{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:53:09Z","timestamp":1767178389280,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009829","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000}}],"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1661331"],"award-info":[{"award-number":["1661331"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"USDA","doi-asserted-by":"crossref","award":["2016-51181-25408"],"award-info":[{"award-number":["2016-51181-25408"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The complexity of biological processes such as cell differentiation is reflected in dynamic transitions between cellular states. Trajectory inference arranges the states into a progression using methodologies propelled by single-cell biology. However, current methods, all returning a best trajectory, do not adequately assess statistical significance of noisy patterns, leading to uncertainty in inferred trajectories. We introduce a tree dimension test for trajectory presence in multivariate data by a dimension measure of Euclidean minimum spanning tree, a test statistic, and a null distribution. Computable in linear time to tree size, the tree dimension measure summarizes the extent of branching more effectively than globally insensitive number of leaves or tree diameter indifferent to secondary branches. The test statistic quantifies trajectory presence and its null distribution is estimated under the null hypothesis of no trajectory in data. On simulated and real single-cell datasets, the test outperformed the intuitive number of leaves and tree diameter statistics. Next, we developed a measure for the tissue specificity of the dynamics of a subset, based on the minimum subtree cover of the subset in a minimum spanning tree. We found that tissue specificity of pathway gene expression dynamics is conserved in human and mouse development: several signal transduction pathways including calcium and Wnt signaling are most tissue specific, while genetic information processing pathways such as ribosome and mismatch repair are least so. Neither the tree dimension test nor the subset specificity measure has any user parameter to tune. Our work opens a window to prioritize cellular dynamics and pathways in development and other multivariate dynamical systems.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009829","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T13:33:11Z","timestamp":1644327191000},"page":"e1009829","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Inference of trajectory presence by tree dimension and subset specificity by subtree cover"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9705-2023","authenticated-orcid":true,"given":"Lovemore","family":"Tenha","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6883-6547","authenticated-orcid":true,"given":"Mingzhou","family":"Song","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"issue":"1","key":"pcbi.1009829.ref001","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1186\/s12864-018-4772-0","article-title":"Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics","volume":"19","author":"K Street","year":"2018","journal-title":"BMC Genomics"},{"issue":"9","key":"pcbi.1009829.ref002","doi-asserted-by":"crossref","first-page":"e1008205","DOI":"10.1371\/journal.pcbi.1008205","article-title":"Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data","volume":"16","author":"TN Tran","year":"2020","journal-title":"PLoS Comput Biol"},{"issue":"Supplement_1","key":"pcbi.1009829.ref003","doi-asserted-by":"crossref","first-page":"i66","DOI":"10.1093\/bioinformatics\/btaa463","article-title":"TinGa: fast and flexible trajectory inference with Growing Neural Gas","volume":"36","author":"H Todorov","year":"2020","journal-title":"Bioinformatics"},{"key":"pcbi.1009829.ref004","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41587-019-0071-9","article-title":"A comparison of single-cell trajectory inference methods","volume":"37","author":"W Saelens","year":"2019","journal-title":"Nat Biotechnol"},{"issue":"13","key":"pcbi.1009829.ref005","doi-asserted-by":"crossref","first-page":"e117","DOI":"10.1093\/nar\/gkw430","article-title":"Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis","volume":"44","author":"Z Ji","year":"2016","journal-title":"Nucleic Acids Research"},{"issue":"1","key":"pcbi.1009829.ref006","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13059-016-0975-3","article-title":"SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data","volume":"7","author":"JD Welch","year":"2016","journal-title":"Genome Biology"},{"key":"pcbi.1009829.ref007","unstructured":"Chazal F, Michel B. An introduction to topological data analysis: fundamental and practical aspects for data scientists. arXiv. 2017; p. 1710.04019."},{"issue":"1","key":"pcbi.1009829.ref008","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13059-019-1663-x","article-title":"PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells","volume":"20","author":"FA Wolf","year":"2019","journal-title":"Genome Biology"},{"issue":"215","key":"pcbi.1009829.ref009","first-page":"1","article-title":"Mining topological structure in graphs through forest representations","volume":"21","author":"R Vandaele","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"pcbi.1009829.ref010","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.patrec.2021.03.035","article-title":"Stable topological signatures for metric trees through graph approximations","volume":"147","author":"R Vandaele","year":"2021","journal-title":"Pattern Recognition Letters"},{"key":"pcbi.1009829.ref011","doi-asserted-by":"crossref","unstructured":"March WB, Ram P, Gray AG. Fast Euclidean minimum spanning tree: algorithm, analysis, and applications. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD\u201910. New York, NY, USA: Association for Computing Machinery; 2010. p. 603\u2013612. Available from: https:\/\/doi.org\/10.1145\/1835804.1835882.","DOI":"10.1145\/1835804.1835882"},{"key":"pcbi.1009829.ref012","doi-asserted-by":"crossref","first-page":"726","DOI":"10.21105\/joss.00726","article-title":"mlpack 3: a fast, flexible machine learning library","volume":"3","author":"RR Curtin","year":"2018","journal-title":"Journal of Open Source Software"},{"key":"pcbi.1009829.ref013","unstructured":"Cannoodt R, Saelens W, Todorov H, Saeys Y. Single-cell -omics datasets containing a trajectory (https:\/\/doi.org\/10.5281\/zenodo.1443566); 2018. 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