{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:44:01Z","timestamp":1753875841355,"version":"3.41.2"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["12222115"],"award-info":[{"award-number":["12222115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The development of single-cell RNA sequencing (scRNA-seq) technology makes it possible to study the cellular dynamic processes such as cell cycle and cell differentiation. Due to the difficulties in generating genuine time-series scRNA-seq data, it is of great importance to computationally infer the pseudotime of the cells along differentiation trajectory based on their gene expression patterns. The existing pseudotime prediction methods often suffer from the high level noise of single-cell data, thus it is still necessary to study the single-cell trajectory inference methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we propose a branched local tangent space alignment (BLTSA) method to infer single-cell pseudotime for multi-furcation trajectories. By assuming that single cells are sampled from a low-dimensional self-intersecting manifold, BLTSA first identifies the tip and branching cells in the trajectory based on cells\u2019 local Euclidean neighborhoods. Local coordinates within the tangent spaces are then determined by each cell\u2019s local neighborhood after clustering all the cells to different branches iteratively. The global coordinates for all the single cells are finally obtained by aligning the local coordinates based on the tangent spaces. We evaluate the performance of BLTSA on four simulation datasets and five real datasets. The experimental results show that BLTSA has obvious advantages over other comparison methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>R codes are available at https:\/\/github.com\/LiminLi-xjtu\/BLTSA.<\/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\/btad054","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T13:52:58Z","timestamp":1674568378000},"source":"Crossref","is-referenced-by-count":1,"title":["BLTSA: pseudotime prediction for single cells by branched local tangent space alignment"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3572-6832","authenticated-orcid":false,"given":"Limin","family":"Li","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi\u2019an 710049, China"}]},{"given":"Yameng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi\u2019an 710049, China"}]},{"given":"Huiran","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-844X","authenticated-orcid":false,"given":"Shuqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Fudan University , Shanghai 200433, China"}]}],"member":"286","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"2023021310393242300_btad054-B1","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1093\/bioinformatics\/btv715","article-title":"Destiny: diffusion maps for large-scale single-cell data in R","volume":"32","author":"Angerer","year":"2016","journal-title":"Bioinformatics"},{"key":"2023021310393242300_btad054-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-0927-y","article-title":"Design and computational analysis of single-cell RNA-sequencing experiments","volume":"17","author":"Bacher","year":"2016","journal-title":"Genome Biol"},{"key":"2023021310393242300_btad054-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":"2023021310393242300_btad054-B4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.12688\/wellcomeopenres.11087.1","article-title":"Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers","volume":"2","author":"Campbell","year":"2017","journal-title":"Wellcome Open Res"},{"key":"2023021310393242300_btad054-B5","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1093\/bioinformatics\/bty498","article-title":"A descriptive marker gene approach to single-cell pseudotime inference","volume":"35","author":"Campbell","year":"2019","journal-title":"Bioinformatics"},{"key":"2023021310393242300_btad054-B6","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/s41586-019-0969-x","article-title":"The single-cell transcriptional landscape of mammalian organogenesis","volume":"566","author":"Cao","year":"2019","journal-title":"Nature"},{"key":"2023021310393242300_btad054-B7","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1093\/bioinformatics\/bty1009","article-title":"Densitypath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2023021310393242300_btad054-B8","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.devcel.2010.02.012","article-title":"Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst","volume":"18","author":"Guo","year":"2010","journal-title":"Dev. Cell"},{"key":"2023021310393242300_btad054-B9","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":"2023021310393242300_btad054-B10","doi-asserted-by":"crossref","first-page":"e117","DOI":"10.1093\/nar\/gkw430","article-title":"Tscan: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis","volume":"44","author":"Ji","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023021310393242300_btad054-B11","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1126\/science.aan6826","article-title":"Single-cell epigenomics: recording the past and predicting the future","volume":"358","author":"Kelsey","year":"2017","journal-title":"Science"},{"key":"2023021310393242300_btad054-B12","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1038\/s41556-018-0123-2","article-title":"Single-cell multi-omics sequencing of human early embryos","volume":"20","author":"Li","year":"2018","journal-title":"Nat. Cell Biol"},{"key":"2023021310393242300_btad054-B13","doi-asserted-by":"crossref","first-page":"182","DOI":"10.12688\/f1000research.7223.1","article-title":"Single-cell transcriptome sequencing: recent advances and remaining challenges","volume":"5","author":"Liu","year":"2016","journal-title":"F1000Res"},{"key":"2023021310393242300_btad054-B14","first-page":"1","article-title":"Reconstructing cell cycle pseudo time-series via single-cell transcriptome data","volume":"8","author":"Liu","year":"2017","journal-title":"Nat. Commun"},{"key":"2023021310393242300_btad054-B15","doi-asserted-by":"crossref","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":"2023021310393242300_btad054-B16","doi-asserted-by":"crossref","first-page":"7909","DOI":"10.1093\/nar\/gkab457","article-title":"Pseudoga: cell pseudotime reconstruction based on genetic algorithm","volume":"49","author":"Mondal","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023021310393242300_btad054-B17","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/j.cell.2016.03.023","article-title":"Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos","volume":"165","author":"Petropoulos","year":"2016","journal-title":"Cell"},{"key":"2023021310393242300_btad054-B18","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1038\/nmeth.4402","article-title":"Reversed graph embedding resolves complex single-cell trajectories","volume":"14","author":"Qiu","year":"2017","journal-title":"Nat. Methods"},{"key":"2023021310393242300_btad054-B19","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":"Saelens","year":"2019","journal-title":"Nat. Biotechnol"},{"key":"2023021310393242300_btad054-B20","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":"2023021310393242300_btad054-B21","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41587-019-0068-4","article-title":"Characterization of cell fate probabilities in single-cell data with Palantir","volume":"37","author":"Setty","year":"2019","journal-title":"Nat. Biotechnol"},{"key":"2023021310393242300_btad054-B22","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/nature13437","article-title":"Single-cell RNA-seq reveals dynamic paracrine control of cellular variation","volume":"510","author":"Shalek","year":"2014","journal-title":"Nature"},{"key":"2023021310393242300_btad054-B23","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.stem.2015.07.013","article-title":"Single-cell RNA-seq with waterfall reveals molecular Cascades underlying adult neurogenesis","volume":"17","author":"Shin","year":"2015","journal-title":"Cell Stem Cell"},{"key":"2023021310393242300_btad054-B24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-018-4772-0","article-title":"Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics","volume":"19","author":"Street","year":"2018","journal-title":"BMC Genomics"},{"key":"2023021310393242300_btad054-B25","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1126\/science.aan6828","article-title":"Single-cell transcriptomics to explore the immune system in health and disease","volume":"358","author":"Stubbington","year":"2017","journal-title":"Science"},{"key":"2023021310393242300_btad054-B26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-01860-2","article-title":"Inference of differentiation time for single cell transcriptomes using cell population reference data","volume":"8","author":"Sun","year":"2017","journal-title":"Nat. Commun"},{"key":"2023021310393242300_btad054-B27","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1101\/gr.190595.115","article-title":"Defining cell types and states with single-cell genomics","volume":"25","author":"Trapnell","year":"2015","journal-title":"Genome Res"},{"key":"2023021310393242300_btad054-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":"2023021310393242300_btad054-B29","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1126\/science.aar4362","article-title":"Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo","volume":"360","author":"Wagner","year":"2018","journal-title":"Science"},{"key":"2023021310393242300_btad054-B30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-0975-3","article-title":"Slicer: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data","volume":"17","author":"Welch","year":"2016","journal-title":"Genome Biol"},{"key":"2023021310393242300_btad054-B31","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":"Wolf","year":"2019","journal-title":"Genome Biol"},{"key":"2023021310393242300_btad054-B32","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1137\/S1064827502419154","article-title":"Principal manifolds and nonlinear dimensionality reduction via tangent space alignment","volume":"26","author":"Zhang","year":"2004","journal-title":"SIAM J. Sci. Comput"},{"year":"2021","author":"Zhang","key":"2023021310393242300_btad054-B33"},{"key":"2023021310393242300_btad054-B34","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/TPAMI.2011.115","article-title":"Adaptive manifold learning","volume":"34","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btad054\/48845101\/btad054.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/2\/btad054\/49167303\/btad054.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/2\/btad054\/49167303\/btad054.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T10:41:09Z","timestamp":1676284869000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btad054\/7000337"}},"subtitle":[],"editor":[{"given":"Olga","family":"Vitek","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,1,24]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad054","relation":{},"ISSN":["1367-4811"],"issn-type":[{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2023,2,1]]},"published":{"date-parts":[[2023,1,24]]},"article-number":"btad054"}}