{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T22:46:33Z","timestamp":1775169993178,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFE0102100"],"award-info":[{"award-number":["2024YFE0102100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Spatial transcriptomics allows for the measurement of high-throughput gene expression data while preserving the spatial structure of tissues and histological images. Integrating gene expression, spatial information, and image data to learn discriminative low-dimensional representations is critical for dissecting tissue heterogeneity and analyzing biological functions. However, most existing methods have limitations in effectively utilizing spatial information and high-resolution histological images. We propose a signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells\/spots.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>SDUCL integrates image features, spatial relationships, and gene expression information. We designed a signal diffusion microenvironment discovery algorithm, which effectively captures and integrates interaction information within the cellular microenvironment by simulating the biological signal diffusion process. By maximizing the mutual information between the local representation and the microenvironment representation of cells\/spots, SDUCL learns more discriminative representations. SDUCL was employed to analyze spatial transcriptomics datasets from multiple species, encompassing both normal and tumor tissues. SDUCL performed well in downstream tasks such as clustering, visualization, trajectory inference, and differential gene analysis, thereby enhancing our understanding of tissue structure and tumor microenvironments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/WeiMin-Li-visual\/SDUCL.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae663","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T17:05:08Z","timestamp":1731690308000},"source":"Crossref","is-referenced-by-count":4,"title":["A signal-diffusion-based unsupervised contrastive representation learning for spatial transcriptomics analysis"],"prefix":"10.1093","volume":"40","author":[{"given":"Nan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University , Shanghai 200444,","place":["China"]}]},{"given":"Xiao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University , Shanghai 200444,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9803-2745","authenticated-orcid":false,"given":"Weimin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University , Shanghai 200444,","place":["China"]}]},{"given":"Fangfang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University , Shanghai 200444,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-1725","authenticated-orcid":false,"given":"Yin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Life Sciences, East China Normal University , Shanghai 200241,","place":["China"]}]},{"given":"Zhongkun","family":"Zuo","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The Second Xiangya Hospital , Changsha 410011,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"2024112520120581200_btae663-B1","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 resolution","volume":"186","author":"Allen","year":"2023","journal-title":"Cell"},{"key":"2024112520120581200_btae663-B2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/ng.3149","article-title":"The genome sequence of the orchid phalaenopsis equestris","volume":"47","author":"Cai","year":"2015","journal-title":"Nat Genet"},{"key":"2024112520120581200_btae663-B3","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1016\/j.cell.2022.04.003","article-title":"Spatiotemporal 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