MRMhub-QUANT turns targeted MRM feature intensities into curated, QC-filtered, quantified results. It is the post-processing module of MRMhub, distributed as the R package mrmhub (library(mrmhub)), and works with any intensity data — from MRMhub-INTEGRATOR, Skyline, Agilent MassHunter, or generic CSV files. MRMhub-QUANT features:
- Reproducible pipelines. Create reproducible computational pipelines with QC vizualizations. Script, re-run, and share it.
- Flexible workflows. Metabolomics and lipidomics data post-processing using dedicated customizable functions.
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A single data object. Data, metadata, and processing details are stored in single sharable data object (
MRMhubExperiment).
Visualise at each step with QC plots — RunScatter, PCA, run-sequence, normalization QC.
Run your first analysis (5 min) →
Prefer point-and-click? build_workflow() opens a guided app that validates your data and metadata, warns about pipeline mismatches, and generates a downloadable Quarto (.qmd) workflow.
Quick Start and Demos
- Installation — install and verify your setup
- Your First Analysis — a 5-minute run on bundled data
- Prepare your data — file formats and importers
- Example: targeted lipidomics — a full lipidomics workflow
- Example: external calibration — a quantitative assay with calibration curves
Installation and Updating
Make sure to use a fresh R session without loaded packages (quit RStudio/Positron first to avoid locked packages):
if (!require("pak")) install.packages("pak")
pak::pak("SLINGhub/MRMhub")
library(mrmhub); mrmhub::check_setup()pak resolves locked packages and parallelises downloads; remotes::install_github("SLINGhub/MRMhub") is an equivalent fallback. For more details and troubleshooting see Installation and Troubleshooting & FAQ.
Contributing
Questions, bug reports, feature requests, and suggestions are welcome via GitHub issues. The project is released with a Contributor Code of Conduct.
Dual licensing
The source code is dual-licensed: GNU AGPLv3 for non-commercial use, or commercial licensing — contact Jonathan Tan (jonathan_tan@nus.edu.sg).