{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T02:58:36Z","timestamp":1781492316676,"version":"3.54.1"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural 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"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92470106"],"award-info":[{"award-number":["92470106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12471350"],"award-info":[{"award-number":["12471350"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["23JC1401000"],"award-info":[{"award-number":["23JC1401000"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding the functions and behaviors of living organisms. However, the acquisition of post-perturbation cellular states via biological experiments is frequently cost-prohibitive. Predicting the single-cell perturbation responses poses a critical challenge in the field of computational biology. In this work, we propose a novel deep learning method called coupled variational autoencoders (CoupleVAE), devised to predict the postperturbation single-cell RNA-Seq data. CoupleVAE is composed of two coupled VAEs connected by a coupler, initially extracting latent features for controlled and perturbed cells via two encoders, subsequently engaging in mutual translation within the latent space through two nonlinear mappings via a coupler, and ultimately generating controlled and perturbed data by two separate decoders to process the encoded and translated features. CoupleVAE facilitates a more intricate state transformation of single cells within the latent space. Experiments in three real datasets on infection, stimulation and cross-species prediction show that CoupleVAE surpasses the existing comparative models in effectively predicting single-cell RNA-seq data for perturbed cells, achieving superior accuracy.<\/jats:p>","DOI":"10.1093\/bib\/bbaf126","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T09:39:55Z","timestamp":1743673195000},"source":"Crossref","is-referenced-by-count":5,"title":["CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data"],"prefix":"10.1093","volume":"26","author":[{"given":"Yahao","family":"Wu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Xi\u2019an, Shaanxi 710049 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Xi\u2019an, Shaanxi 710049 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanni","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Xi\u2019an, Shaanxi 710049 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-844X","authenticated-orcid":false,"given":"Shuqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences , Center for Applied Mathematics, Research Institute of Intelligent Complex Systems, and Shanghai Key Laboratory for Contemporary Applied Mathematics, , 220 Handan Road, 200433 Shanghai ,","place":["China"]},{"name":"Fudan University , Center for Applied Mathematics, Research Institute of Intelligent Complex Systems, and Shanghai Key Laboratory for Contemporary Applied Mathematics, , 220 Handan Road, 200433 Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3572-6832","authenticated-orcid":false,"given":"Limin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , No. 28 Xianning West Road, Xi\u2019an, Shaanxi 710049 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"2025040311313365900_ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-32869-x","article-title":"Heterogeneity and transcriptome changes of human cd8+ t cells across nine decades of life","volume":"13","author":"Lu","year":"2022","journal-title":"Nat Commun"},{"key":"2025040311313365900_ref2","doi-asserted-by":"publisher","first-page":"5275","DOI":"10.1038\/s41467-020-19012-4","article-title":"Single-cell transcriptomics identifies divergent developmental lineage trajectories during human pituitary development","volume":"11","author":"Zhang","year":"2020","journal-title":"Nat Commun"},{"key":"2025040311313365900_ref3","doi-asserted-by":"publisher","first-page":"e1007488","DOI":"10.1371\/journal.pcbi.1007488","article-title":"Quantifying pluripotency landscape of cell differentiation from scrna-seq data by continuous birth-death process","volume":"15","author":"Shi","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2025040311313365900_ref4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s42256-023-00763-w","article-title":"Reconstructing growth and dynamic trajectories from single-cell transcriptomics data","volume":"6","author":"Sha","year":"2023","journal-title":"Nat Mach Intell"},{"key":"2025040311313365900_ref5","doi-asserted-by":"publisher","first-page":"bbac324","DOI":"10.1093\/bib\/bbac324","article-title":"Celldrift: Inferring perturbation responses in temporally sampled single-cell data","volume":"23","author":"Jin","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025040311313365900_ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-33170-7","article-title":"The immune landscape of human thymic epithelial tumors","volume":"13","author":"Xin","year":"2022","journal-title":"Nat Commun"},{"key":"2025040311313365900_ref7","first-page":"26711","article-title":"Predicting cellular responses to novel drug perturbations at a single-cell resolution","volume":"35","author":"Hetzel","year":"2022","journal-title":"Adv Neural Inform Process Syst"},{"key":"2025040311313365900_ref8","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1016\/j.cell.2016.11.038","article-title":"Perturb-seq: Dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens","volume":"167","author":"Dixit","year":"2016","journal-title":"Cell"},{"key":"2025040311313365900_ref9","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1016\/j.cell.2016.11.048","article-title":"A multiplexed single-cell crispr screening platform enables systematic dissection of the unfolded protein response","volume":"167","author":"Adamson","year":"2016","journal-title":"Cell"},{"key":"2025040311313365900_ref10","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1038\/nmeth.4177","article-title":"Pooled crispr screening with single-cell transcriptome readout","volume":"14","author":"Datlinger","year":"2017","journal-title":"Nat Methods"},{"key":"2025040311313365900_ref11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-021-27611-y","article-title":"Mechanisms of sars-cov-2 neutralization by shark variable new antigen receptors elucidated through X-ray crystallography","volume":"12","author":"Ubah","year":"2021","journal-title":"Nat Commun"},{"key":"2025040311313365900_ref12","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.cels.2021.05.016","article-title":"Machine learning for perturbational single-cell omics","volume":"12","author":"Ji","year":"2021","journal-title":"Cell Syst"},{"key":"2025040311313365900_ref13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1126\/science.aax6234","article-title":"Massively multiplex chemical transcriptomics at single-cell resolution","volume":"367","author":"Srivatsan","year":"2020","journal-title":"Science"},{"key":"2025040311313365900_ref14","doi-asserted-by":"publisher","first-page":"5076","DOI":"10.1093\/bioinformatics\/btaa624","article-title":"Style transfer with variational autoencoders is a promising approach to rna-seq data harmonization and analysis","volume":"36","author":"Russkikh","year":"2020","journal-title":"Bioinformatics"},{"key":"2025040311313365900_ref15","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.cels.2018.10.013","article-title":"Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model","volume":"7","author":"Fr\u00f6hlich","year":"2018","journal-title":"Cell Syst"},{"key":"2025040311313365900_ref16","first-page":"351767","article-title":"Inferring reaction networks using perturbation data","author":"Choi","year":"2018","journal-title":"BioRxiv"},{"key":"2025040311313365900_ref17","doi-asserted-by":"publisher","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":"2025040311313365900_ref18","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1038\/s41467-018-07931-2","article-title":"Single-cell rna-seq denoising using a deep count autoencoder","volume":"10","author":"Eraslan","year":"2019","journal-title":"Nat Commun"},{"key":"2025040311313365900_ref19","first-page":"262501","article-title":"Generative adversarial networks simulate gene expression and predict perturbations in single cells","author":"Ghahramani","year":"2018","journal-title":"BioRxiv"},{"key":"2025040311313365900_ref20","doi-asserted-by":"publisher","first-page":"3377","DOI":"10.1093\/bioinformatics\/btac357","article-title":"Scpregan, a deep generative model for predicting the response of single-cell expression to perturbation","volume":"38","author":"Wei","year":"2022","journal-title":"Bioinformatics"},{"key":"2025040311313365900_ref21","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1038\/s41592-019-0494-8","article-title":"Scgen predicts single-cell perturbation responses","volume":"16","author":"Lotfollahi","year":"2019","journal-title":"Nat Methods"},{"key":"2025040311313365900_ref22","doi-asserted-by":"publisher","first-page":"i610","DOI":"10.1093\/bioinformatics\/btaa800","article-title":"Conditional out-of-distribution generation for unpaired data using transfer vae","volume":"36","author":"Lotfollahi","year":"2020","journal-title":"Bioinformatics"},{"key":"2025040311313365900_ref23","article-title":"Learning structured output representation using deep conditional generative models","volume":"28","author":"Sohn","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"2025040311313365900_ref24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-021-02373-4","article-title":"Michigan: Sampling from disentangled representations of single-cell data using generative adversarial networks","volume":"22","author":"Yu","year":"2021","journal-title":"Genome Biol"},{"key":"2025040311313365900_ref25","doi-asserted-by":"publisher","first-page":"btae265","DOI":"10.1093\/bioinformatics\/btae265","article-title":"Scpram accurately predicts single-cell gene expression perturbation response based on attention mechanism","volume":"40","author":"Jiang","year":"2024","journal-title":"Bioinformatics"},{"key":"2025040311313365900_ref26","doi-asserted-by":"publisher","first-page":"100817","DOI":"10.1016\/j.patter.2023.100817","article-title":"Generative modeling of single-cell gene expression for dose-dependent chemical perturbations","volume":"4","author":"Kana","year":"2023","journal-title":"Patterns"},{"key":"2025040311313365900_ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2024.10.035","article-title":"Scperb: Predict single-cell perturbation via style transfer-based variational autoencoder","author":"Tang","year":"2024","journal-title":"J Adv Res"},{"key":"2025040311313365900_ref28","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"2025040311313365900_ref29","article-title":"Tutorial on variational autoencoders","author":"Doersch","year":"2016"},{"key":"2025040311313365900_ref30","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"2025040311313365900_ref31","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1038\/s41587-021-01001-7","article-title":"Mapping single-cell data to reference atlases by transfer learning","volume":"40","author":"Lotfollahi","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2025040311313365900_ref32","article-title":"Learning single-cell perturbation responses using neural optimal transport","volume":"20","author":"Bunne","year":"2023","journal-title":"Nat Methods"},{"key":"2025040311313365900_ref33","doi-asserted-by":"publisher","first-page":"1","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":"2025040311313365900_ref34","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1038\/s41591-020-0944-y","article-title":"A single-cell atlas of the peripheral immune response in patients with severe covid-19","volume":"26","author":"Wilk","year":"2020","journal-title":"Nat Med"},{"key":"2025040311313365900_ref35","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1038\/s41591-020-0901-9","article-title":"Single-cell landscape of bronchoalveolar immune cells in patients with covid-19","volume":"26","author":"Liao","year":"2020","journal-title":"Nat Med"},{"key":"2025040311313365900_ref36","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1016\/j.cell.2020.08.002","article-title":"Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild Covid-19","volume":"182","author":"Silvin","year":"2020","journal-title":"Cell"},{"key":"2025040311313365900_ref37","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1016\/j.cell.2020.08.001","article-title":"Severe covid-19 is marked by a dysregulated myeloid cell compartment","volume":"182","author":"Schulte-Schrepping","year":"2020","journal-title":"Cell"},{"key":"2025040311313365900_ref38","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41421-020-0168-9","article-title":"Immune cell profiling of covid-19 patients in the recovery stage by single-cell sequencing","volume":"6","author":"Wen","year":"2020","journal-title":"Cell Discovery"},{"key":"2025040311313365900_ref39","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/s41586-018-0657-2","article-title":"Gene expression variability across cells and species shapes innate immunity","volume":"563","author":"Hagai","year":"2018","journal-title":"Nature"},{"key":"2025040311313365900_ref40","article-title":"Umap: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018"},{"key":"2025040311313365900_ref41","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2025040311313365900_ref42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-32867-z","article-title":"Circulating multimeric immune complexes contribute to immunopathology in Covid-19","volume":"13","author":"Ankerhold","year":"2022","journal-title":"Nat Commun"},{"key":"2025040311313365900_ref43","first-page":"1","article-title":"Sars-cov-2 infection results in immune responses in the respiratory tract and peripheral blood that suggest mechanisms of disease severity","volume":"13","author":"Zhang","year":"2022","journal-title":"Nat Commun"},{"key":"2025040311313365900_ref44","doi-asserted-by":"publisher","first-page":"6578","DOI":"10.1093\/nar\/gkad450","article-title":"Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks","volume":"51","author":"Yang","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2025040311313365900_ref45","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/TCBB.2022.3161131","article-title":"Network-based structural learning nonnegative matrix factorization algorithm for clustering of scrna-seq data","volume":"20","author":"Wu","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025040311313365900_ref46","doi-asserted-by":"publisher","first-page":"3535","DOI":"10.1109\/TCBB.2023.3298334","article-title":"Multi-view clustering with graph learning for scrna-seq data","volume":"20","author":"Wu","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2025040311313365900_ref47","doi-asserted-by":"publisher","first-page":"bbac068","DOI":"10.1093\/bib\/bbac068","article-title":"Learning deep features and topological structure of cells for clustering of scrna-sequencing data","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025040311313365900_ref48","doi-asserted-by":"publisher","first-page":"3134","DOI":"10.1109\/JBHI.2024.3370868","article-title":"Learning consistency and specificity of cells from single-cell multi-omic data","volume":"28","author":"Wang","year":"2024","journal-title":"IEEE J Biomed Health Inform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf126\/62852874\/bbaf126.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf126\/62852874\/bbaf126.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T09:40:22Z","timestamp":1743673222000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf126\/8104857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf126","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.03.05.583614","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,3]]},"published":{"date-parts":[[2025,3]]},"article-number":"bbaf126"}}