{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:14Z","timestamp":1772138054981,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"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\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI-1846216"],"award-info":[{"award-number":["DBI-1846216"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-2113754"],"award-info":[{"award-number":["DMS-2113754"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health\/NIGMS","doi-asserted-by":"crossref","award":["R01GM120507"],"award-info":[{"award-number":["R01GM120507"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health\/NIGMS","doi-asserted-by":"crossref","award":["R35GM140888"],"award-info":[{"award-number":["R35GM140888"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Johnson and Johnson WiSTEM2D Award; Sloan Research Fellowship"},{"name":"UCLA David Geffen School of Medicine W.M. Keck Foundation Junior Faculty Award; and Chan-Zuckerberg Initiative Single-Cell Biology Data Insights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we propose the single-cell generalized trend model (scGTM) for capturing a gene\u2019s expression trend, which may be monotone, hill-shaped or valley-shaped, along cell pseudotime. The scGTM has three advantages: (i) it can capture non-monotonic trends that are easy to interpret, (ii) its parameters are biologically interpretable and trend informative, and (iii) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression datasets using the scGTM and show that scGTM can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying biological processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The Python package scGTM is open-access and available at https:\/\/github.com\/ElvisCuiHan\/scGTM.<\/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\/btac423","type":"journal-article","created":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T08:32:13Z","timestamp":1656318733000},"page":"3927-3934","source":"Crossref","is-referenced-by-count":6,"title":["Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime"],"prefix":"10.1093","volume":"38","author":[{"given":"Elvis Han","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of California , Los Angeles, CA 90095-1772, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyuan","family":"Song","sequence":"additional","affiliation":[{"name":"Bioinformatics Interdepartmental Ph.D. Program, University of California , Los Angeles, CA 90095-7246, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weng Kee","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of California , Los Angeles, CA 90095-1772, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9288-5648","authenticated-orcid":false,"given":"Jingyi Jessica","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of California , Los Angeles, CA 90095-1772, USA"},{"name":"Bioinformatics Interdepartmental Ph.D. Program, University of California , Los Angeles, CA 90095-7246, USA"},{"name":"Department of Statistics, University of California , Los Angeles, CA 90095-1554, USA"},{"name":"Department of Computational Medicine, University of California , Los Angeles, CA 90095-1766, USA"},{"name":"Department of Human Genetics, University of California , Los Angeles, CA 90095-7088, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2023062704172429900_btac423-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-018-2405-x","article-title":"Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments","volume":"19","author":"Bacher","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2023062704172429900_btac423-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":"2023062704172429900_btac423-B3","first-page":"120","author":"Bratton","year":"2007"},{"key":"2023062704172429900_btac423-B4","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1111\/2041-210X.13559","article-title":"The consequences of checking for zero-inflation and overdispersion in the analysis of count data","volume":"12","author":"Campbell","year":"2021","journal-title":"Methods Ecol. 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