Integrating cross-omics research through FAIR Digital Objects with DataPLANT

In plant sciences, single-cell and spatial transcriptomics generate large complex datasets which require structured metadata in order to make them interoperable and reproducible. In this work, we demonstrate how the Annotated Research Context (ARC) can be used for the management of such data using a barley single-cell RNA sequencing dataset integrated with spatial transcriptomics data. Experimental metadata was captured in ISA tables using ontology annotations to ensure machine readability and interpretability. The computational analyses were wrapped as Common Workflow Language (CWL) scripts to ensure reproducibility of the results and reusability of both the data and the analysis pipeline. The ARC links input materials unambiguously to the analysis results, allowing the research community to trace the entire investigation procedure. Our use case shows the benefits of using an ARC for structuring and annotating heterogeneous data, enabling comparative analyses across different datasets. While wrapping analysis scripts with CWL required some technical knowledge, the resulting standardized and reusable workflows outweigh this entry barrier. Parameter variations can be easily tested and results linked correctly without manual editing of the workflows themselves. Overall, this work highlights how using the ARC framework for single-cell datasets improves the FAIRness of the data and increases the reusability.