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Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>scShaper is available as an R package at https:\/\/github.com\/elolab\/scshaper. The test data are available at https:\/\/doi.org\/10.5281\/zenodo.5734488.<\/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\/btab831","type":"journal-article","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T15:12:43Z","timestamp":1638544363000},"page":"1328-1335","source":"Crossref","is-referenced-by-count":18,"title":["scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3872-9668","authenticated-orcid":false,"given":"Johannes","family":"Smolander","sequence":"first","affiliation":[{"name":"Turku Bioscience Centre, University of Turku and \u00c5bo Akademi University, Tykist\u00f6katu 6, 20520 Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3754-5584","authenticated-orcid":false,"given":"Sini","family":"Junttila","sequence":"additional","affiliation":[{"name":"Turku Bioscience Centre, University of Turku and \u00c5bo Akademi University, Tykist\u00f6katu 6, 20520 Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikko S","family":"Ven\u00e4l\u00e4inen","sequence":"additional","affiliation":[{"name":"Turku Bioscience Centre, University of Turku and \u00c5bo Akademi University, Tykist\u00f6katu 6, 20520 Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura L","family":"Elo","sequence":"additional","affiliation":[{"name":"Turku Bioscience Centre, University of Turku and \u00c5bo Akademi University, Tykist\u00f6katu 6, 20520 Turku, Finland"},{"name":"Institute of Biomedicine, University of Turku , 20520 Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"2023020108560913600_btab831-B1","doi-asserted-by":"crossref","first-page":"296","DOI":"10.3390\/e22030296","article-title":"Robust and scalable learning of complex intrinsic dataset geometry via ElPiGraph","volume":"22","author":"Albergante","year":"2020","journal-title":"Entropy"},{"key":"2023020108560913600_btab831-B2","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1038\/s41587-020-0591-3","article-title":"Generalizing RNA velocity to transient cell states through dynamical modeling","volume":"38","author":"Bergen","year":"2020","journal-title":"Nat. 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