{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:10:57Z","timestamp":1776082257085,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":45,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The rapid development of spatially resolved transcriptomics (SRT) technologies has provided unprecedented opportunities for exploring the structure of specific organs or tissues. However, these techniques (such as image-based SRT) can achieve single-cell resolution, but can only capture the expression levels of tens to hundreds of genes. Such spatial transcriptomics (ST) data, carrying a large number of undetected genes, have limited its application value. To address the challenge, we develop SpaDiT, a deep learning framework for spatial reconstruction and gene expression prediction using scRNA-seq data. SpaDiT employs scRNA-seq data as an a priori condition and utilizes shared genes between ST and scRNA-seq data as latent representations to construct inputs, thereby facilitating the accurate prediction of gene expression in ST data. SpaDiT enhances the accuracy of spatial gene expression predictions over a variety of spatial transcriptomics datasets. We have demonstrated the effectiveness of SpaDiT by conducting extensive experiments on both seq-based and image-based ST data. We compared SpaDiT with eight highly effective baseline methods and found that our proposed method achieved an 8%\u201312% improvement in performance across multiple metrics. Source code and all datasets used in this paper are available at https:\/\/github.com\/wenwenmin\/SpaDiT and https:\/\/zenodo.org\/records\/12792074.<\/jats:p>","DOI":"10.1093\/bib\/bbae571","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T11:54:31Z","timestamp":1730980471000},"source":"Crossref","is-referenced-by-count":29,"title":["SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0829-0663","authenticated-orcid":false,"given":"Xiaoyu","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University , 650500, Kunming, Yunnan ,","place":["China"]}]},{"given":"Fangfang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Health and Nursing, Yunnan Open University , 650599, Kunming ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2558-2911","authenticated-orcid":false,"given":"Wenwen","family":"Min","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University , 650500, Kunming, Yunnan ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"2024110711541020900_ref1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/nmeth.2694","article-title":"Quantitative assessment of single-cell rna-sequencing methods","volume":"11","author":"Wu","year":"2014","journal-title":"Nat Methods"},{"key":"2024110711541020900_ref2","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1038\/s41576-021-00370-8","article-title":"Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics","volume":"22","author":"Longo","year":"2021","journal-title":"Nat Rev 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