{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T10:01:33Z","timestamp":1789466493483,"version":"build-2803163510"},"posted":{"date-parts":[[2026,9,10]]},"group-title":"Bioinformatics","reference-count":34,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"award":["R00 GR052357"],"award-info":[{"award-number":["R00 GR052357"]}],"id":[{"id":"https:\/\/ror.org\/00baak391","id-type":"ROR","asserted-by":"publisher"}]},{"award":["UM1HG011586"],"award-info":[{"award-number":["UM1HG011586"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"crossref","award":["GM131745"],"award-info":[{"award-number":["GM131745"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  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\n                  <jats:italic>in vivo<\/jats:italic>\n                  temporal mapping for\n                  <jats:italic>in vitro<\/jats:italic>\n                  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.\n                <\/jats:p>","DOI":"10.64898\/2026.09.04.749461","type":"posted-content","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:55:20Z","timestamp":1789106120000},"source":"Crossref","is-referenced-by-count":0,"title":["Reference-guided pseudotime inference across species and biological contexts"],"prefix":"10.64898","author":[{"given":"Natalie","family":"Rittenhouse","sequence":"first","affiliation":[{"name":"School of Data and Information Science, University of North Carolina - Chapel Hill"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth","family":"Dannenfelser","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rice University, Houston, Texas 77005, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Galina N","family":"Filippova","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vicky","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rice University, Houston, Texas 77005, USA"},{"name":"Synthetic Biology Institute and Ken Kennedy Institute, Rice University, Houston, Texas 77005, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxian","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine M.","family":"Disteche","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA"},{"name":"Department of Medicine, University of Washington, Seattle, Washington 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Data and Information Science, University of North Carolina - 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