{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:04:10Z","timestamp":1782281050418,"version":"3.54.5"},"posted":{"date-parts":[[2025,8,31]]},"group-title":"Bioinformatics","reference-count":29,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH","award":["R35GM146586"],"award-info":[{"award-number":["R35GM146586"]}]},{"name":"NIH","award":["P20GM130454"],"award-info":[{"award-number":["P20GM130454"]}]},{"name":"NIH","award":["P30CA023108"],"award-info":[{"award-number":["P30CA023108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>High-throughput spatial transcriptomics (ST) now profiles hundreds of thousands of cells or locations per section, creating computational bottlenecks for routine analysis. Sketching, or intelligent sub-sampling, addresses scale by selecting small, representative subsets. While effective for scRNA-seq data, existing sketching methods, which optimize coverage in expression space but ignore physical location, can introduce spatial bias when applied to ST data. To explore the impact of sketching on ST analysis, we systematically benchmarked uniform sampling, leverage-score sampling, Geosketch (minimax\/Hausdorff), and scSampler (maximin) across multiple real ST datasets (mouse ovary, MERFISH brain, human breast cancer, lung) and simulations, using three input representations: PCA embeddings, spatial coordinates, and spatially smoothed embeddings. We show that expression-only designs capture global transcriptomic heterogeneity but distort tissue architecture by over-sampling high-variability regions and under-sampling homogeneous areas. Coordinate-only sampling restores tissue coverage but misses transcriptional extremes. A simple spatially aware extension, computing leverage scores from a randomized SVD basis smoothed by a spatial weights matrix, strikes a favorable balance, recovering rare cell states while maintaining uniform tissue coverage and avoiding edge effects. Across robust Hausdorff distances, clustering stability (ARI), PCA loading drift, and local cell-type MSE, spatially smoothed leverage scores match or outperform alternatives. These results motivate joint spatial\u2013transcriptomic sketching objectives to enable fast, unbiased analyses of increasingly large ST datasets.<\/jats:p>","DOI":"10.1101\/2025.08.26.672376","type":"posted-content","created":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T16:00:10Z","timestamp":1756656010000},"source":"Crossref","is-referenced-by-count":0,"title":["Benchmarking sketching methods on spatial transcriptomics data"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0881-5258","authenticated-orcid":false,"given":"Ian K.","family":"Gingerich","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brittany A.","family":"Goods","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. Robert","family":"Frost","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025090501301247000_2025.08.26.672376v2.1","doi-asserted-by":"publisher","DOI":"10.1080\/08982112.2015.1100447"},{"key":"2025090501301247000_2025.08.26.672376v2.2","unstructured":"Chen, Y. , Welling, M. & Smola, A. Super-Samples from Kernel Herding. en."},{"key":"2025090501301247000_2025.08.26.672376v2.3","first-page":"68","article-title":"Spatial subsemble estimator for large geostatistical data","volume":"22","year":"2025","journal-title":"Spatial Statistics","ISSN":"https:\/\/id.crossref.org\/issn\/2211-6753","issn-type":"print"},{"key":"2025090501301247000_2025.08.26.672376v2.4","first-page":"131","article-title":"Minimax and maximin distance designs","volume":"26","year":"2025","journal-title":"Journal of Statistical Planning and Inference","ISSN":"https:\/\/id.crossref.org\/issn\/0378-3758","issn-type":"print"},{"key":"2025090501301247000_2025.08.26.672376v2.5","first-page":"54","article-title":"Low-Rank Approximation and Regression in Input Sparsity Time","volume":"63","year":"2025","journal-title":"J. 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Dictionary learning for integrative, multimodal, and scalable single-cell analysis en. Pages: 2022.02.24.481684 Section: New Results. Feb. 2022. https:\/\/www.biorxiv.org\/content\/10.1101\/2022.02.24.481684v1 (2025)."},{"key":"2025090501301247000_2025.08.26.672376v2.12","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa408"},{"key":"2025090501301247000_2025.08.26.672376v2.13","doi-asserted-by":"crossref","unstructured":"Huang, L. , Gong, W. & Chen, D. scValue: value-based subsampling of large-scale single-cell transcriptomic data for machine and deep learning tasks en. 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