{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:42:59Z","timestamp":1743111779355,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819751303"},{"type":"electronic","value":"9789819751310"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5131-0_10","type":"book-chapter","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T23:02:31Z","timestamp":1720738951000},"page":"107-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accurately Deciphering Novel Cell Type in\u00a0Spatially Resolved Single-Cell Data Through Optimal Transport"],"prefix":"10.1007","author":[{"given":"Mai","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuansong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningyuan","family":"Shangguan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"issue":"9","key":"10_CR1","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1038\/s41592-021-01203-6","volume":"18","author":"SM Lewis","year":"2021","unstructured":"Lewis, S.M., et al.: Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods 18(9), 997\u20131012 (2021)","journal-title":"Nat. Methods"},{"issue":"4","key":"10_CR2","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.cels.2016.03.008","volume":"2","author":"B Bodenmiller","year":"2016","unstructured":"Bodenmiller, B.: Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. 2(4), 225\u2013238 (2016)","journal-title":"Cell Syst."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S., Zhuang, X.: Spatially resolved, highly multiplexed rna profiling in single cells. Science 348(6233), aaa6090 (2015)","DOI":"10.1126\/science.aaa6090"},{"issue":"3","key":"10_CR4","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/s41592-021-01316-y","volume":"19","author":"JW Hickey","year":"2022","unstructured":"Hickey, J.W., et al.: Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nat. Methods 19(3), 284\u2013295 (2022)","journal-title":"Nat. Methods"},{"issue":"4","key":"10_CR5","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1093\/bioinformatics\/btab704","volume":"38","author":"H Teng","year":"2022","unstructured":"Teng, H., Yuan, Y., Bar-Joseph, Z.: Clustering spatial transcriptomics data. Bioinformatics 38(4), 997\u20131004 (2022)","journal-title":"Bioinformatics"},{"issue":"6","key":"10_CR6","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1111\/febs.15572","volume":"288","author":"G Partel","year":"2021","unstructured":"Partel, G., W\u00e4hlby, C.: Spage2vec: Unsupervised representation of localized spatial gene expression signatures. FEBS J. 288(6), 1859\u20131870 (2021)","journal-title":"FEBS J."},{"issue":"11","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1038\/s41587-021-00935-2","volume":"39","author":"E Zhao","year":"2021","unstructured":"Zhao, E., et al.: Spatial transcriptomics at subspot resolution with bayesspace. Nat. Biotechnol. 39(11), 1375\u20131384 (2021)","journal-title":"Nat. Biotechnol."},{"issue":"11","key":"10_CR8","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41592-021-01255-8","volume":"18","author":"J Hu","year":"2021","unstructured":"Hu, J.: Spagcn: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18(11), 1342\u20131351 (2021)","journal-title":"Nat. Methods"},{"issue":"1","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-022-02653-7","volume":"23","author":"Z Zeng","year":"2022","unstructured":"Zeng, Z., Li, Y., Li, Y., Luo, Y.: Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol. 23(1), 1\u201323 (2022)","journal-title":"Genome Biol."},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Zeng, Y., et al.: Identifying spatial domain by adapting transcriptomics with histology through contrastive learning. Briefings in Bioinformatics 24(2), bbad048 (2023)","DOI":"10.1093\/bib\/bbad048"},{"issue":"11","key":"10_CR11","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1038\/s41592-018-0175-z","volume":"15","author":"S Codeluppi","year":"2018","unstructured":"Codeluppi, S., et al.: Spatial organization of the somatosensory cortex revealed by osmfish. Nat. Methods 15(11), 932\u2013935 (2018)","journal-title":"Nat. Methods"},{"issue":"7","key":"10_CR12","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1016\/j.cub.2018.02.040","volume":"28","author":"S Pandey","year":"2018","unstructured":"Pandey, S., Shekhar, K., Regev, A., Schier, A.F.: Comprehensive identification and spatial mapping of habenular neuronal types using single-cell rna-seq. Curr. Biol. 28(7), 1052\u20131065 (2018)","journal-title":"Curr. Biol."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Shekhar, K., et al.: Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166(5), 1308\u20131323 (2016)","DOI":"10.1016\/j.cell.2016.07.054"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Cao, Z.J., Wei, L., Lu, S., Yang, D.C., Gao, G.: Cell blast: searching large-scale scrna-seq databases via unbiased cell embedding. BioRxiv p. 587360 (2019)","DOI":"10.1101\/587360"},{"issue":"7","key":"10_CR15","doi-asserted-by":"publisher","first-page":"792","DOI":"10.3390\/genes11070792","volume":"11","author":"L Chen","year":"2020","unstructured":"Chen, L., Zhai, Y., He, Q., Wang, W., Deng, M.: Integrating deep supervised, self-supervised and unsupervised learning for single-cell rna-seq clustering and annotation. Genes 11(7), 792 (2020)","journal-title":"Genes"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Brbi\u0107, M., et al.: Mars: discovering novel cell types across heterogeneous single-cell experiments. Nat. Methods 17(12), 1200\u20131206 (2020)","DOI":"10.1038\/s41592-020-00979-3"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Lotfollahi, M., et al.: Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40(1), 121\u2013130 (2022)","DOI":"10.1038\/s41587-021-01001-7"},{"issue":"11","key":"10_CR18","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1038\/s41592-022-01651-8","volume":"19","author":"M Brbi\u0107","year":"2022","unstructured":"Brbi\u0107, M., et al.: Annotation of spatially resolved single-cell data with stellar. Nat. Methods 19(11), 1411\u20131418 (2022)","journal-title":"Nat. Methods"},{"key":"10_CR19","first-page":"9912","volume":"33","author":"M Caron","year":"2020","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural. Inf. Process. Syst. 33, 9912\u20139924 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR20","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. Adv. Neural. Inf. Process. Syst. 26 (2013)"},{"issue":"314","key":"10_CR21","doi-asserted-by":"publisher","first-page":"2563","DOI":"10.1090\/mcom\/3303","volume":"87","author":"L Chizat","year":"2018","unstructured":"Chizat, L., Peyr\u00e9, G., Schmitzer, B., Vialard, F.X.: Scaling algorithms for unbalanced optimal transport problems. Math. Comput. 87(314), 2563\u20132609 (2018)","journal-title":"Math. Comput."},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Saito, K., Saenko, K.: Ovanet: One-vs-all network for universal domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9000\u20139009 (2021)","DOI":"10.1109\/ICCV48922.2021.00887"},{"key":"10_CR24","first-page":"16282","volume":"33","author":"K Saito","year":"2020","unstructured":"Saito, K., Kim, D., Sclaroff, S., Saenko, K.: Universal domain adaptation through self supervision. Adv. Neural. Inf. Process. Syst. 33, 16282\u201316292 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"10_CR25","doi-asserted-by":"publisher","first-page":"7640","DOI":"10.1038\/s41467-022-35288-0","volume":"13","author":"R Shen","year":"2022","unstructured":"Shen, R., et al.: Spatial-id: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. Nat. Commun. 13(1), 7640 (2022)","journal-title":"Nat. Commun."}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5131-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T23:09:19Z","timestamp":1720739359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5131-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819751303","9789819751310"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5131-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"12 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kunming","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bio.csu.edu.cn\/ISBRA2024\/ISBRA2024_Home.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}