SuperSpot: coarse graining spatial transcriptomics data into metaspots.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39657949", "kind": "science-record", "revision": 2, "content_hash": "sha256:617da1060694b0c6c6732dfa9d62abc7aab8ad3b11e6f048fb5bf828a3557c5f", "created_at": "2026-11-01T20:29:57Z", "updated_at": "2026-11-01T21:34:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SuperSpot: coarse graining spatial transcriptomics data into metaspots.", "doi": "10.1093/bioinformatics/btae734", "pmid": "39657949", "authors": ["Matei Teleman", "Aurélie A G Gabriel", "Léonard Hérault", "David Gfeller"], "journal": "Bioinformatics (Oxford, England)", "year": 2024, "abstract": "SUMMARY: Spatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. With current technologies such as VisiumHD, thousands of genes can be detected across millions of spots (also called cells or bins depending on the technologies). Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptomically similar spots into \"metaspots\". The process involves representing spots as nodes in a graph with edges connecting spots in spatial proximity and edge weights representing transcriptomic similarity. Hierarchical clustering is used to aggregate spots into metaspots at a user-defined resolution. We demonstrate that metaspots reduce the size and sparsity of spatial transcriptomic data and facilitate the analysis of large datasets generated with the most recent technologies.\nAVAILABILITY AND IMPLEMENTATION: SuperSpot is an R package available at https://github.com/GfellerLab/SuperSpot and archived on Zenodo (https://doi.org/10.5281/zenodo.14222088). The code to reproduce the figures is available at https://github.com/GfellerLab/SuperSpot/tree/main/figures (https://doi.org/10.5281/zenodo.14222088).", "claims": [{"id": "claim-method", "statement": "As in the SuperCell method (Bilous et al. 2022), the walktrap hierarchical clustering (Pons and Latapy 2005) is then applied on the graph to merge the spots into metaspots.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39657949", "locator": "sections[2] \"2 Results\" paragraph 2", "quote": "As in the SuperCell method (Bilous et al. 2022), the walktrap hierarchical clustering (Pons and Latapy 2005) is then applied on the graph to merge the spots into metaspots."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The STARMAPplus Mouse brain data is accessible from Zenodo (https://zenodo.org/records/8327576) as “well7_5”. 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The 10x Visium HD data is available from 10x’s website: Human Colorectal Cancer sample (https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc) and a mouse brain (https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39657949", "locator": "sections[8] \"Data availability\" paragraph 2", "quote": "The STARMAPplus Mouse brain data is accessible from Zenodo (https://zenodo.org/records/8327576) as “well7_5”. The 10x Visium Mouse Cortex raw data is accessible from the Globus endpoint “jhpce#HumanPilot10x” (http://research.libd.org/globus) and the processed data from SpatialLIBD (Maynard et al. 2021) R package and website (https://research.libd.org/spatialLIBD/index.html). The 10x Visium Human Breast Cancer data is available from 10x’s website (https://www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0). Nanostring CosMx Human Pancreas dataset and corresponding data are directly available from Nanostring’s website (https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/cosmx-smi-human-pancreas-ffpe-dataset/). The 10x Visium HD data is available from 10x’s website: Human Colorectal Cancer sample (https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc) and a mouse brain (https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We demonstrate that metaspots reduce the size and sparsity of spatial transcriptomic data and facilitate the analysis of large datasets generated with the most recent technologies.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39657949", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We demonstrate that metaspots reduce the size and sparsity of spatial transcriptomic data and facilitate the analysis of large datasets generated with the most recent technologies."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We demonstrate that metaspots reduce the size and sparsity of spatial transcriptomic data and facilitate the analysis of large datasets generated with the most recent technologies.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39657949"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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