Single-cell reference mapping to construct and extend cell-type hierarchies
Single-cell reference mapping to construct and extend cell-type hierarchies is a dataset published in NAR Genomics and Bioinformatics (2023). On theSindex it has a DataRank of 0.952, placing it in the top 42.1% of the data-sharing corpus. It has been cited 34 times, with 25 citing works in its 1-hop citation network. Its calibrated FAIR score is 49/100.
Abstract
Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge in the field. Whereas recent computational techniques, referred to as 'reference mapping' methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present 'treeArches', a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.
›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
0.520
From this paper's citation signal
Citation Network Contribution
0.432
From 17 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.
- Integrated analysis of multimodal single-cell dataCell202115,542 citationsDataRank 1.4
- Enrichr: a comprehensive gene set enrichment analysis web server 2016 updateNucleic Acids Research201611,578 citationsDataRank 15.9Top 11%
- Benchmarking atlas-level data integration in single-cell genomicsNature Methods20211,376 citationsDataRank 10.3Top 21%
- The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humansScience2022979 citationsDataRank 9.0Top 23%
- Comparative cellular analysis of motor cortex in human, marmoset and mouseNature2021818 citationsDataRank 1.0
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 55% comes from its base citations and 45% from the citation network (17 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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