Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows
Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows is a dataset published in Nature Methods (2025). On theSindex it has a DataRank of 1.3, placing it in the top 39.3% of the data-sharing corpus. It has been cited 81 times, with 73 citing works in its 1-hop citation network. Its calibrated FAIR score is 50/100.
Abstract
The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.
›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
0.601
From this paper's citation signal
Citation Network Contribution
0.727
From 38 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.
- Spatial reconstruction of single-cell gene expression dataNature Biotechnology20157,529 citationsDataRank 1.3
- Visualization and analysis of gene expression in tissue sections by spatial transcriptomicsScience20163,731 citationsDataRank 1.2
- Deep learning and alignment of spatially resolved single-cell transcriptomes with TangramNature Methods2021856 citationsDataRank 1.0
- Spatially resolved cell atlas of the mouse primary motor cortex by MERFISHNature2021527 citationsDataRank 7.6Top 25%
- Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systemsScience2021408 citationsDataRank 0.902
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 45% comes from its base citations and 55% from the citation network (38 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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