{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T10:51:16Z","timestamp":1791975076318,"version":"4.4.0"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n Single-cell time-series atlases provide discrete cross-sectional snapshots of continuous developmental processes, making missing-snapshot prediction essential for reconstructing trajectories and dynamic programs. However, destructive scRNA-seq profiling prevents tracking the same cells across time, yielding unpaired population snapshots and making missing-snapshot prediction challenging. Existing methods typically rely on explicit dynamical propagation or transport-based coupling between snapshots, which may ignore the statistical constraints of the overall population distribution and be sensitive to sampling noise and cell-to-cell variability.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We introduce scChronos, a hierarchical temporal context learning framework that formulates missing-snapshot prediction as target-conditioned population distribution prediction rather than individual-cell propagation. Unlike cell-centric propagation approaches, scChronos explicitly captures dual-level dynamics: (i) Within each observed snapshot, scChronos compresses cell populations into cluster-representative cell representations with a transcriptomic encoder, preserving population information while reducing noise; (ii) Across reference snapshots, the dual-branch temporal context integration module fuses information from all available and nearby time points into a temporal representation, which is then decoded with learnable cell-generation seeds to predict the target distribution. Extensive experiments demonstrate that scChronos improves both interpolation-style recovery and extrapolation-style forecasting compared with state-of-the-art methods. Beyond reducing distributional discrepancy, scChronos better preserves developmental topology and maintains cell-type-specific marker and pathway programs.<\/jats:p>\n <\/jats:sec>\n \n Availability<\/jats:title>\n The code and data are available at https:\/\/github.com\/yjs193\/scChronos and https:\/\/doi.org\/10.5281\/zenodo.20933283.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag772","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:39:35Z","timestamp":1791891575000},"source":"Crossref","is-referenced-by-count":0,"title":["scChronos: Hierarchical Temporal Context Learning for Missing Snapshot Prediction in Single-Cell Time Series"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2838-3425","authenticated-orcid":false,"given":"Jiongsen","family":"Yao","sequence":"first","affiliation":[{"name":"South China University of Technology School of Computer Science and Engineering, , Guangzhou, 510006,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-7919","authenticated-orcid":false,"given":"Yunfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"South China University of Technology School of Future Technology, , Guangzhou, 510641,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7183-3155","authenticated-orcid":false,"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology School of Computer Science and Engineering, , Guangzhou, 510006,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5446-2214","authenticated-orcid":false,"given":"Jinjin","family":"Ma","sequence":"additional","affiliation":[{"name":"South China University of Technology The Institute of Future Health, , Guangzhou, 511442,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4022-0852","authenticated-orcid":false,"given":"Si","family":"Wu","sequence":"additional","affiliation":[{"name":"South China University of Technology School of Computer Science and Engineering, , Guangzhou, 510006,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,14]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag772\/71938706\/btag772.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag772\/71938706\/btag772.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T10:19:56Z","timestamp":1791973196000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article\/doi\/10.1093\/bioinformatics\/btag772\/8916270"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,10,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag772","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,14]]},"article-number":"btag772"}}