{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:42:04Z","timestamp":1753875724464,"version":"3.41.2"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["R03CA270725","R01GM105785","R01CA263494"],"award-info":[{"award-number":["R03CA270725","R01GM105785","R01CA263494"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The implementation of our method is available as an open-source R\/Bioconductor package with a user-friendly manual available at https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/RegionalST.html.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae186","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T23:25:02Z","timestamp":1712186702000},"source":"Crossref","is-referenced-by-count":0,"title":["Regional analysis to delineate intrasample heterogeneity with RegionalST"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8912-6624","authenticated-orcid":false,"given":"Yue","family":"Lyu","sequence":"first","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, United States"},{"name":"Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston , Houston, TX 77030, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8400-1785","authenticated-orcid":false,"given":"Chong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6350-1107","authenticated-orcid":false,"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center , Seattle, WA 98109, United States"},{"name":"Department of Biostatistics, University of North Carolina at Chapel Hill , Chapel Hill, NC 27516, United States"},{"name":"Department of Biostatistics, University of Washington , Seattle, WA 98195, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8359-0533","authenticated-orcid":false,"given":"Ziyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, TX 77030, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"2024041900050635600_btae186-B1","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1038\/s41467-018-04724-5","article-title":"Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity","volume":"9","author":"Berglund","year":"2018","journal-title":"Nat Commun"},{"key":"2024041900050635600_btae186-B2","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s41576-019-0129-z","article-title":"Spatial transcriptomics coming of age","volume":"20","author":"Burgess","year":"2019","journal-title":"Nat Rev Genet"},{"key":"2024041900050635600_btae186-B3","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1038\/s41592-022-01575-3","article-title":"Cell type-specific inference of differential expression in spatial 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