Optimal sample size to achieve a desired FDR
R package: https://cran.r-project.org/package=MetSizeR
Shiny app: https://adiet.shinyapps.io/metsizer
The MetSizeR application allows metabolomic scientists to estimate the optimal sample size required for a study to achieve a desired false discovery rate. MetSizeR can be used with or without pilot data to estimate the sample size required.
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Biomarker guided dietary intake 
Shiny app: https://adiet.shinyapps.io/Bio-Intake/
Bio-Intake (Biomarker guided dietary intake) allows users to upload mean daily self-reported citrus intake data (g/day) (estimated from food diaries) and computes calibrated intakes (g/day) based on a biomarker calibration equation. The biomarker based calibration equation was developed from urinary proline betaine and mean daily intake (estimated from a 4 day food diary) and is used to correct self-report estimates for measurement error.
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Shiny app: https://adiet.shinyapps.io/multiMarker/
multiMarker is a web application that infers the relationship between biomarker and food quantity data from an intervention study, and allows prediction of food intake when only biomarker data are available. Further, the framework allows quantification of the uncertainty in intake predictions.
Latent Variable Model to Infer Food Intake from Multiple Biomarkers
R package: https://CRAN.R-project.org/package=multiMarker 
A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates prediction of the intake along with its associated uncertainty. Details are in D'Angelo, et al. (2020).
Probabilistic Latent Variable Models for Metabolomic Data
R package: https://CRAN.R-project.org/package=MetabolAnalyze
Fits probabilistic principal components analysis, probabilistic principal components and covariates analysis and mixtures of probabilistic principal components models to metabolomic spectral data.