Abstract
High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing software must evolve to integrate seamlessly with downstream analysis tools, ensuring efficient and streamlined data analysis. Since its introduction in 2006, the xcms R package has become one of the most widely used preprocessing tools. Its development has followed an open-source, community-driven approach, ensuring long-term stability, continuous innovation, and broad accessibility.
We present recent advancements and extensions of xcms, including methodological improvements, expanded support for diverse mass spectrometry (MS) data formats, and enhanced integration with a growing ecosystem of R packages for MS data analysis. Improved data structures and flexible handling enable efficient processing of large-scale experiments with over 10,000 samples, even on standard computational setups. Lastly, xcms now interfaces with external software such as Sirius, GNPS and various Python libraries, facilitating more comprehensive workflows. These enhancements effectively establish xcms as a central component of a complete and interconnected software ecosystem, empowering users to create complete, tailored, reproducible data analysis workflow, without format conversions needed. A growing collection of tutorials and teaching materials helps users navigate this ecosystem, making it easier to leverage native R functionalities, Bioconductor packages, and statistical modeling tools to extend and customize their workflows.
Over the last two decades, xcms has embraced a strategy of flexibility, external collaboration, and open-source innovation rather than a monolithic structure. This approach has positioned xcms as a modern, adaptable preprocessing tool that will continue to evolve with the metabolomics community for the next 20 years and beyond.