{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T17:13:44Z","timestamp":1780074824401,"version":"3.54.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U24CA248453"],"award-info":[{"award-number":["U24CA248453"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U24CA264027"],"award-info":[{"award-number":["U24CA264027"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Schmidt DataX Fund at Princeton University"},{"name":"Schmidt Futures Foundation"},{"name":"Eric and Wendy Schmidt Center"},{"DOI":"10.13039\/100013114","name":"Broad Institute of MIT and Harvard","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013114","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Perturbations in biological tissues\u2014e.g. due to inflammation, disease, or drug treatment\u2014alter the composition of cell types and cell states in the tissue. These alterations are often spatially localized in different regions of a tissue, and can be measured using spatial transcriptomics technologies. However, current methods to analyze differential abundance in cell types or cell states, either do not incorporate spatial information\u2014and thus cannot identify spatially localized alterations\u2014or use heuristic and inaccurate approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce Spatial Anomaly Region Detection in Expression Manifolds (Sardine), a method to estimate spatially localized changes in spatial transcriptomics data obtained from tissue slices from two or more conditions. Sardine estimates the probability of a cell state being at the same (relative) spatial location between different conditions using spatially localized density estimation. On simulated data, Sardine recapitulates the spatial patterning of expression changes more accurately than existing approaches. On a Visium dataset of the mouse cerebral cortex before and after injury response, as well as on a Visium dataset of a mouse spinal cord undergoing electrotherapy, Sardine identifies regions of spatially localized expression changes that are more biologically plausible than alternative approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>We implement Sardine in Python 3, with an open source implementation available at: https:\/\/github.com\/raphael-group\/spatial_anomaly_detection.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf242","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:03:07Z","timestamp":1752584587000},"page":"i493-i501","source":"Crossref","is-referenced-by-count":2,"title":["Anomaly detection in spatial transcriptomics via spatially localized density comparison"],"prefix":"10.1093","volume":"41","author":[{"given":"Gary","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Princeton University, Princeton, NJ, 08540,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Gold","sequence":"additional","affiliation":[{"name":"Center for Statistics and Machine Learning, Princeton University , Princeton, NJ, 08540,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-0960","authenticated-orcid":false,"given":"Uthsav","family":"Chitra","sequence":"additional","affiliation":[{"name":"Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard , Cambridge, MA, 02142,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunay","family":"Joshi","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computational Sciences, University of Pennsylvania , Philadelphia, PA, 19104,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1274-048X","authenticated-orcid":false,"given":"Benjamin J","family":"Raphael","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Princeton University, Princeton, NJ, 08540,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"2025071509030169800_btaf242-B1","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1038\/s41587-020-00803-5","article-title":"Quantifying the effect of experimental perturbations at single-cell resolution","volume":"39","author":"Burkhardt","year":"2021","journal-title":"Nat Biotechnol"},{"key":"2025071509030169800_btaf242-B2","first-page":"1908","author":"Chitra U, Ding K, Lee 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