{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:13:13Z","timestamp":1787170393819,"version":"build-2736575974"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Classic RNA sequencing dissociates cells from their native tissue architecture, discarding spatial information that critically shapes transcriptional programs in development, homeostasis, and cancer. However, current ST platforms often produce incomplete and noisy profiles due to technical limitations and tissue variability. These limitations obscure biologically meaningful spatial patterns and hinder downstream interpretation. Here, we introduce STORM (spatial transcriptomics optimization by resolution via matrix factorization), a machine learning framework that improves the fidelity of spatial transcriptomics data under severe sparsity. STORM formulates spatial transcriptomics recovery as a low-rank tensor decomposition problem and integrates multimodal biological priors through a principled regularization strategy. Specifically, the model jointly captures spatial continuity, tissue morphology derived from whole-slide histology images, and gene\u2013gene interaction structure informed by protein\u2013protein interaction networks. This method enables accurate reconstruction at unobserved locations while preserving biologically meaningful spatial structure. Across diverse lung tissue profiles, including both healthy and malignant samples, STORM consistently outperforms existing state-of-the-art methods in recovering spatial gene\u2013expression patterns and remains robust even when a majority of spatial measurements are missing. By explicitly embedding biological structure into the reconstruction process, STORM provides a reliable foundation for high-resolution spatial transcriptomic analysis in settings where experimental data are sparse or incomplete. Availability: The source code developed in this study is publicly available at https:\/\/github.com\/denizgurarslan\/STORM.<\/jats:p>","DOI":"10.1093\/bib\/bbag324","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:23:18Z","timestamp":1781612598000},"source":"Crossref","is-referenced-by-count":1,"title":["STORM: spatial transcriptomics optimization by resolution via matrix factorization"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3799-9537","authenticated-orcid":false,"given":"Deniz","family":"Gurarslan","sequence":"first","affiliation":[{"name":"Institute of Data Science and Artificial Intelligence, Bogazici University , South Campus, Bebek, Besiktas, Istanbul 34342,","place":["Turkey"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4608-1588","authenticated-orcid":false,"given":"Oscar","family":"Camargo","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science and Engineering , University of Florida, 1889 Museum Road, Gainesville, FL 32611,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1292-3403","authenticated-orcid":false,"given":"Omer","family":"Zeyveli","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Fatih Sultan Mehmet Vakif University , Halic Campus, Sutluce, Beyoglu, Istanbul 34445,","place":["Turkey"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9251-7853","authenticated-orcid":false,"given":"Yasin","family":"Almalioglu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, Wolfson Building , Parks Road, Oxford OX1 3QD,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6277-4189","authenticated-orcid":false,"given":"Yanjun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science and Engineering, University of Florida , 1889 Museum Road, Gainesville, FL 32611,","place":["United States"]},{"name":"Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida , 1345 Center Drive, Room P3-12, Gainesville, FL 32610,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0913-2531","authenticated-orcid":false,"given":"Mehmet","family":"Turan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bogazici University , Bebek, Besiktas, Istanbul 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