ArticleiScience2026
SC-framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Comparison between a conventional tool and deep learning models for RNA velocity analysis of scRNA-Seq data.Molecular genetics and genomics : MGG · 2026Article
- Modern Mining: The Role of Single-cell RNA Sequencing in Advancing Neuroscience Research.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.