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metabom8

A High-Performance R Package for Metabolomics Modeling and Analysis

metabom8 (metabo-mate) provides:

  • 1D NMR data import & preprocessing
  • Multivariate modeling (PCA, PLS, O-PLS)
  • Metabolite identification

Core functionality is accelerated using C++ (Rcpp, Armadillo, Eigen) for improved performance.

🛠️ Features

  • NMR data import and preprocessing, including:
    • calibration, baseline correction, alignment, normalisation
    • provenance logging
  • Multivariate statistics:
    • Principal Components Analysis (PCA)
    • Partial Least Squares (PLS)
    • Orthogonal Partial Least Squares (OPLS)
  • Automatic selection of the optimal number of components for supervised models
  • Robust cross-validation framework, for large and small sample sizes (e.g., Monte Carlo CV)
  • OPLS model diagnostics & validation (DModX, permutation testing)
  • Tools supporting metabolite identification (STOCSY, STORM)
  • Native C++ acceleration via RcppArmadillo and RcppEigen

📦 Installation 

# Install from Bioconductor (once available in the release scheduled for end of April 2026)
install.packages("BiocManager")
BiocManager::install("metabom8")

# Install development version (until Bioconductor release)
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("tkimhofer/metabom8")

🚀 Quick Start 

library(metabom8)

# Load example data
data(hiit_raw, package = "metabom8")

# plot spectra interactively with plotly
plot_spec(hiit_raw)

# piped preprocessing 
hiit <- hiit_raw |> 
  calibrate(type = "tsp") |>
  excise() |>
  correct_baseline(method='asls') |>
  align_spectra() |>
  pqn()

## Provenance logging
print_provenance(hiit)


data(covid, package = "metabom8")
X <- covid$X
Y <- covid$an$type

# Modelling Context
uv = uv_scaling(center = TRUE)
mc_cv <- balanced_mc(k=15, split=2/3, type="DA")

# PCA
pca_model <- pca(covid$X, scaling = uv, ncomp=2)

# PLS
pls_model <- pls(X, Y, scaling = uv, validation_strategy=mc_cv)
show(pls_model)

# OPLS
opls_model <- opls(X, Y, scaling = uv, validation_strategy=mc_cv)

Tx <- scores(opls_model)
Px <- loadings(opls_model)
vip <- vip(opls_model)

⚡ Performance

Benchmarking OPLS modelling against another widely used R implementations under comparable conditions:

bench::mark(
  metabom8 = metabom8::opls(X, Y, scaling=uv, validation_strategy=kfold),
  ropls    = ropls::opls(X, Y, predI = 1, orthoI = 1),
  check = FALSE
)
Method Median Time Iter/sec Memory Use
metabom8 1.61 sec 0.617 247.33 MB
ropls 9.12 sec 0.110 2.09 GB

metabom8 is based on C++ accelerated linear algebra and provides a >5× speed-up over ropls*.

*ropls (uv scaling, 7-fold CV; 1 predictive and 1 orthogonal component).

📘 Documentation

Comprehensive documentation and vignettes are available at:
🔗 https://tkimhofer.github.io/metabom8/

📝 License & Citation

MIT License © Torben Kimhofer
If metabom8 or any of its components contributes to your work, please ensure appropriate citation. See CITATION for citation details.

🙋 Getting Help

  • Found a bug? Open an issue
  • Want to contribute? Fork the repo and submit a pull request

Built with 💙 by @tkimhofer