MiMIR: R-shiny application to infer risk factors and endpoints from Nightingale Health’s (1)H-NMR metabolomics data

MOTIVATION: (1)H-NMR metabolomics is rapidly becoming a standard resource in large epidemiological studies to acquire metabolic profiles in large numbers of samples in a relatively low-priced and standardized manner. Concomitantly, metabolomics-based models are increasingly developed that capture disease risk or clinical risk factors. These developments raise the need for user-friendly toolbox to inspect new (1)H-NMR metabolomics data and project a wide array of previously established risk models. RESULTS: We present MiMIR (Metabolomics-based Models for Imputing Risk), a graphical user interface that provides an intuitive framework for ad hoc statistical analysis of Nightingale Health’s (1)H-NMR metabolomics data and allows for the projection and calibration of 24 pre-trained metabolomics-based models, without any pre-required programming knowledge. AVAILABILITY AND IMPLEMENTATION: The R-shiny package is available in CRAN or downloadable at https://github.com/DanieleBizzarri/MiMIR, together with an extensive user manual (also available as Supplementary Documents to the article). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.