Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas
Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas is a dataset published in Nature Metabolism (2023). On theSindex it has a DataRank of 1.5, placing it in the top 37.9% of the data-sharing corpus. It has been cited 79 times, with 70 citing works in its 1-hop citation network. Its calibrated FAIR score is 61/100.
Abstract
Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.639
From this paper's citation signal
Citation Network Contribution
0.848
From 43 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- <tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics200944,025 citationsDataRank 1.6
- Integrating single-cell transcriptomic data across different conditions, technologies, and speciesNature Biotechnology201814,465 citationsDataRank 1.4
- Massively parallel digital transcriptional profiling of single cellsNature Communications20177,641 citationsDataRank 1.3
- Ensembl BioMarts: a hub for data retrieval across taxonomic spaceDatabase20111,535 citationsDataRank 14.8
- Benchmarking atlas-level data integration in single-cell genomicsNature Methods20211,376 citationsDataRank 10.3Top 21%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 43% comes from its base citations and 57% from the citation network (43 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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