{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:49Z","timestamp":1772138089580,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2018,6,23]],"date-time":"2018-06-23T00:00:00Z","timestamp":1529712000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"doctoral studentship"},{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"crossref"}]},{"name":"postdoctoral fellowship"},{"name":"Canadian Statistical Sciences Institute"},{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"crossref","award":["MR\/P02646X\/1"],"award-info":[{"award-number":["MR\/P02646X\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"crossref","award":["MR\/L001411\/1"],"award-info":[{"award-number":["MR\/L001411\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Pseudotime estimation from single-cell gene expression data allows the recovery of temporal information from otherwise static profiles of individual cells. Conventional pseudotime inference methods emphasize an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. However, the resulting trajectories can only be understood in terms of abstract geometric structures and not in terms of interpretable models of gene behaviour.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here we introduce an orthogonal Bayesian approach termed \u2018Ouija\u2019 that learns pseudotimes from a small set of marker genes that might ordinarily be used to retrospectively confirm the accuracy of unsupervised pseudotime algorithms. Crucially, we model these genes in terms of switch-like or transient behaviour along the trajectory, allowing us to understand why the pseudotimes have been inferred and learn informative parameters about the behaviour of each gene. Since each gene is associated with a switch or peak time the genes are effectively ordered along with the cells, allowing each part of the trajectory to be understood in terms of the behaviour of certain genes. We demonstrate that this small panel of marker genes can recover pseudotimes that are consistent with those obtained using the entire transcriptome. Furthermore, we show that our method can detect differences in the regulation timings between two genes and identify \u2018metastable\u2019 states\u2014discrete cell types along the continuous trajectories\u2014that recapitulate known cell types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>An open source implementation is available as an R package at http:\/\/www.github.com\/kieranrcampbell\/ouija and as a Python\/TensorFlow package at http:\/\/www.github.com\/kieranrcampbell\/ouijaflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty498","type":"journal-article","created":{"date-parts":[[2018,6,21]],"date-time":"2018-06-21T07:10:01Z","timestamp":1529565001000},"page":"28-35","source":"Crossref","is-referenced-by-count":44,"title":["A descriptive marker gene approach to single-cell pseudotime inference"],"prefix":"10.1093","volume":"35","author":[{"given":"Kieran R","family":"Campbell","sequence":"first","affiliation":[{"name":"Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK"},{"name":"Wellcome Trust Centre for Human Genetics University of Oxford, Oxford, UK"}]},{"given":"Christopher","family":"Yau","sequence":"additional","affiliation":[{"name":"Wellcome Trust Centre for Human Genetics University of Oxford, Oxford, UK"},{"name":"Department of Statistics, University of Oxford, Oxford, UK"},{"name":"Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, UK"}]}],"member":"286","published-online":{"date-parts":[[2018,6,23]]},"reference":[{"key":"2023013107202143600_bty498-B1","volume-title":"arXiv preprint","author":"Abadi","year":"2016"},{"key":"2023013107202143600_bty498-B2","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.cell.2014.04.005","article-title":"Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development","volume":"157","author":"Bendall","year":"2014","journal-title":"Cell"},{"key":"2023013107202143600_bty498-B3","doi-asserted-by":"crossref","first-page":"488.","DOI":"10.1186\/1471-2105-9-488","article-title":"Identifying differential correlation in gene\/pathway combinations","volume":"9","author":"Braun","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023013107202143600_bty498-B4","doi-asserted-by":"crossref","first-page":"e1005212","DOI":"10.1371\/journal.pcbi.1005212","article-title":"Order under uncertainty: robust differential expression analysis using probabilistic models for pseudotime inference","volume":"12","author":"Campbell","year":"2016","journal-title":"PLoS Comput. 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