{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T12:48:58Z","timestamp":1776257338718,"version":"3.50.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,11,19]],"date-time":"2018-11-19T00:00:00Z","timestamp":1542585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"K\u00f6ln Fortune","award":["385\/2017"],"award-info":[{"award-number":["385\/2017"]}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["SFB1054"],"award-info":[{"award-number":["SFB1054"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["TP A06"],"award-info":[{"award-number":["TP A06"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recent imaging technologies allow for high-throughput tracking of cells as they migrate, divide, express fluorescent markers and change their morphology. The interpretation of these data requires unbiased, efficient statistical methods that model the dynamics of cell phenotypes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce treeHFM, a probabilistic model which generalizes the theory of hidden Markov models to tree structured data. While accounting for the entire genealogy of a cell, treeHFM categorizes cells according to their primary phenotypic features. It models all relevant events in a cell\u2019s life, including cell division, and thereby enables the analysis of event order and cell fate heterogeneity. Simulations show higher accuracy in predicting correct state labels when modeling the more complex, tree-shaped dependency of samples over standard HMM modeling. Applying treeHFM to time lapse images of hematopoietic progenitor cell differentiation, we demonstrate that progenitor cells undergo a well-ordered sequence of differentiation events.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The treeHFM is implemented in C++. We provide wrapper functions for the programming languages R (CRAN package, https:\/\/CRAN.R-project.org\/package=treeHFM) and Matlab (available at Mathworks Central, http:\/\/se.mathworks.com\/matlabcentral\/fileexchange\/57575-treehfml).<\/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\/bty939","type":"journal-article","created":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T02:12:14Z","timestamp":1542420734000},"page":"2291-2299","source":"Crossref","is-referenced-by-count":4,"title":["Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imaging"],"prefix":"10.1093","volume":"35","author":[{"given":"Henrik","family":"Failmezger","sequence":"first","affiliation":[{"name":"Department of Molecular Pathology, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK"},{"name":"Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ezgi","family":"Dursun","sequence":"additional","affiliation":[{"name":"Department of Medicine, Institute for Immunology, Biomedical Center, Ludwig-Maximilians-University Munich, Martinsried, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"D\u00fcmcke","sequence":"additional","affiliation":[{"name":"Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Max","family":"Endele","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Don","family":"Poron","sequence":"additional","affiliation":[{"name":"Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timm","family":"Schroeder","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne","family":"Krug","sequence":"additional","affiliation":[{"name":"Department of Medicine, Institute for Immunology, Biomedical Center, Ludwig-Maximilians-University Munich, Martinsried, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Achim","family":"Tresch","sequence":"additional","affiliation":[{"name":"Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany"},{"name":"Department of Medicine, Center for Data and Simulation Science, University of Cologne, Cologne, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,11,19]]},"reference":[{"key":"2023051612123140900_bty939-B1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1093\/biostatistics\/kxj033","article-title":"A mutagenetic tree hidden markov model for longitudinal clonal hiv sequence data","volume":"8","author":"Beerenwinkel","year":"2007","journal-title":"Biostatistics (Oxford, England)"},{"key":"2023051612123140900_bty939-B2","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1109\/TPAMI.2002.1039210","article-title":"Infrared-image classification using hidden Markov trees","volume":"24","author":"Bharadwaj","year":"2002","journal-title":"IEEE Trans. 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