metabom8

An R library for NMR-based metabolic profiling

metabom8 (pronounced metabo-mate) provides pipelines for 1D NMR data import, preprocessing, multivariate modeling (PCA, O-PLS), metabolite identification, and visualization — with core functions accelerated using C++ (Rcpp, Armadillo, Eigen) for improved computational performance.

🛠️ Features

• Preprocessing and analysis of 1D NMR spectra
• Prinicpal Component Analysis (PCA) and backscaled model loadings
• Orthogonal Partial Least Squares (OPLS) modeling via the NIPALS algorithm
• OPLS with automatic selection of the optimal number of components based on R2, Q2 and cross-valdated AUC
• Robust statistical validation for small to large sample sizes: stratified Monte Carlo CV and k-fold CV
• Custom model visualisations using ggplot2 and plotly
• Model diagnostics & validation (DModX, Permutation Testing)
• Metabolite identification via STOCSY and STORM
• Native C++ acceleration via RcppArmadillo and RcppEigen

📦 Installation

r install.packages("remotes") remotes::install_github("tkimhofer/metabom8")

🚀 Quick Start

```r library(metabom8) library(nmrdata)

Load example data

data(bariatric, package = "nmrdata")

idx <- bariatric$an$Class %in% c("Pre-op", "RYGB") X <- bariatric$X.pqn[idx, ] Y <- bariatric$an$Class[idx]

Fit an OPLS model using Monte Carlo Cross-Valdation

model <- opls( X = X, Y = Y, center = TRUE, scale = "UV", cv = list(method = "MC", k = 7, split=2/3) )

Plot scores and loadings

plotscores(model) plotload(model) ```

⚡ Performance

Benchmarked against the ropls package under identical conditions using the bench package:

r bench::mark( metabom8 = metabom8::opls(X, Y, ...), ropls = ropls::opls(X, Y, predI = 1, orthoI = 1, ...), check = FALSE )

| Method | Median Time | Iter/sec | Memory Use | |------------|-------------|----------|------------| | metabom8 | 1.61 sec | 0.621 | 2.81 GB | | ropls | 3.95 sec | 0.253 | 1.61 GB |

metabom8 provides a ~2.5× speed-up using C++ linear algebra (Eigen, Armadillo) when compared to widely used implementations [^1].

[^1]: Benchmark comparison uses the ropls package (Bioconductor), a widely used OPLS implementation (parameters: uv scaling, 7-fold CV, 1 predictive + 1 orthogonal component).

📘 Documentation

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

• MVA vignette
• Preprocessing pipeline
• Function reference

🔗 Related Packages

• nmrdata: Example dataset for NMR spectral analysis (used for performance comparison)

If you find metabom8 useful, please consider giving it a ⭐ — it makes it easier for others to discover the project!

📝 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
• Code of Conduct: see CODE_OF_CONDUCT.md

Built with 💙 by @tkimhofer