{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T11:28:06Z","timestamp":1781090886815,"version":"3.54.1"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["No. SQ2025YFE0204912"],"award-info":[{"award-number":["No. SQ2025YFE0204912"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62172122"],"award-info":[{"award-number":["No. 62172122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Single-cell RNA sequencing (scRNA-seq) provides an important means to reveal the heterogeneity and dynamic processes of tissues, organisms, and complex diseases, but technical capture loss (dropout) often obscures true biological expression, and existing imputation methods have difficulty distinguishing biological zeros (silent expression) from technical noise. To address this, we propose the imputation framework scZN. scZN assumes that the observed scRNA-seq data arise from a combination of RNA\u2019s two-state transcription process and dropout, and formulates imputation as nonnegative factorization: decomposing the raw count matrix into two interpretable nonnegative factors, performing learning and optimization under constraints from prior knowledge and multiple regularizations, thereby reconstructing the cellular expression landscape. Experiments show that scZN can capture the true distributional characteristics at both the gene and cell levels and significantly suppress spurious activation of genes that should not be expressed. Across multiple real datasets, it outperforms dozens of state-of-the-art methods. Especially in complex experimental design scenarios, scZN markedly improves trajectory inference for embryonic stem cells and mouse dentate gyrus data. In Alzheimer\u2019s disease data, scZN can also effectively recover pathways related to neuroinflammation, improving downstream scRNA-seq analysis. Overall, scZN provides a unified framework for missing-value imputation and expression reconstruction that combines accuracy and interpretability.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014051","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:49:52Z","timestamp":1774028992000},"page":"e1014051","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prior-guided factorization for reliable imputation of scRNA-seq data"],"prefix":"10.1371","volume":"22","author":[{"given":"You","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4950-0789","authenticated-orcid":true,"given":"Li","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1557-3720","authenticated-orcid":true,"given":"Ye Win","family":"Aung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex Michel","family":"Daoud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"issue":"1","key":"pcbi.1014051.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/stmcls\/sxad077","article-title":"Single-cell RNA sequencing technology landscape in 2023","volume":"42","author":"H-Q Qu","year":"2024","journal-title":"Stem Cells"},{"issue":"16","key":"pcbi.1014051.ref002","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.3390\/cancers15164164","article-title":"Advancing breast cancer heterogeneity analysis: insights from genomics, transcriptomics and proteomics at bulk and single-cell levels","volume":"15","author":"Z Zhu","year":"2023","journal-title":"Cancers (Basel)"},{"issue":"1","key":"pcbi.1014051.ref003","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1038\/s41467-022-28803-w","article-title":"Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data","volume":"13","author":"A Ianevski","year":"2022","journal-title":"Nat Commun"},{"issue":"8","key":"pcbi.1014051.ref004","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1016\/j.molp.2021.05.028","article-title":"Reconstruction of lateral root formation through single-cell RNA sequencing reveals order of tissue initiation","volume":"14","author":"L Serrano-Ron","year":"2021","journal-title":"Mol Plant"},{"issue":"2","key":"pcbi.1014051.ref005","doi-asserted-by":"crossref","first-page":"102202","DOI":"10.1016\/j.cpcardiol.2023.102202","article-title":"Single-cell RNA sequencing (scRNA-seq): advances and challenges for cardiovascular diseases (CVDs)","volume":"49","author":"SU Khan","year":"2024","journal-title":"Current problems in cardiology"},{"key":"pcbi.1014051.ref006","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/978-981-97-7395-4_2","article-title":"RNA sequencing and data analysis: a revolutionary approach to transcriptome profiling in livestock","volume-title":"Bioinformatics in veterinary science: Vetinformatics","author":"D Kim","year":"2025"},{"issue":"1","key":"pcbi.1014051.ref007","doi-asserted-by":"crossref","first-page":"8512","DOI":"10.1038\/s41467-024-52544-7","article-title":"Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data","volume":"15","author":"AC Weiner","year":"2024","journal-title":"Nat Commun"},{"issue":"3","key":"pcbi.1014051.ref008","doi-asserted-by":"crossref","DOI":"10.1002\/ctm2.694","article-title":"Single-cell RNA sequencing technologies and applications: a brief overview","volume":"12","author":"D Jovic","year":"2022","journal-title":"Clin Transl Med"},{"issue":"19","key":"pcbi.1014051.ref009","doi-asserted-by":"crossref","first-page":"10978","DOI":"10.1093\/nar\/gkx754","article-title":"Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data","volume":"45","author":"C Jia","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"pcbi.1014051.ref010","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1016\/j.gpb.2022.11.011","article-title":"Application of deep learning on single-cell RNA sequencing data analysis: a review","volume":"20","author":"M Brendel","year":"2022","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"pcbi.1014051.ref011","first-page":"111591","article-title":"MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data","author":"v Dijk D","year":"2017","journal-title":"BioRxiv"},{"issue":"1","key":"pcbi.1014051.ref012","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1186\/s12859-018-2226-y","article-title":"DrImpute: imputing dropout events in single cell RNA sequencing data","volume":"19","author":"W Gong","year":"2018","journal-title":"BMC Bioinformatics"},{"issue":"7","key":"pcbi.1014051.ref013","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1038\/s41592-018-0033-z","article-title":"SAVER: gene expression recovery for single-cell RNA sequencing","volume":"15","author":"M Huang","year":"2018","journal-title":"Nat Methods"},{"key":"pcbi.1014051.ref014","doi-asserted-by":"crossref","unstructured":"Kang B, Abeysinghe E, Agarwal D, Wang Q, Pamidighantam S, Huang M, et al. Online single-cell RNA-seq data denoising with transfer learning. In: Practice and Experience in Advanced Research Computing. 2020. p. 469\u201372. doi: 10.1145\/3311790.3399617","DOI":"10.1145\/3311790.3399617"},{"key":"pcbi.1014051.ref015","first-page":"655365","article-title":"Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis","author":"F Wagner","year":"2019","journal-title":"BioRxiv"},{"issue":"1","key":"pcbi.1014051.ref016","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13059-019-1681-8","article-title":"SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data","volume":"20","author":"T Peng","year":"2019","journal-title":"Genome Biol"},{"issue":"1","key":"pcbi.1014051.ref017","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1038\/s41467-018-03405-7","article-title":"An accurate and robust imputation method scImpute for single-cell RNA-seq data","volume":"9","author":"WV Li","year":"2018","journal-title":"Nat Commun"},{"issue":"1","key":"pcbi.1014051.ref018","doi-asserted-by":"crossref","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":"Y Sha","year":"2024","journal-title":"Nat Mach Intell"},{"issue":"1","key":"pcbi.1014051.ref019","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1186\/s13059-019-1837-6","article-title":"DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data","volume":"20","author":"C Arisdakessian","year":"2019","journal-title":"Genome Biol"},{"issue":"1","key":"pcbi.1014051.ref020","doi-asserted-by":"crossref","first-page":"16329","DOI":"10.1038\/s41598-018-34688-x","article-title":"AutoImpute: autoencoder based imputation of single-cell RNA-seq data","volume":"8","author":"D Talwar","year":"2018","journal-title":"Sci Rep"},{"issue":"1","key":"pcbi.1014051.ref021","doi-asserted-by":"crossref","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":"G Eraslan","year":"2019","journal-title":"Nat Commun"},{"issue":"2","key":"pcbi.1014051.ref022","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/s41587-021-01206-w","article-title":"A Python library for probabilistic analysis of single-cell omics data","volume":"40","author":"A Gayoso","year":"2022","journal-title":"Nat Biotechnol"},{"issue":"5","key":"pcbi.1014051.ref023","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1038\/s41587-023-01733-8","article-title":"The scverse project provides a computational ecosystem for single-cell omics data analysis","volume":"41","author":"I Virshup","year":"2023","journal-title":"Nat Biotechnol"},{"issue":"1","key":"pcbi.1014051.ref024","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1186\/s13059-020-02083-3","article-title":"DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning","volume":"21","author":"Y He","year":"2020","journal-title":"Genome Biol"},{"issue":"15","key":"pcbi.1014051.ref025","article-title":"scIGANs: single-cell RNA-seq imputation using generative adversarial networks","volume":"48","author":"Y Xu","year":"2020","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"pcbi.1014051.ref026","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/s12859-025-06138-9","article-title":"Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation","volume":"26","author":"Y Wu","year":"2025","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"pcbi.1014051.ref027","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1038\/s41467-021-22197-x","article-title":"scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses","volume":"12","author":"J Wang","year":"2021","journal-title":"Nat Commun"},{"key":"pcbi.1014051.ref028","author":"S Yun","year":"2023"},{"issue":"3","key":"pcbi.1014051.ref029","article-title":"Single-cell RNA sequencing data imputation using bi-level feature propagation","volume":"25","author":"J Lee","year":"2024","journal-title":"Brief Bioinform"},{"key":"pcbi.1014051.ref030","doi-asserted-by":"crossref","unstructured":"Ahn SJ, Um D, Yeo Y, Yoon JW. Gene-gene relationship modeling based on genetic evidence for single-cell RNA-Seq data imputation. In: Advances in Neural Information Processing Systems 37, 2024. p. 18882\u2013909. doi: 10.52202\/079017-0598","DOI":"10.52202\/079017-0598"},{"key":"pcbi.1014051.ref031","doi-asserted-by":"crossref","first-page":"110933","DOI":"10.1016\/j.patcog.2024.110933","article-title":"Semi-supervised pivotal-aware nonnegative matrix factorization with label and pairwise constraint propagation for data clustering","volume":"157","author":"X Yang","year":"2025","journal-title":"Pattern Recognition"},{"issue":"1","key":"pcbi.1014051.ref032","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell RNA sequencing data","volume":"18","author":"L Zappia","year":"2017","journal-title":"Genome Biol"},{"issue":"2","key":"pcbi.1014051.ref033","article-title":"AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification","volume":"40","author":"X Zhu","year":"2024","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1014051.ref034","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1186\/s12879-024-09940-7","article-title":"Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model","volume":"24","author":"W Zhou","year":"2024","journal-title":"BMC Infect Dis"},{"issue":"04","key":"pcbi.1014051.ref035","doi-asserted-by":"crossref","first-page":"124","DOI":"10.12945\/j.aorta.2016.16.014","article-title":"The mystery of the Z-score","volume":"04","author":"A Curtis","year":"2016","journal-title":"Aorta"},{"issue":"1","key":"pcbi.1014051.ref036","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":"FA Wolf","year":"2018","journal-title":"Genome Biol"},{"issue":"7745","key":"pcbi.1014051.ref037","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":"J Cao","year":"2019","journal-title":"Nature"},{"key":"pcbi.1014051.ref038","first-page":"1","article-title":"Neuroinflammation in Alzheimer disease","author":"MT Heneka","year":"2024","journal-title":"Nature Reviews Immunology"},{"key":"pcbi.1014051.ref039","first-page":"1","article-title":"Cell2fate infers RNA velocity modules to improve cell fate prediction","author":"A Aivazidis","year":"2025","journal-title":"Nature Methods"},{"issue":"5","key":"pcbi.1014051.ref040","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"R Satija","year":"2015","journal-title":"Nat Biotechnol"},{"key":"pcbi.1014051.ref041","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/978-3-030-57077-4_10","volume-title":"\u201cPyTorch\u201d, Programming with TensorFlow: solution for edge computing applications","author":"S Imambi","year":"2021"},{"issue":"7","key":"pcbi.1014051.ref042","doi-asserted-by":"crossref","first-page":"9700","DOI":"10.1109\/TNNLS.2023.3236415","article-title":"Robust tensor completion via capped frobenius norm","volume":"35","author":"XP Li","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"pcbi.1014051.ref043","doi-asserted-by":"crossref","first-page":"6486","DOI":"10.1109\/TPAMI.2024.3382294","article-title":"Towards understanding convergence and generalization of AdamW","volume":"46","author":"P Zhou","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"pcbi.1014051.ref044","doi-asserted-by":"crossref","first-page":"6586","DOI":"10.1038\/s41467-022-34188-7","article-title":"UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference","volume":"13","author":"M Gao","year":"2022","journal-title":"Nat Commun"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:50:00Z","timestamp":1774029000000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014051"}},"subtitle":[],"editor":[{"given":"Lihua","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3,20]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1014051","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]}}}