{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:46:02Z","timestamp":1740185162099,"version":"3.37.3"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Bright Focus Foundation","award":["NIH EY033005"],"award-info":[{"award-number":["NIH EY033005"]}]},{"DOI":"10.13039\/100001818","name":"Research to Prevent Blindness","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001818","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>To develop and assess the accuracy of deep learning models that identify different retinal cell types, as well as different retinal ganglion cell (RGC) subtypes, based on patterns of single-cell RNA sequencing (scRNA-seq) in multiple datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Deep domain adaptation models were developed and tested using three different datasets. The first dataset included 44\u200a808 single retinal cells from mice (39 cell types) with 24\u200a658 genes, the second dataset included 6225 single RGCs from mice (41 subtypes) with 13\u200a616 genes and the third dataset included 35\u200a699 single RGCs from mice (45 subtypes) with 18\u200a222 genes. We used four loss functions in the learning process to align the source and target distributions, reduce misclassification errors and maximize robustness. Models were evaluated based on classification accuracy and confusion matrix. The accuracy of the model for correctly classifying 39 different retinal cell types in the first dataset was \u223c92%. Accuracy in the second and third datasets reached \u223c97% and 97% in correctly classifying 40 and 45 different RGCs subtypes, respectively. Across a range of seven different batches in the first dataset, the accuracy of the lead model ranged from 74% to nearly 100%. The lead model provided high accuracy in identifying retinal cell types and RGC subtypes based on scRNA-seq data. The performance was reasonable based on data from different batches as well. The validated model could be readily applied to scRNA-seq data to identify different retinal cell types and subtypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The code and datasets are available on https:\/\/github.com\/DM2LL\/Detecting-Retinal-Cell-Classes-and-Ganglion-Cell-Subtypes. We have also added the class labels of all samples to the datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac514","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T13:00:36Z","timestamp":1658754036000},"page":"4321-4329","source":"Crossref","is-referenced-by-count":1,"title":["Detecting retinal neural and stromal cell classes and ganglion cell subtypes based on transcriptome data with deep transfer learning"],"prefix":"10.1093","volume":"38","author":[{"given":"Yeganeh","family":"Madadi","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, University of Tennessee Health Science Center , Memphis, TN, USA"},{"name":"University of Tehran , Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Tennessee Health Science Center , Memphis, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science Center , Memphis, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Williams","sequence":"additional","affiliation":[{"name":"Department of Genetics and Informatics, University of Tennessee Health Science Center , Memphis, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8633-5730","authenticated-orcid":false,"given":"Siamak","family":"Yousefi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Tennessee Health Science Center , Memphis, TN, USA"},{"name":"Department of Genetics and Informatics, University of Tennessee Health Science Center , Memphis, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1186\/s13059-019-1862-5","article-title":"scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data","volume":"20","author":"Alquicira-Hernandez","year":"2019","journal-title":"Genome Biol"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/s41590-018-0276-y","article-title":"Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage","volume":"20","author":"Aran","year":"2019","journal-title":"Nat. Immunol"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","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":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-17281-7","article-title":"Searching large-scale scRNA-seq databases via unbiased cell embedding with cell BLAST","volume":"11","author":"Cao","year":"2020","journal-title":"Nat. Commun"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7578.001.0001","volume-title":"Eye, Retina, and Visual System of the Mouse","author":"Chalupa","year":"2008"},{"year":"2020","author":"Chen","key":"2023041408240499400_"},{"year":"2020","author":"Cui","key":"2023041408240499400_"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.13","article-title":"Single cell RNA sequencing of stem cell-derived retinal ganglion cells","volume":"5","author":"Daniszewski","year":"2018","journal-title":"Sci. Data"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"e95","DOI":"10.1093\/nar\/gkz543","article-title":"CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing","volume":"47","author":"De Kanter","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-22851-4","article-title":"Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces","volume":"12","author":"Ding","year":"2021","journal-title":"Nat. Commun"},{"year":"2015","author":"Ganin","key":"2023041408240499400_"},{"key":"2023041408240499400_","first-page":"659","volume-title":"Eye, Retina, and Visual System of the Mouse","author":"Geisert","year":"2008"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1038\/nbt.4091","article-title":"Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors","volume":"36","author":"Haghverdi","year":"2018","journal-title":"Nat. Biotechnol"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"4696","DOI":"10.1093\/bioinformatics\/btz295","article-title":"LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection","volume":"35","author":"Johnson","year":"2019","journal-title":"Bioinformatics"},{"year":"2019","author":"Kang","key":"2023041408240499400_"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13062-016-0169-7","article-title":"Sensitivity, specificity, and reproducibility of RNA-Seq differential expression calls","volume":"11","author":"\u0141abaj","year":"2016","journal-title":"Biol. Direct"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1097\/01.ijg.0000169396.25051.e9","article-title":"Retinal ganglion cells and supporting elements in culture","volume":"14","author":"Levin","year":"2005","journal-title":"J Glaucoma"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"3918","DOI":"10.1109\/TPAMI.2020.2991050","article-title":"Maximum density divergence for domain adaptation","volume":"43","author":"Li","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"2023041408240499400_","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":"Li","year":"2018","journal-title":"Nat. Commun"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"e0205499","DOI":"10.1371\/journal.pone.0205499","article-title":"CaSTLe\u2014classification of single cells by transfer learning: harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments","volume":"13","author":"Lieberman","year":"2018","journal-title":"PLoS One"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"e2006687","DOI":"10.1371\/journal.pbio.2006687","article-title":"Quantitative assessment of cell population diversity in single-cell landscapes","volume":"16","author":"Liu","year":"2018","journal-title":"PLoS Biol"},{"key":"2023041408240499400_","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":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"e100811","DOI":"10.15252\/embj.2018100811","article-title":"A single-cell transcriptome atlas of the adult human retina","volume":"38","author":"Lukowski","year":"2019","journal-title":"EMBO J"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1049\/iet-ipr.2020.0087","article-title":"Deep visual unsupervised domain adaptation for classification tasks: a survey","volume":"14","author":"Madadi","year":"2020","journal-title":"IET Image Process"},{"key":"2023041408240499400_","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1038\/s41467-018-05134-3","article-title":"Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes","volume":"9","author":"Rheaume","year":"2018","journal-title":"Nat. Commun"},{"key":"2023041408240499400_","first-page":"353","article-title":"Retinal ganglion cells death in glaucoma\u2013mechanism and potential treatment. Part II","volume":"109","author":"Rokicki","year":"2007","journal-title":"Klin. Oczna"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.neuron.2014.08.054","article-title":"Neuronal cell types and connectivity: lessons from the retina","volume":"83","author":"Seung","year":"2014","journal-title":"Neuron"},{"key":"2023041408240499400_","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":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1126\/science.274.5290.1115","article-title":"Diversity and pattern in the developing spinal cord","volume":"274","author":"Tanabe","year":"1996","journal-title":"Science"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0896-6273(03)00389-1","article-title":"Visual stimulation is required for refinement of on and off pathways in postnatal retina","volume":"39","author":"Tian","year":"2003","journal-title":"Neuron"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.neuron.2019.11.006","article-title":"Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes","volume":"104","author":"Tran","year":"2019","journal-title":"Neuron"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"10339","DOI":"10.1073\/pnas.2001250117","article-title":"Cell atlas of aqueous humor outflow pathways in eyes of humans and four model species provides insight into glaucoma pathogenesis","volume":"117","author":"van Zyl","year":"2020","journal-title":"Proc. Natl. Acad Sci. USA"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1186\/s13059-019-1764-6","article-title":"Bermuda: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes","volume":"20","author":"Wang","year":"2019","journal-title":"Genome Biol"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1523\/JNEUROSCI.13-01-00208.1993","article-title":"Rapid evolution of the visual system: a cellular assay of the retina and dorsal lateral geniculate nucleus of the Spanish wildcat and the domestic cat","volume":"13","author":"Williams","year":"1993","journal-title":"J. Neurosci"},{"key":"2023041408240499400_","first-page":"65","volume-title":"The Visual Neurosciences","author":"Williams","year":"2003"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1167\/iovs.04-0183","article-title":"Seeing the unseen: microarray-based gene expression profiling in vision","volume":"45","author":"Zareparsi","year":"2004","journal-title":"Invest. Ophthalmol. Vis. Sci"},{"key":"2023041408240499400_","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neunet.2019.07.010","article-title":"Multi-representation adaptation network for cross-domain image classification","volume":"119","author":"Zhu","year":"2019","journal-title":"Neural Netw"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac514\/45083043\/btac514.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/18\/4321\/49885127\/btac514.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/18\/4321\/49885127\/btac514.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T23:06:56Z","timestamp":1700867216000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/18\/4321\/6649619"}},"subtitle":[],"editor":[{"given":"Olga","family":"Vitek","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":39,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac514","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2022,9,15]]},"published":{"date-parts":[[2022,7,25]]}}}