An integrated transcriptomic cell atlas of human neural organoids
An integrated transcriptomic cell atlas of human neural organoids is a dataset published in Nature (2024). On theSindex it has a DataRank of 1.8, placing it in the top 36.3% of the data-sharing corpus. It has been cited 111 times, with 93 citing works in its 1-hop citation network. Its calibrated FAIR score is 50/100.
Abstract
Human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to study human brain development, evolution and disease. However, it is unclear which parts of the human brain are covered by existing protocols, and it has been difficult to quantitatively assess organoid variation and fidelity. Here we integrate 36 single-cell transcriptomic datasets spanning 26 protocols into one integrated human neural organoid cell atlas totalling more than 1.7 million cells1-26. Mapping to developing human brain references27-30 shows primary cell types and states that have been generated in vitro, and estimates transcriptomic similarity between primary and organoid counterparts across protocols. We provide a programmatic interface to browse the atlas and query new datasets, and showcase the power of the atlas to annotate organoid cell types and evaluate new organoid protocols. Finally, we show that the atlas can be used as a diverse control cohort to annotate and compare organoid models of neural disease, identifying genes and pathways that may underlie pathological mechanisms with the neural models. The human neural organoid cell atlas will be useful to assess organoid fidelity, characterize perturbed and diseased states and facilitate protocol development.
›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
0.677
From this paper's citation signal
Citation Network Contribution
1.1
From 58 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.
- Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profilesProceedings of the National Academy of Sciences200555,906 citationsDataRank 1.6
- STAR: ultrafast universal RNA-seq alignerBioinformatics201355,202 citationsDataRank 1.6
- <tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics200944,025 citationsDataRank 1.6
- RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genomeBMC Bioinformatics201123,145 citationsDataRank 1.5
- The Molecular Signatures Database Hallmark Gene Set CollectionCell Systems201514,282 citationsDataRank 17.1Top 9%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 38% comes from its base citations and 62% from the citation network (58 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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