scChronos: Hierarchical Temporal Context Learning for Missing Snapshot Prediction in Single-Cell Time Series

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ID: 332344
2026
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Abstract
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. Results 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. Availability The code and data are available at https://github.com/yjs193/scChronos and https://doi.org/10.5281/zenodo.20933283.
Reference Key
openalex_W7221785833 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiongsen Yao, Yunfei Zhang, Yong Xu, Jinjin Ma, Si Wu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag772
URL
https://doi.org/10.1093/bioinformatics/btag772
Keywords Keywords not found

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