Squidpy: a scalable framework for spatial single cell analysis is a research paper (2021). On theSindex it has a DataRank of 3.0. It has been cited 71 times, with 65 citing works in its 1-hop citation network.
Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides both infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.
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
2.4
From 59 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 21% comes from its base citations and 79% from the citation network (59 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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