MRMhub Workflow Examples

Reproducible example reports of the MRMhub targeted LC-MS data-processing pipeline

These notebooks reproduce the MRMhub pipeline end-to-end on real targeted LC-MS datasets — from INTEGRATOR peak-integration results through QUANT normalization, quantification, quality control and reporting. Each is a self-contained Quarto notebook combining narrative, executable R and outputs, provided as Supplementary Materials for the MRMhub manuscript.

Workflows

  • Dataset 1 ↗ — targeted plasma lipidomics, Tan et al. (Tan et al. 2022); 503 features across 937 samples. ISTD normalization, quantification, drift/batch correction and QC filtering.
  • Dataset 3 ↗ — targeted lipidomics, Chen et al. (Chen et al. 2025); 829 features across 4,591 samples. The same pipeline at population scale.
  • Dataset 4 ↗ — fully quantitative steroid assay (Panel 1, 15 analytes) with external calibration, low/high QC and EQA samples (Jansen et al. 2014). Validates MRMhub against Agilent MassHunter across the INTEGRATOR (peak areas) and QUANT (concentrations) modules.
  • Manuscript Figure ↗ — worked example of assembling publication-ready QC panels with MRMhub, reproducing the manuscript workflow figure’s data panels from the Dataset 1 pipeline.

The MRMhub pipeline

Open MRMhub-INTEGRATOR Peak Integration INTEGRATOR Open MRMhub-QUANT Quantitation Quality Control Reporting QUANT Retention time alignment Peak identification / selection Peak border refinement Integration Reporting ISTD normalization Interference correction Calibration curves Quantification Drift and batch correction Data integrity Feature annotation Outlier detection Runtime effects Processing efficacy Feature filtering Dataset export QC reporting Process reporting FAIR data sharing

Citation: Provided as Supplementary Materials for the MRMhub manuscript (in preparation).  |  Source: github.com/SLINGhub/MRMhub-workflows

Authors

MRMhub is developed by:

Data

Source data are available from Zenodo (record MRMhub-data).

Citation

  • MRMhub manuscript (in preparation)

Contact

For questions or feedback, contact bo.burla@nus.edu.sg and hyung_won_choi@nus.edu.sg.

References

Chen, Yuqing, Federico Torta, Hiromi W. L. Koh, Peter I. Benke, Resham L. Gurung, Jian-Jun Liu, Keven Ang, et al. 2025. “Metabolomics Profiling in Multi-Ancestral Individuals with Type 2 Diabetes in Singapore Identified Metabolites Associated with Renal Function Decline.” Diabetologia 68 (3): 557–75. https://doi.org/10.1007/s00125-024-06324-z.
Jansen, Rob, Nuthar Jassam, Annette Thomas, Carmen Perich, Pilar Fernandez-Calle, Ana Paula Faria, Helena Correia, et al. 2014. “A Category 1 EQA Scheme for Comparison of Laboratory Performance and Method Performance: An International Pilot Study in the Framework of the Calibration 2000 Project.” Clinica Chimica Acta 432 (May): 90–98. https://doi.org/10.1016/j.cca.2013.11.003.
Tan, Sock Hwee, Hiromi W. L. Koh, Jing Yi Chua, Bo Burla, Ching Ching Ong, Li San Lynette Teo, Xiaoxun Yang, et al. 2022. “Variability of the Plasma Lipidome and Subclinical Coronary Atherosclerosis.” Arteriosclerosis, Thrombosis, and Vascular Biology 42 (1): 100–112. https://doi.org/10.1161/ATVBAHA.121.316847.