{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T19:39:04Z","timestamp":1771357144727,"version":"3.50.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Knight-Hennessy Scholars"},{"DOI":"10.13039\/100023581","name":"National Science Foundation Graduate Research Fellowship Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100023581","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005492","name":"Stanford University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005492","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["1942926"],"award-info":[{"award-number":["1942926"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P30AG059307"],"award-info":[{"award-number":["P30AG059307"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["5RM1HG010023"],"award-info":[{"award-number":["5RM1HG010023"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Silicon Valley Foundation"},{"DOI":"10.13039\/100014989","name":"Chan-Zuckerberg Initiative","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Spatially resolved single-cell transcriptomics have provided unprecedented insights into gene expression in situ, particularly in the context of cell interactions or organization of tissues. However, current technologies for profiling spatial gene expression at single-cell resolution are generally limited to the measurement of a small number of genes. To address this limitation, several algorithms have been developed to impute or predict the expression of additional genes that were not present in the measured gene panel. Current algorithms do not leverage the rich spatial and gene relational information in spatial transcriptomics. To improve spatial gene expression predictions, we introduce Spatial Propagation and Reinforcement of Imputed Transcript Expression (SPRITE) as a meta-algorithm that processes predictions obtained from existing methods by propagating information across gene correlation networks and spatial neighborhood graphs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>SPRITE improves spatial gene expression predictions across multiple spatial transcriptomics datasets. Furthermore, SPRITE predicted spatial gene expression leads to improved clustering, visualization, and classification of cells. SPRITE can be used in spatial transcriptomics data analysis to improve inferences based on predicted gene expression.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The SPRITE software package is available at https:\/\/github.com\/sunericd\/SPRITE. Code for generating experiments and analyses in the manuscript is available at https:\/\/github.com\/sunericd\/sprite-figures-and-analyses.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae253","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T09:35:42Z","timestamp":1719567342000},"page":"i521-i528","source":"Crossref","is-referenced-by-count":7,"title":["SPRITE: improving spatial gene expression imputation with gene and cell networks"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8805-9864","authenticated-orcid":false,"given":"Eric D","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University , Stanford, CA 94305, United States"}]},{"given":"Rong","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA 02115, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8880-4764","authenticated-orcid":false,"given":"James","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University , Stanford, CA 94305, United States"}]}],"member":"286","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"2024062809081395000_btae253-B1","doi-asserted-by":"crossref","first-page":"e107","DOI":"10.1093\/nar\/gkaa740","article-title":"SpaGE: spatial gene enhancement using scRNA-seq","volume":"48","author":"Abdelaal","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2024062809081395000_btae253-B2","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.cell.2022.12.010","article-title":"Molecular and spatial signatures of mouse brain aging at single-cell 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