The future of rapid and automated single-cell data analysis using reference mapping is a research paper published in Cell (2024). On theSindex it has a DataRank of 1.7. It has been cited 71 times, with 69 citing works in its 1-hop citation network.
As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.641
From this paper's citation signal
Citation Network Contribution
1.0
From 44 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
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 (44 citing papers contributed measurable signal).
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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