{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:57Z","timestamp":1772138097783,"version":"3.50.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T00:00:00Z","timestamp":1571011200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"crossref","award":["MC_UU_00002\/1"],"award-info":[{"award-number":["MC_UU_00002\/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":["MC_UU_00002\/10"],"award-info":[{"award-number":["MC_UU_00002\/10"]}],"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":["MC_UU_00002\/13"],"award-info":[{"award-number":["MC_UU_00002\/13"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Many methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and\/or over-confident gene clusters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed method, GPseudoClust, is a novel approach that jointly infers pseudotemporal ordering and gene clusters, and quantifies the uncertainty in both. GPseudoClust combines a recent method for pseudotime inference with non-parametric Bayesian clustering methods, efficient Markov Chain Monte Carlo sampling and novel subsampling strategies which aid computation. We consider a broad array of simulated and experimental datasets to demonstrate the effectiveness of GPseudoClust in a range of settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>An implementation is available on GitHub: https:\/\/github.com\/magStra\/nonparametricSummaryPSM and https:\/\/github.com\/magStra\/GPseudoClust.<\/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\/btz778","type":"journal-article","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T07:54:29Z","timestamp":1570607669000},"page":"1484-1491","source":"Crossref","is-referenced-by-count":9,"title":["GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution"],"prefix":"10.1093","volume":"36","author":[{"given":"Magdalena E","family":"Strauss","sequence":"first","affiliation":[{"name":"Wellcome Sanger Institute , Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK"},{"name":"MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge , Cambridge CB2 0SR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5931-7489","authenticated-orcid":false,"given":"Paul D W","family":"Kirk","sequence":"additional","affiliation":[{"name":"MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge , Cambridge CB2 0SR, UK"},{"name":"Department of Medicine, University of Cambridge , Addenbrooke's Hospital, Cambridge CB2 0SP, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7762-6760","authenticated-orcid":false,"given":"John E","family":"Reid","sequence":"additional","affiliation":[{"name":"MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge , Cambridge CB2 0SR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenz","family":"Wernisch","sequence":"additional","affiliation":[{"name":"MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge , Cambridge CB2 0SR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,10,14]]},"reference":[{"key":"2023060910265130000_btz778-B1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1093\/bioinformatics\/bty533","article-title":"GrandPrix: scaling up the Bayesian GPLVM for single-cell data","volume":"35","author":"Ahmed","year":"2019","journal-title":"Bioinformatics"},{"key":"2023060910265130000_btz778-B2","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"key":"2023060910265130000_btz778-B5","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1038\/nbt.3102","article-title":"Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells","volume":"33","author":"Buettner","year":"2015","journal-title":"Nat. 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