SC-framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

Single-cell (SC) sequencing technologies have advanced our ability to resolve cellular heterogeneity, yet the analysis of the resulting data remains complex, insufficiently standardized, and difficult to reproduce. Here, we present the SC-Framework, an FAIR-compliant, layered, semi-interactive analysis environment that combines a Python package with a structured series of Jupyter Notebooks to provide a complete, guided, reproducible, and flexible SC analysis workflow, across multiple modalities. The framework ensures traceability and findability via self-documenting data objects and configuration-defined directory structures. Containerized releases support long-term reproducibility, on both local machines and high-performance clusters. Exemplary single-cell RNA sequencing (scRNA-seq) and single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) analysis retrace published results within a standardized workflow, and benchmarking demonstrates scalability to nearly 1,000,000 cells. The SC-Framework addresses a gap between rigid automated pipelines and flexible but unstructured toolkit-based approaches, by balancing automation with interactivity, making robust SC analysis accessible to a broad range of users.