Reference-guided pseudotime inference across species and biological contexts
- 1University of North Carolina - Chapel Hill
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.
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BibTeX
@misc{rittenhouse2026referenceguided,
title = {{Reference-guided pseudotime inference across species and biological contexts}},
author = {N. Rittenhouse and R. Dannenfelser and G. N. Filippova and V. Yao and X. Deng and C. M. Disteche and R. Zhang},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.04.749461},
url = {https://www.biorxiv.org/content/10.64898/2026.09.04.749461v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Reference-guided pseudotime inference across species and biological contexts
AU - N. Rittenhouse
AU - R. Dannenfelser
AU - G. N. Filippova
AU - V. Yao
AU - X. Deng
AU - C. M. Disteche
AU - R. Zhang
PY - 2026
DA - 2026/09/04
PB - bioRxiv
DO - 10.64898/2026.09.04.749461
UR - https://www.biorxiv.org/content/10.64898/2026.09.04.749461v1
AB - 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.
ER -