Chromatograms: A Modular Infrastructure for Scalable Chromatographic Data Handling in R.
4 affiliations
- 1Institute for Biomedicine, Eurac Research, Via A. Volta 21, 39100Bolzano, Italy.
- 2Chair for Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743Jena, Germany.
- 3Computational Biology and Bioinformatics, de Duve Institute, UCLouvain, Avenue Hippocrate 75, 1200Brussels, Belgium.
- 4Department of Anaesthesiology and Intensive Care, University Medicine Greifswald, Ferdinand-Sauerbruch-Strasse 23A, 17475Greifswald, Germany.
Abstract
Chromatographic traces are central to LC-MS analysis, yet most software treats them as transient intermediates inside a fixed pipeline rather than as data objects users can inspect and manipulate directly. In R, xcms provides the established infrastructure for large-scale LC-MS analysis, but the way it represents chromatograms limits what can be done with them once extracted. We present chromatograms, an open-source R/Bioconductor package and a core component of the R for mass spectrometry ecosystem, built on two design principles: interchangeable storage backends, which keep the analysis code independent of where the data reside (in memory, on disk, etc.), and delayed evaluation, which applies queued operations only to the chromatograms actually requested and processed at a time. Chromatograms was fully integrated into xcms hence enabling the efficient extraction of EICs for identified features, exemplified on public data, for which extraction of 4980 features across 4000 samples took only a few hours, against a full day with the legacy infrastructure. For targeted data analysis or inspection, a peak table from any external software, or published with a study, is enough to recover the corresponding chromatograms from the raw files, with annotation columns preserved; four published biomarkers were recovered from all 4063 samples of a cohort even without parallel processing in just under an hour. Chromatograms does not perform peak detection: it provides the infrastructure for working with chromatographic data once peaks are defined, giving a scalable foundation for targeted and untargeted LC-MS analysis in R.
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BibTeX
@article{louail2026chromatograms,
title = {{Chromatograms: A Modular Infrastructure for Scalable Chromatographic Data Handling in R.}},
author = {Philippine Louail and Laurent Gatto and Sebastian Gibb and Johannes Rainer},
journal = {Analytical chemistry},
year = {2026},
month = sep,
doi = {10.1021/acs.analchem.6c03926},
url = {https://europepmc.org/article/MED/42916383},
note = {CC BY},
}
RIS
TY - JOUR
TI - Chromatograms: A Modular Infrastructure for Scalable Chromatographic Data Handling in R.
AU - Philippine Louail
AU - Laurent Gatto
AU - Sebastian Gibb
AU - Johannes Rainer
PY - 2026
DA - 2026/09/01
JO - Analytical chemistry
DO - 10.1021/acs.analchem.6c03926
UR - https://europepmc.org/article/MED/42916383
AB - Chromatographic traces are central to LC-MS analysis, yet most software treats them as transient intermediates inside a fixed pipeline rather than as data objects users can inspect and manipulate directly. In R, xcms provides the established infrastructure for large-scale LC-MS analysis, but the way it represents chromatograms limits what can be done with them once extracted. We present chromatograms, an open-source R/Bioconductor package and a core component of the R for mass spectrometry ecosystem, built on two design principles: interchangeable storage backends, which keep the analysis code independent of where the data reside (in memory, on disk, etc.), and delayed evaluation, which applies queued operations only to the chromatograms actually requested and processed at a time. Chromatograms was fully integrated into xcms hence enabling the efficient extraction of EICs for identified features, exemplified on public data, for which extraction of 4980 features across 4000 samples took only a few hours, against a full day with the legacy infrastructure. For targeted data analysis or inspection, a peak table from any external software, or published with a study, is enough to recover the corresponding chromatograms from the raw files, with annotation columns preserved; four published biomarkers were recovered from all 4063 samples of a cohort even without parallel processing in just under an hour. Chromatograms does not perform peak detection: it provides the infrastructure for working with chromatographic data once peaks are defined, giving a scalable foundation for targeted and untargeted LC-MS analysis in R.
SN - 0003-2700
SN - 1520-6882
ER -