{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T03:12:01Z","timestamp":1776395521166,"version":"3.51.2"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["DOC 33-B27"],"award-info":[{"award-number":["DOC 33-B27"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ERC Synergy","award":["856529"],"award-info":[{"award-number":["856529"]}]},{"name":"Cancerfonden and Paradifference Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g. RNA and ATAC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>scFates is released as open-source software under the BSD 3-Clause \u2018New\u2019 License and is available from the Python Package Index at https:\/\/pypi.org\/project\/scFates\/. The source code is available on GitHub at https:\/\/github.com\/LouisFaure\/scFates\/. Code reproduction and tutorials on published datasets are available on GitHub at https:\/\/github.com\/LouisFaure\/scFates_notebooks.<\/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\/btac746","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T07:45:45Z","timestamp":1668671145000},"source":"Crossref","is-referenced-by-count":91,"title":["scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4621-586X","authenticated-orcid":false,"given":"Louis","family":"Faure","sequence":"first","affiliation":[{"name":"Department of Neuroimmunology, Center for Brain Research, Medical University Vienna , 1090 Vienna, Austria"}]},{"given":"Ruslan","family":"Soldatov","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School , Boston, MA 02115, USA"}]},{"given":"Peter V","family":"Kharchenko","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School , Boston, MA 02115, USA"},{"name":"Altos Labs , San Diego, CA 92121, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5471-0356","authenticated-orcid":false,"given":"Igor","family":"Adameyko","sequence":"additional","affiliation":[{"name":"Department of Neuroimmunology, Center for Brain Research, Medical University Vienna , 1090 Vienna, Austria"}]}],"member":"286","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"2023010107514200100_btac746-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":"2023010107514200100_btac746-B2","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1073\/pnas.1621412114","article-title":"Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells","volume":"114","author":"Bargaje","year":"2017","journal-title":"Proc. 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