{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T03:58:20Z","timestamp":1778903900243,"version":"3.51.4"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T00:00:00Z","timestamp":1749859200000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"China Postdoctoral Science Foundation, the Postdoctoral Fellowship Program","award":["GZB20230084"],"award-info":[{"award-number":["GZB20230084"]}]},{"name":"Chinese Academy of Medical Sciences & Peking Union Medical College, Union Medical College Young Scholar Support Program","award":["2023086 and 2023088"],"award-info":[{"award-number":["2023086 and 2023088"]}]},{"name":"Special Research Fund for Central Universities, Peking Union Medical College","award":["3332024089"],"award-info":[{"award-number":["3332024089"]}]},{"name":"NCTIB Fund for R&D Platform for Cell and Gene Therapy, the Suzhou Municipal Key Laboratory","award":["SZS2022005"],"award-info":[{"award-number":["SZS2022005"]}]},{"name":"Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences","award":["2022-RC416\u201301"],"award-info":[{"award-number":["2022-RC416\u201301"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2021-I2M-1-061, 2022-I2M-2-004, 2023-I2M-2-005"],"award-info":[{"award-number":["2021-I2M-1-061, 2022-I2M-2-004, 2023-I2M-2-005"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32300560"],"award-info":[{"award-number":["32300560"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large single-cell ribonucleic acid-sequencing (scRNA-seq) datasets offer unprecedented biological insights but present substantial computational challenges for visualization and analysis. While existing subsampling methods can enhance efficiency, they may not ensure optimal performance in downstream machine learning and deep learning (ML\/DL) tasks. Here, we introduce scValue, a novel approach that ranks individual cells by \u2018data value\u2019 using out-of-bag estimates from a random forest model. scValue prioritizes high-value cells and allocates greater representation to cell types with higher variability in data value, effectively preserving key biological signals within subsamples. We benchmarked scValue on automatic cell-type annotation tasks across four large datasets, paired with distinct ML\/DL models. Our method consistently outperformed existing subsampling methods, closely matching full-data performance across all annotation tasks. In three additional case studies\u2014label transfer learning, cross-study label harmonization, and bulk RNA-seq deconvolution\u2014scValue more effectively preserved T-cell annotations across human gut-colon datasets, more accurately reproduced T-cell subtype relationships in a human spleen dataset, and constructed a more reliable single-cell immune reference for cell-type deconvolution in simulated bulk tissue samples. Finally, using 16 public datasets ranging from tens of thousands to millions of cells, we evaluated subsampling quality based on computational time, Gini coefficient, and Hausdorff distance. scValue demonstrated fast execution, well-balanced cell-type representation, and distributional properties akin to uniform sampling. Overall, scValue provides a robust and scalable solution for subsampling large scRNA-seq data in ML\/DL workflows. It is available as an open-source Python package installable via pip, with source code at https:\/\/github.com\/LHBCB\/scvalue.<\/jats:p>","DOI":"10.1093\/bib\/bbaf279","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T07:45:36Z","timestamp":1748591136000},"source":"Crossref","is-referenced-by-count":3,"title":["scValue: value-based subsampling of large-scale single-cell transcriptomic data for machine and deep learning tasks"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9437-4366","authenticated-orcid":false,"given":"Li","family":"Huang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College , 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weikang","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College , 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123 ,","place":["China"]},{"name":"Center for Artificial Intelligence and Computational Biology, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College , 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7196-4136","authenticated-orcid":false,"given":"Dongsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College , 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"2025061400125418100_ref1","doi-asserted-by":"publisher","first-page":"e27041","DOI":"10.7554\/eLife.27041","article-title":"The human cell atlas","volume":"6","author":"Regev","year":"2017","journal-title":"elife"},{"key":"2025061400125418100_ref2","doi-asserted-by":"publisher","first-page":"D886","DOI":"10.1093\/nar\/gkae1142","article-title":"CZ CELL\u00d7 GENE discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data","volume":"53","author":"CZI Single-Cell Biology Program","year":"2025","journal-title":"Nucleic Acids 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