{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:19:51Z","timestamp":1784290791440,"version":"3.55.0"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"S5","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The technologies advances of single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) allowed to generate thousands of single cells in a relatively easy and economic manner and it is rapidly advancing the understanding of the cellular composition of complex organisms and tissues. The data structure and feature in scRNA-seq is similar to that in scATAC-seq, therefore, it\u2019s encouraged to identify and classify the cell types in scATAC-seq through traditional supervised machine learning methods, which are proved reliable in scRNA-seq datasets.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we evaluated the classification performance of 6 well-known machine learning methods on scATAC-seq. A total of 4 public scATAC-seq datasets vary in tissues, sizes and technologies were applied to the evaluation of the performance of the methods. We assessed these methods using a 5-folds cross validation experiment, called intra-dataset experiment, based on recall, precision and the percentage of correctly predicted cells. The results show that these methods performed well in some specific types of the cell in a specific scATAC-seq dataset, while the overall performance is not as well as that in scRNA-seq analysis. In addition, we evaluated the classification performance of these methods by training and predicting in different datasets generated from same sample, called inter-datasets experiments, which may help us to assess the performance of these methods in more realistic scenarios.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Both in intra-dataset and in inter-dataset experiment, SVM and NMC are overall outperformed others across all 4 datasets. Thus, we recommend researchers to use SVM and NMC as the underlying classifier when developing an automatic cell-type classification method for scATAC-seq.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04774-z","type":"journal-article","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T18:03:16Z","timestamp":1663783396000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluation of classification in single cell atac-seq data with machine learning methods"],"prefix":"10.1186","volume":"23","author":[{"given":"Hongzhe","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongbo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yadong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8398-3537","authenticated-orcid":false,"given":"Zhe","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"4774_CR1","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/s41587-020-0469-4","volume":"38","author":"E Mereu","year":"2020","unstructured":"Mereu E, Lafzi A, Moutinho C, Ziegenhain C, McCarthy DJ, \u00c1lvarez-Varela A, Batlle E, Sagar GD, Lau JK, et al. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat Biotechnol. 2020;38:747\u201355.","journal-title":"Nat Biotechnol"},{"key":"4774_CR2","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1016\/j.cell.2018.06.052","volume":"174","author":"DA Cusanovich","year":"2018","unstructured":"Cusanovich DA, Hill AJ, Aghamirzaie D, Daza RM, Pliner HA, Berletch JB, Filippova GN, Huang X, Christiansen L, DeWitt WS, et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell. 2018;174:1309-1324.e1318.","journal-title":"Cell"},{"key":"4774_CR3","doi-asserted-by":"publisher","DOI":"10.1101\/456129","author":"F Wagner","year":"2018","unstructured":"Wagner F, Yanai I. Moana: a robust and scalable cell type classification framework for single-cell RNA-Seq data. 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