{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:55Z","timestamp":1772138095087,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T00:00:00Z","timestamp":1556582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1R01GM122096"],"award-info":[{"award-number":["1R01GM122096"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["OT2OD026682"],"award-info":[{"award-number":["OT2OD026682"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004897","name":"Pennsylvania Department of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004897","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Health Research Nonformula"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Methods for reconstructing developmental trajectories from time-series single-cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed a new method based on continuous-state HMMs (CSHMMs) for representing and modeling time-series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single-cell datasets, we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Software and Supporting website: www.andrew.cmu.edu\/user\/chiehl1\/CSHMM\/<\/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\/btz296","type":"journal-article","created":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T15:18:28Z","timestamp":1555600708000},"page":"4707-4715","source":"Crossref","is-referenced-by-count":43,"title":["Continuous-state HMMs for modeling time-series single-cell RNA-Seq data"],"prefix":"10.1093","volume":"35","author":[{"given":"Chieh","family":"Lin","sequence":"first","affiliation":[{"name":"Machine Learning Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, US"}]},{"given":"Ziv","family":"Bar-Joseph","sequence":"additional","affiliation":[{"name":"Machine Learning Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, US"},{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, US"}]}],"member":"286","published-online":{"date-parts":[[2019,4,30]]},"reference":[{"key":"2023013108325278000_btz296-B1","author":"Ainsleigh","year":"2001"},{"key":"2023013108325278000_btz296-B2","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1038\/nrg3244","article-title":"Studying and modelling dynamic biological processes using time-series gene expression data","volume":"13","author":"Bar-Joseph","year":"2012","journal-title":"Nat. Rev. Genet"},{"key":"2023013108325278000_btz296-B3","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":"2023013108325278000_btz296-B4","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. Biotechnol"},{"key":"2023013108325278000_btz296-B5","doi-asserted-by":"crossref","first-page":"7190","DOI":"10.1523\/JNEUROSCI.4646-14.2015","article-title":"Bex3 dimerization regulates NGF-dependent neuronal survival and differentiation by enhancing trkA gene transcription","volume":"35","author":"Calvo","year":"2015","journal-title":"J. Neurosci"},{"key":"2023013108325278000_btz296-B6","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. Biol"},{"key":"2023013108325278000_btz296-B7","first-page":"gr-225979","article-title":"Reconstructing differentiation networks and their regulation from time series single cell expression data","author":"Ding","year":"2018","journal-title":"Genome Res"},{"key":"2023013108325278000_btz296-B8","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1136\/thoraxjnl-2015-207035","article-title":"\u2018lunggens\u2019: a web-based tool for mapping single-cell gene expression in the developing lung","volume":"70","author":"Du","year":"2015","journal-title":"Thorax"},{"key":"2023013108325278000_btz296-B9","doi-asserted-by":"crossref","first-page":"eaar3131.","DOI":"10.1126\/science.aar3131","article-title":"Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis","volume":"360","author":"Farrell","year":"2018","journal-title":"Science"},{"key":"2023013108325278000_btz296-B10","author":"Gutierrez","year":"2016"},{"key":"2023013108325278000_btz296-B11","doi-asserted-by":"crossref","first-page":"845.","DOI":"10.1038\/nmeth.3971","article-title":"Diffusion pseudotime robustly reconstructs lineage branching","volume":"13","author":"Haghverdi","year":"2016","journal-title":"Nat. Methods"},{"key":"2023013108325278000_btz296-B12","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1038\/nmeth.2967","article-title":"Bayesian approach to single-cell differential expression analysis","volume":"11","author":"Kharchenko","year":"2014","journal-title":"Nat. Methods"},{"key":"2023013108325278000_btz296-B13","doi-asserted-by":"crossref","first-page":"i147","DOI":"10.1093\/bioinformatics\/btn152","article-title":"Alignment and classification of time series gene expression in clinical studies","volume":"24","author":"Lin","year":"2008","journal-title":"Bioinformatics"},{"key":"2023013108325278000_btz296-B14","doi-asserted-by":"crossref","first-page":"pii: eaal2192","DOI":"10.1126\/sciimmunol.aal2192","article-title":"Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves th1\/tfh fate bifurcation in malaria","volume":"2","author":"L\u00f6nnberg","year":"2017","journal-title":"Sci. Immunol"},{"key":"2023013108325278000_btz296-B15","doi-asserted-by":"crossref","first-page":"E5643","DOI":"10.1073\/pnas.1408993111","article-title":"Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape","volume":"111","author":"Marco","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023013108325278000_btz296-B16","doi-asserted-by":"crossref","first-page":"9768","DOI":"10.1073\/pnas.1333958100","article-title":"Characterization of myotubularin-related protein 7 and its binding partner, myotubularin-related protein 9","volume":"100","author":"Mochizuki","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023013108325278000_btz296-B17","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1080\/01902140590918830","article-title":"Extracellular matrix-driven alveolar epithelial cell differentiation in vitro","volume":"31","author":"Olsen","year":"2005","journal-title":"Exp. Lung Res"},{"key":"2023013108325278000_btz296-B18","doi-asserted-by":"crossref","first-page":"309.","DOI":"10.1038\/nmeth.4150","article-title":"Single-cell mRNA quantification and differential analysis with census","volume":"14","author":"Qiu","year":"2017","journal-title":"Nat. Methods"},{"key":"2023013108325278000_btz296-B19","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1093\/bioinformatics\/btx173","article-title":"TASIC: determining branching models from time series single cell data","volume":"33","author":"Rashid","year":"2017","journal-title":"Bioinformatics"},{"key":"2023013108325278000_btz296-B20","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1093\/bioinformatics\/btw372","article-title":"Pseudotime estimation: deconfounding single cell time series","volume":"32","author":"Reid","year":"2016","journal-title":"Bioinformatics"},{"key":"2023013108325278000_btz296-B21","doi-asserted-by":"crossref","first-page":"W83-9","DOI":"10.1093\/nar\/gkw199","article-title":"g: profiler a web server for functional interpretation of gene lists (2016 update)","volume":"44","author":"Reimand","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023013108325278000_btz296-B22","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1038\/nbt.3854","article-title":"Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development","volume":"35","author":"Rizvi","year":"2017","journal-title":"Nat. Biotechnol"},{"key":"2023013108325278000_btz296-B23","doi-asserted-by":"crossref","first-page":"637.","DOI":"10.1038\/nbt.3569","article-title":"Wishbone identifies bifurcating developmental trajectories from single-cell data","volume":"34","author":"Setty","year":"2016","journal-title":"Nat. Biotechnol"},{"key":"2023013108325278000_btz296-B24","doi-asserted-by":"crossref","first-page":"236.","DOI":"10.1038\/nature12172","article-title":"Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells","volume":"498","author":"Shalek","year":"2013","journal-title":"Nature"},{"key":"2023013108325278000_btz296-B25","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1038\/nrg3542","article-title":"Single-cell sequencing-based technologies will revolutionize whole-organism science","volume":"14","author":"Shapiro","year":"2013","journal-title":"Nat. Rev. Genet"},{"key":"2023013108325278000_btz296-B26","doi-asserted-by":"crossref","first-page":"15599.","DOI":"10.1038\/ncomms15599","article-title":"Single-cell entropy for accurate estimation of differentiation potency from a cell\u2019s transcriptome","volume":"8","author":"Teschendorff","year":"2017","journal-title":"Nat. Commun"},{"key":"2023013108325278000_btz296-B27","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Statist. Soc. Series B Methodol"},{"key":"2023013108325278000_btz296-B28","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nbt.2859","article-title":"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells","volume":"32","author":"Trapnell","year":"2014","journal-title":"Nat. Biotechnol"},{"key":"2023013108325278000_btz296-B29","doi-asserted-by":"crossref","first-page":"371.","DOI":"10.1038\/nature13173","article-title":"Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq","volume":"509","author":"Treutlein","year":"2014","journal-title":"Nature"},{"key":"2023013108325278000_btz296-B30","doi-asserted-by":"crossref","first-page":"391.","DOI":"10.1038\/nature18323","article-title":"Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq","volume":"534","author":"Treutlein","year":"2016","journal-title":"Nature"},{"key":"2023013108325278000_btz296-B31","doi-asserted-by":"crossref","first-page":"3903","DOI":"10.1073\/pnas.1621177114","article-title":"Transcription factor etv5 is essential for the maintenance of alveolar type ii cells","volume":"114","author":"Zhang","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btz296\/28665328\/btz296.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/22\/4707\/48978437\/bioinformatics_35_22_4707.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/22\/4707\/48978437\/bioinformatics_35_22_4707.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T22:29:43Z","timestamp":1721168983000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/35\/22\/4707\/5481957"}},"subtitle":[],"editor":[{"given":"Janet","family":"Kelso","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,4,30]]},"references-count":31,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2019,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btz296","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/380568","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,11,15]]},"published":{"date-parts":[[2019,4,30]]}}}