{
  "abstract": "Motivation: 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.\n\nResults: 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 and (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.\n\nAvailability: The code and data are available at https://github.com/yjs193/scChronos and https://doi.org/10.5281/zenodo.20933283.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Jiongsen Yao",
      "orcid": "0009-0008-2838-3425"
    },
    {
      "name": "Yunfei Zhang",
      "orcid": "0000-0002-4593-7919"
    },
    {
      "name": "Yong Xu",
      "orcid": "0000-0001-7183-3155"
    },
    {
      "name": "Jinjin Ma",
      "orcid": "0000-0002-5446-2214"
    },
    {
      "name": "Si Wu",
      "orcid": "0000-0003-4022-0852"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9c44b2qzyb27d.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag772",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13722282/",
  "id": "9c44b2qzyb27d",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://doi.org/10.1093/bioinformatics/btag772",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Gene Expression"
    ],
    [
      "heading",
      "Original Paper"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221785833",
  "pdf_url": null,
  "pmcid": "PMC13722282",
  "pmid": "42872475",
  "published": "2026-10-14",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/9c44b2qzyb27d.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:22:49Z",
      "native_id": "PMC13722282",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bioinformatics/btag772"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "heading:Gene Expression",
      "subfield": "molecular-biology"
    }
  ],
  "title": "scChronos: hierarchical temporal context learning for missing snapshot prediction in single-cell time series",
  "title_text": "scChronos: hierarchical temporal context learning for missing snapshot prediction in single-cell time series",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:49Z",
  "url": "https://research.draconitic.com/paper/9c44b2qzyb27d",
  "venue": "Bioinformatics",
  "versions": []
}