In modern biomedical research, cytometry produces vast volumes of high-dimensional data essential for scientific progress. Effectively managing these datasets while upholding rigorous quality standards, reproducibility, and FAIR principles (Findable, Accessible, Interoperable, and Reusable) has become increasingly challenging for research teams and specialized core facilities.
To address this, we have extended our established rsync-based data management system by integrating PeacoQC, a specialized R package for cytometry quality control. The solution first performs thorough quality assessment of raw FCS files on the local workstation using PeacoQC. After completion, it proceeds with a complete backup of both the original files and all QC outputs, including reports, diagnostic visualizations, and processed data. The stack is available in two configurations: a manual interface for on-demand operation and a fully automated service that runs silently in the background or during scheduled off-peak periods.
A key strength of the system is its precision and auditability. It selectively identifies newly acquired or recently modified FCS files, applies customizable PeacoQC analyses, and archives the results in an organized structure. Comprehensive and robust (error) logs and QC documentation are automatically generated and stored alongside the data, providing full traceability of processing steps, parameters, and outcomes. All records are systematically organized by workstation and date, supporting the transparency and long-term traceability required by FAIR standards.
Designed for (though not limited to) multi-user environments with high sample throughput, the solution manages network storage connections, authentication, and file permissions efficiently. This ensures that both raw and quality-controlled data remain secure yet accessible to authorized collaborators. The automated mode can be scheduled and includes an optional controlled shutdown after completion to reduce energy use and hardware uptime.
Overall, the integrated system embeds automated quality control directly into a reliable backup process, enabling research groups to generate cleaner, well-documented datasets with minimal manual effort and greater confidence in data quality. Built on proven technologies—including Python orchestration, rsync synchronization, and R-based QC routines—this development creates a unified, end-to-end workflow optimized for cytometry data management.
For additional details or to explore deployment options, please feel free to contact us.