Streamlining LC-MS/MS Data Analysis in R with Open-Source xcms and RforMassSpectrometry: An End-to-End Workflow
Creators
- 1. Institute for Biomedicine, Eurac Research, Italy
- 2. Sensing Technologies Laboratory (STL), Faculty of Engineering, Free University of Bozen-Bolzano , Italy
- 3. Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Italy
- 4. Department of Food Chemistry and Toxicology, University of Vienna, Austria
- 5. Department of Chemistry, Aristotle University of Thessaloniki, Greece
- 6. Biomic_AUTh, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, Greece
Description
Despite untargeted LC-MS/MS data being a powerful approach for large-scale metabolomics analysis, a significant challenge in the field lies in the reproducible and efficient analysis of such data. The power of R-based analysis workflows lies in their high customizability and adaptability to specific instrumental and experimental setups, but while various specialized packages exist for individual analysis steps, their seamless integration and application to large cohort datasets remains elusive. Addressing this gap, we present a comprehensible end-to-end R workflow that leverages xcms and packages of the RforMassSpectrometry environment to encompass all aspects of pre-processing and downstream analyses for LC-MS/MS datasets in a reproducible manner.
This poster/presentation delineates a step-by-step analysis of an example untargeted metabolomics dataset tailored to quantify the small polar metabolome in human plasma samples and aimed to identify differences between individuals suffering from cardiovascular disease and healthy controls. The objective of the workflow is to meticulously detail each step, from the preprocessing of raw mzML files to the annotation of differentially abundant ions between the two groups.
Our workflow seamlessly integrates Bioconductor packages, offering adaptability to diverse study designs and analysis requirements. This workflow facilitates preprocessing, feature detection, alignment, normalization, statistical analysis, and annotation within a unified framework, thereby enhancing the efficiency of metabolomic investigations. We also discuss alternative approaches to accommodate various datasets and goals, while emphasizing proper quality management for LC-MS data analysis.
Notes
Files
EndtoEndWorkflow_metabolomics.pdf
Files
(2.7 MB)
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Additional details
Funding
Software
- Repository URL
- https://github.com/EuracBiomedicalResearch/end-to-end-untargeted-metabolomics
- Programming language
- R, RMarkdown
- Development Status
- Active
References
- Rainer, J., Louail, P., Vicini, A., Gine, R., Badia, J. M., Stravs, M., Garcia-Aloy, M., Huber, C., Salzer, L., Stanstrup, J., Shahaf, N., Panse, C., Naake, T., Kumler, W., Vangeenderhuysen, P., Brunius, C., Hecht, H., Neumann, S., Witting, M., … Gatto, L. (2024). An Open Software Development-based Ecosystem of R Packages for Metabolomics Data Analysis (v2.0.0). 20th Annual Conference of the Metabolomics Society; Metabolomics 2024 (METABOLOMICS 2024), Osaka, Japan. Zenodo. https://doi.org/10.5281/zenodo.11370345