Authors
Natalie Rittenhouse, Ruth Dannenfelser, Galina N Filippova, Vicky Yao, Xinxian Deng, Christine M Disteche, Ran Zhang
Published in
bioRxiv : the preprint server for biology. Sep 10, 2026. Epub Sep 10, 2026.
Abstract
Cells collected at the same chronological age can vary substantially in biological age due to the heterogeneity in the timing of differentiation, speed of maturation, and degeneration. However, existing pseudotime inference methods either disregard chronological time information, or rely on accurate time-series labels within similar species or biological conditions of interest. As a result, both types of strategies often fail to faithfully order cells from biological contexts without reliable time labels, along the desired axis of interest such as human embryonic development or disease progression. Here, we propose Cavebear, a machine learning framework that enables pseudotime inference in a query species or condition guided by scRNA-seq time-series profiles from a reference species or condition. Cavebear achieves more accurate developmental pseudotime inference than existing methods and provides in vivo temporal mapping for in vitro experiments. Furthermore, we illustrate the potential of Cavebear to study cellular-level disease progression in human patients using mouse cancer development models as references. By transferring temporal information across species and conditions, Cavebear enables systematic investigation of biological variation in contexts where such annotations were previously unattainable.
PMID:
42818223
Bibliographic data and abstract were imported from PubMed on 01 Oct 2026.
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