{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:22:51Z","timestamp":1791894171760,"version":"4.3.4"},"reference-count":245,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, and are often contaminated by noise and uncertainty, obscuring the underlying biological signal. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, metabolite levels, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering approaches struggle with the complexity of biological networks, while deep learning, with its ability to handle high-dimensional data and automatically identify meaningful patterns, has shown great promise in overcoming these challenges. Besides systematically reviewing the strengths and weaknesses of advanced deep learning methods, we have curated 21 datasets from nine benchmarks to evaluate the performance of 58 computational methods. Our analysis reveals that model performance can vary significantly across different benchmark datasets and evaluation metrics, providing a useful perspective for selecting the most appropriate approach based on a specific application scenario. We highlight three key areas for future development, offering valuable insights into how deep learning can be effectively applied to transcriptomic data analysis in biological, medical, and clinical settings.<\/jats:p>","DOI":"10.1093\/bib\/bbaf136","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:00:16Z","timestamp":1743789616000},"source":"Crossref","is-referenced-by-count":80,"title":["Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3103-0157","authenticated-orcid":false,"given":"Shuang","family":"Ge","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University , 2279 Lishui Road, Nanshan District, Shenzhen 518055, Guangdong ,","place":["China"]},{"name":"Pengcheng Laboratory , 6001 Shahe West Road, Nanshan District, Shenzhen 518055, Guangdong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqing","family":"Sun","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University , 2279 Lishui Road, Nanshan District, Shenzhen 518055, Guangdong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Public Health, Anhui University of Science and Technology , 15 Fengxia Road, Changfeng County, Hefei 231131, Anhui ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Kentucky , 329 Rose Street, Lexington 40506, Kentucky ,","place":["USA"]},{"name":"Institute for Biomedical Informatics, University of Kentucky , 800 Rose Street, Lexington 40506, Kentucky ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiang","family":"Ren","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory , 6001 Shahe West Road, Nanshan District, Shenzhen 518055, Guangdong ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"2025040421591155800_ref1","article-title":"Single-cell omics: experimental workflow, data analyses and applications","volume":"68","author":"Sun","year":"2024","journal-title":"Sci China Life Sci"},{"key":"2025040421591155800_ref2","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1038\/s12276-020-0422-0","article-title":"Single-cell transcriptomics in cancer: computational challenges and opportunities","volume":"52","author":"Fan","year":"2020","journal-title":"Exp Mol Med"},{"key":"2025040421591155800_ref3","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2801","article-title":"Method of the year 2013","volume":"11","author":"Anonymous","journal-title":"Nat Methods"},{"key":"2025040421591155800_ref4","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s43586-022-00190-y","article-title":"Single-cell pathogen diagnostics for combating antibiotic resistance","volume":"3","author":"Li","year":"2023","journal-title":"Nat. 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