MiMIR

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.

Intalling

  1. Install the “devtools” package (if not already done):
install.packages("devtools")
  1. Install the “MetaboRiSc” package:
library("devtools")
devtools::install_github("DanieleBizzarri/MiMIR")
  1. Launch the application:
library("MiMIR")
MiMIR::startApp()

Quick Start

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.

  1. Start the application
  2. Upload your metabolites with the same column names as in the example dataset (both CSV and TSV are accepted).
  3. Check if the App could find all the necessary metabolites in your dataset.
  4. Check if your dataset was correctly uploaded
  5. View the Predicted Scores and the Figures
  6. Download the results

Requirements

R version: 3.6+

Install packages

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")

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Version

Install

install.packages('MiMIR')

Version

1.5

License

GPL-3

Last Published

February 1st, 2024

Functions in MiMIR (1.5)

apply.scale

apply.scale
activateButtn

activateButtn
calib_data_frame

calib_data_frame
comp_covid_score

comp_covid_score
comp.mort_score

comp.mort_score
comp.T2D_Ahola_Olli

comp.T2D_Ahola_Olli
comp.CVD_score

comp.CVD_score
get.s

get.s
cor_assoc

cor_assoc
get.p

get.p
covid_betas

COVID-score betas
calibration_surro

calibration_surro
getECE

getECE
calculate_surrogate_scores

calculate_surrogate_scores
kapmeier_scores

kapmeier_scores
find_BBMRI_names

find_BBMRI_names
hist_plots

hist_plots
model_coeff_heat

model_coeff_heat
c21

c21
getvol

getvol
mort_betas

Mortality score betas
multi_hist

multi_hist
getMCE

helper function to calculate the MCE of the calibrations
loading

withSpinner
is.sym

is.sym
pheno_barplots

pheno_barplots
prep_data_COVID_score

prep_data_COVID_score
predictions_surrogates

predictions_surrogates
do_metabowas

Helper function to compute MetaboWASs
loading_spin

loading_spin
metabo_names_translator

metabolomics feature nomenclatures
plattCalibration

plattCalibration
plot_corply

plot_corply
impute_miss

impute_miss
prep_met_for_scores

prep_met_for_scores
hist_plots_mortality

hist_plots_mortality
plot_na_heatmap

plot_na_heatmap
report.dim

report.dim
rendertable

rendertable
scatterplot_predictions

scatterplot_predictions
roc_surro

roc_surro
resort.on.s

resort.on.s
metabolites_subsets

metabolomics feature subsets
plotly_NA_message

plotly_NA_message
roc_surro_subplots

roc_surro_subplots
startApp

startMiMIR
plattCalib_evaluation

Function that plots the Platt Calibrations using plotly
phenotypes_names

phenotypic features names
subset_metabolites_overlap

subset_metabolites_overlap
ttest_scores

ttest_scores
ttest_surrogates

ttest_surrogates
synthetic_metabolic_dataset

synthetic metabolomics dataset
synthetic_phenotypic_dataset

synthetic metabolomics dataset
subset_samples_zero

subset_samples_miss
subset_samples_miss

subset_samples_miss
subset_samples_sd_surrogates

subset_samples_sd_surrogates
subset_samples_sd

subset_samples_sd
resort.on.p

resort.on.p
BMI_LDL_eGFR

BMI_LDL_eGFR
MetaboWAS

MetaboWAS
Ahola_Olli_betas

T2D-score Betas
BBMRI_hist

BBMRI_hist
NA_message

NA_message
apply.fit

apply.fit
CVD_score_betas

CVD-score betas
LOBOV_accuracies

LOBOV_accuracies
acc_LOBOV

acc_LOBOV
binarize_all_pheno

binarize_all_pheno
MOLEPI_LCBC_header

MOLEPI_LCBC_header
apply.fit_surro

apply.fit_surro
PARAM_metaboAge

PARAMETERS MetaboAge
PARAM_surrogates

PARAMETERS surrogates
BBMRI_hist_plot

multi_hist
QCprep_surrogates

QCprep_surrogates
QCprep

QCprep
BBMRI_hist_scaled

BBMRI_hist_scaled