{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:14:03Z","timestamp":1760235243684,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21775107"],"award-info":[{"award-number":["21775107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an autoencoder\u2013decoder, a symmetry neural network architecture, and non-negative matrix factorization methods to characterize the high-dimensional data. To evaluate the performance of multiple methods, in this work, we performed a multiple comparison for characterizing scATAC-seq based on four kinds of auto-encoders known as a symmetry neural network, and two kinds of matrix factorization methods. Different sizes of latent features were used to generate the UMAP plots and for further K-means clustering. Using a gold-standard data set, we practically explored the performance among the methods and the number of latent features in a comprehensive way. Finally, we briefly discuss the underlying difficulties and future directions for scATAC-seq characterizing. As a result, the method designed for handling the sparsity outperforms other tools in the generated dataset.<\/jats:p>","DOI":"10.3390\/sym13081467","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T02:14:41Z","timestamp":1628648081000},"page":"1467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition"],"prefix":"10.3390","volume":"13","author":[{"given":"Yuyao","family":"Huang","sequence":"first","affiliation":[{"name":"College of Chemistry, Sichuan University, Chengdu 610064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhou","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Chemistry, Sichuan University, Chengdu 610064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runyu","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Menglong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Chemistry, Sichuan University, Chengdu 610064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1038\/nmeth.2688","article-title":"Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position","volume":"10","author":"Buenrostro","year":"2013","journal-title":"Nat. Methods"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1038\/nature14590","article-title":"Single-cell chromatin accessibility reveals principles of regulatory variation","volume":"523","author":"Buenrostro","year":"2015","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1126\/science.aab1601","article-title":"Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing","volume":"348","author":"Cusanovich","year":"2015","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1038\/s41588-021-00790-6","article-title":"ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis","volume":"53","author":"Granja","year":"2021","journal-title":"Nat. Genet."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive Integration of Single-Cell Data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"SCANPY: Large-scale single-cell gene expression data analysis","volume":"19","author":"Wolf","year":"2018","journal-title":"Genome Biol."},{"key":"ref_7","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":"ref_8","unstructured":"Fang, R., Preissl, S., Hou, X., Lucero, J., and Ren, B. (2019). Fast and Accurate Clustering of Single Cell Epigenomes Reveals Cis-Regulatory Elements in Rare Cell Types. bioRxiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.1093\/nar\/gky950","article-title":"Classifying cells with Scasat, a single-cell ATAC-seq analysis tool","volume":"47","author":"Murtuza","year":"2019","journal-title":"Nuclc Acids Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1038\/s41592-019-0367-1","article-title":"cisTopic: Cis-regulatory topic modeling on single-cell ATAC-seq data","volume":"16","author":"Minnoye","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1038\/s41467-018-04629-3","article-title":"Unsupervised clustering and epigenetic classification of single cells","volume":"9","author":"Mahdi","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13059-020-02008-0","article-title":"ScATAC-pro: A comprehensive workbench for single-cell chromatin accessibility sequencing data","volume":"21","author":"Yu","year":"2020","journal-title":"Genome Biol."},{"key":"ref_13","unstructured":"Kingma, D.P., and Welling, M. (2014). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","article-title":"Deep generative modeling for single-cell transcriptomics","volume":"15","author":"Lopez","year":"2018","journal-title":"Nat. Methods"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1038\/s41467-019-12630-7","article-title":"SCALE method for single-cell ATAC-seq analysis via latent feature extraction","volume":"10","author":"Xiong","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4415","DOI":"10.1093\/bioinformatics\/btaa293","article-title":"scVAE: Variational auto-encoders for single-cell gene expression data","volume":"36","author":"Grnbech","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cao, Y., Fu, L., Wu, J., Peng, Q., and Xie, X. (2021). SAILER: Scalable and Accurate Invariant Representation Learning for Single-Cell ATAC-Seq Processing and Integration. bioRxiv.","DOI":"10.1101\/2021.01.28.428689"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/aic.690370209","article-title":"Nonlinear principal component analysis using autoassociative neural networks","volume":"37","author":"Kramer","year":"1991","journal-title":"AIChE J."},{"key":"ref_19","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_20","unstructured":"Makhzani, A., and Frey, B. (2013). k-Sparse Autoencoders. arXiv."},{"key":"ref_21","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"Ng","year":"2011","journal-title":"CS294A Lect. Notes"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bottou, L., Chapelle, O., DeCoste, D., and Weston, J. (2007). Scaling Learning Algorithms Towards AI. Large-Scale Kernel, Machines, MIT Press.","DOI":"10.7551\/mitpress\/7496.001.0001"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/bioinformatics\/btw607","article-title":"Robust classification of single-cell transcriptome data by nonnegative matrix factorization","volume":"33","author":"Shao","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1038\/s41593-018-0079-3","article-title":"Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation","volume":"21","author":"Preissl","year":"2018","journal-title":"Nat. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","volume":"3","author":"Mcinnes","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_26","first-page":"2579","article-title":"Visualizing High-Dimensional Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","first-page":"8026","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., Krizhevsky, A., and Wang, S.D. (2011). Transforming Auto-Encoders, Springer.","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2013.09.055","article-title":"Autoencoder for words","volume":"139","author":"Liou","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_31","first-page":"5105709","article-title":"A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis","volume":"2018","author":"Liu","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_32","unstructured":"Doersch, C. (2016). Tutorial on Variational Autoencoders. arXiv."},{"key":"ref_33","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Dilokthanakul, N., Mediano, P., Garnelo, M., Lee, M., Salimbeni, H., Arulkumaran, K., and Shanahan, M. (2016). Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"ref_36","unstructured":"Dhillon, I., and Sra, S. (2005). Generalized Nonnegative Matrix Approximations with Bregman Divergences. Neural Information Processing Systems, MIT Press."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3847\/1538-4357\/ab7024","article-title":"Using Data Imputation for Signal Separation in High Contrast Imaging","volume":"892","author":"Ren","year":"2020","journal-title":"Astrophys. J."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ben, M., Thomas, W., Jan, B., Robert, K., Sasha, M., Gerdus, B., Du, B.L., Daniel, K., Tristan, H., and Konrad, S. (2011). Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0028898"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"ref_40","first-page":"1457","article-title":"Nonnegative matrix factorization with sparseness constraints","volume":"5","author":"Hoyer","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","first-page":"849","article-title":"NIMFA: A Python Library for Nonnegative Matrix Factorization","volume":"13","author":"Zitnik","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1162\/neco.2007.19.10.2756","article-title":"Projected Gradient Methods for Nonnegative Matrix Factorization","volume":"19","author":"Lin","year":"2014","journal-title":"Neural Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1186\/1471-2105-7-175","article-title":"LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates","volume":"7","author":"Wang","year":"2006","journal-title":"BMC Bioinform."},{"key":"ref_44","unstructured":"Arthur, D., and Vassilvitskii, S. (2007, January 7\u20139). k-means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Classif."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/8\/1467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:43:49Z","timestamp":1760165029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/8\/1467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,11]]},"references-count":45,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["sym13081467"],"URL":"https:\/\/doi.org\/10.3390\/sym13081467","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,8,11]]}}}