{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T17:42:38Z","timestamp":1772732558279,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["BAS\/1\/1624-01"],"award-info":[{"award-number":["BAS\/1\/1624-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["FCC\/1\/1976-23-01"],"award-info":[{"award-number":["FCC\/1\/1976-23-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["URF\/1\/4077-01-01"],"award-info":[{"award-number":["URF\/1\/4077-01-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["URF\/1\/4098-01-01"],"award-info":[{"award-number":["URF\/1\/4098-01-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["REI\/1\/4216-01-01"],"award-info":[{"award-number":["REI\/1\/4216-01-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["REI\/1\/4437-01-01"],"award-info":[{"award-number":["REI\/1\/4437-01-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["REI\/1\/4473-01-01"],"award-info":[{"award-number":["REI\/1\/4473-01-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["URF\/1\/4352-01-01"],"award-info":[{"award-number":["URF\/1\/4352-01-01"]}]},{"name":"Office of Research Administration (ORA) at King Abdullah University of Science and Technology","award":["REI\/1\/4742-01-01"],"award-info":[{"award-number":["REI\/1\/4742-01-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Unveiling the heterogeneity in the tissues is crucial to explore cell\u2013cell interactions and cellular targets of human diseases. Spatial transcriptomics (ST) supplies spatial gene expression profile which has revolutionized our biological understanding, but variations in cell-type proportions of each spot with dozens of cells would confound downstream analysis. Therefore, deconvolution of ST has been an indispensable step and a technical challenge toward the higher-resolution panorama of tissues.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we propose a novel ST deconvolution method called SD2 integrating spatial information of ST data and embracing an important characteristic, dropout, which is traditionally considered as an obstruction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract the dropout-based genes as informative features from ST and scRNA-seq data by fitting a Michaelis\u2013Menten function. After synthesizing pseudo-ST spots by randomly composing cells from scRNA-seq data, auto-encoder is applied to discover low-dimensional and non-linear representation of the real- and pseudo-ST spots. Next, we create a graph containing embedded profiles as nodes, and edges determined by transcriptional similarity and spatial relationship. Given the graph, a graph convolutional neural network is used to predict the cell-type compositions for real-ST spots. We benchmark the performance of SD2 on the simulated seqFISH+ dataset with different resolutions and measurements which show superior performance compared with the state-of-the-art methods. SD2 is further validated on three real-world datasets with different ST technologies and demonstrates the capability to localize cell-type composition accurately with quantitative evidence. Finally, ablation study is conducted to verify the contribution of different modules proposed in SD2.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The SD2 is freely available in github (https:\/\/github.com\/leihouyeung\/SD2) and Zenodo (https:\/\/doi.org\/10.5281\/zenodo.7024684).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac605","type":"journal-article","created":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T17:58:46Z","timestamp":1662400726000},"page":"4878-4884","source":"Crossref","is-referenced-by-count":28,"title":["SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3164-8240","authenticated-orcid":false,"given":"Haoyang","family":"Li","sequence":"first","affiliation":[{"name":"Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"},{"name":"Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"}]},{"given":"Hanmin","family":"Li","sequence":"additional","affiliation":[{"name":"Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"},{"name":"Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6739-6236","authenticated-orcid":false,"given":"Juexiao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"},{"name":"Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-3574","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"},{"name":"Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST) , Thuwal 23955, Saudi Arabia"}]}],"member":"286","published-online":{"date-parts":[[2022,9,5]]},"reference":[{"key":"2022122417404851800_btac605-B2","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1093\/bioinformatics\/bty1044","article-title":"M3Drop: dropout-Based feature selection for ScRNASeq","volume":"35","author":"Andrews","year":"2019","journal-title":"Bioinformatics"},{"key":"2022122417404851800_btac605-B3","doi-asserted-by":"crossref","first-page":"5650","DOI":"10.1038\/s41467-020-19015-1","article-title":"Benchmarking of cell type deconvolution pipelines for transcriptomics data","volume":"11","author":"Avila Cobos","year":"2020","journal-title":"Nat. Commun"},{"key":"2022122417404851800_btac605-B104","article-title":"A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart","volume":"179","year":"2019","journal-title":"Cell"},{"key":"2022122417404851800_btac605-B4","doi-asserted-by":"crossref","DOI":"10.1017\/ATSIP.2020.13","article-title":"Graph representation learning: a survey","volume":"9","author":"Chen","year":"2020","journal-title":"SIP"},{"key":"2022122417404851800_btac605-B5","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1093\/bib\/bbz166","article-title":"SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references","volume":"22","author":"Dong","year":"2021","journal-title":"Brief. Bioinform"},{"key":"2022122417404851800_btac605-B6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s13059-021-02362-7","article-title":"SpatialDWLS: accurate deconvolution of spatial transcriptomic data","volume":"22","author":"Dong","year":"2021","journal-title":"Genome Biol"},{"key":"2022122417404851800_btac605-B7","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1038\/s41467-020-14666-6","article-title":"Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder","volume":"11","author":"Dwivedi","year":"2020","journal-title":"Nat. Commun"},{"key":"2022122417404851800_btac605-B8","doi-asserted-by":"crossref","first-page":"e50","DOI":"10.1093\/nar\/gkab043","article-title":"SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes","volume":"49","author":"Elosua-Bayes","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2022122417404851800_btac605-B9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1038\/s41586-019-1049-y","article-title":"Transcriptome-scale super-resolved imaging in tissues by RNA SeqFISH+","volume":"568","author":"Eng","year":"2019","journal-title":"Nature"},{"key":"2022122417404851800_btac605-B10","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":"Eraslan","year":"2019","journal-title":"Nat. Commun"},{"key":"2022122417404851800_btac605-B11","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.neuron.2018.07.002","article-title":"Parcellating cerebral cortex: how invasive animal studies inform noninvasive mapmaking in humans","volume":"99","author":"Van Essen","year":"2018","journal-title":"Neuron"},{"key":"2022122417404851800_btac605-B12","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/s41586-019-1506-7","article-title":"Conserved cell types with divergent features in human versus mouse cortex","volume":"573","author":"Hodge","year":"2019","journal-title":"Nature"},{"key":"2022122417404851800_btac605-B13","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1023\/A:1018529323734","article-title":"Developmental expression of the myelin gene MOBP in the rat nervous system","volume":"26","author":"Holz","year":"1997","journal-title":"J. Neurocytol"},{"key":"2022122417404851800_btac605-B14","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s13059-021-02290-6","article-title":"A benchmark for RNA-Seq deconvolution analysis under dynamic testing environments","volume":"22","author":"Jin","year":"2021","journal-title":"Genome Biol"},{"key":"2022122417404851800_btac605-B15","author":"Kingma","year":"2015"},{"key":"2022122417404851800_btac605-B16","author":"Kipf","year":"2017"},{"key":"2022122417404851800_btac605-B17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/nrdp.2016.22","article-title":"Pancreatic cancer","volume":"2","author":"Kleeff","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"2022122417404851800_btac605-B18","author":"Kleshchevnikov","year":"2020"},{"key":"2022122417404851800_btac605-B19","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1038\/s41467-021-22266-1","article-title":"Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets","volume":"12","author":"Miao","year":"2021","journal-title":"Nat. Commun"},{"key":"2022122417404851800_btac605-B20","doi-asserted-by":"crossref","first-page":"eaau5324","DOI":"10.1126\/science.aau5324","article-title":"Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region","volume":"362","author":"Moffitt","year":"2018","journal-title":"Science"},{"key":"2022122417404851800_btac605-B21","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/s41587-019-0392-8","article-title":"Integrating microarray-based spatial transcriptomics and single-cell RNA-Seq reveals tissue architecture in pancreatic ductal adenocarcinomas","volume":"38","author":"Moncada","year":"2020","journal-title":"Nat. Biotechnol"},{"key":"2022122417404851800_btac605-B22","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1186\/s13014-019-1345-6","article-title":"Pancreatic ductal adenocarcinoma: biological hallmarks, current status, and future perspectives of combined modality treatment approaches","volume":"14","author":"Orth","year":"2019","journal-title":"Radiat. Oncol"},{"key":"2022122417404851800_btac605-B122","doi-asserted-by":"crossref","first-page":"eabb3446","DOI":"10.1126\/sciadv.abb3446","article-title":"Molecular atlas of the adult mouse brain","volume":"6","year":"2020","journal-title":"Science advances"},{"key":"2022122417404851800_btac605-B23","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1038\/s41467-020-14976-9","article-title":"Embracing the dropouts in single-cell RNA-Seq analysis","volume":"11","author":"Qiu","year":"2020","journal-title":"Nat. Commun"},{"key":"2022122417404851800_btac605-B24","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1038\/nrn2719","article-title":"Evolution of the neocortex: a perspective from developmental biology","volume":"10","author":"Rakic","year":"2009","journal-title":"Nat. Rev. Neurosci"},{"key":"2022122417404851800_btac605-B25","doi-asserted-by":"crossref","first-page":"143","DOI":"10.3758\/s13423-016-1015-8","article-title":"A simple introduction to Markov chain Monte\u2013Carlo sampling","volume":"25","author":"van Ravenzwaaij","year":"2018","journal-title":"Psychon. Bull. Rev"},{"key":"2022122417404851800_btac605-B26","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1172\/JCI72271","article-title":"Molecular mechanisms of diabetic kidney disease","volume":"124","author":"Reidy","year":"2014","journal-title":"J. Clin. Investig"},{"key":"2022122417404851800_btac605-B27","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21442","article-title":"Cancer statistics, 2018","volume":"68","author":"Siegel","year":"2018","journal-title":"Cancer J. Clin"},{"key":"2022122417404851800_btac605-B28","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.aaf2403","article-title":"Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","volume":"353","author":"St\u00e5hl Patrik","year":"2016","journal-title":"Science"},{"key":"2022122417404851800_btac605-B29","year":"2020"},{"key":"2022122417404851800_btac605-B30","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1016\/j.cell.2021.04.021","article-title":"A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation","volume":"184","author":"Yao","year":"2021","journal-title":"Cell"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac605\/45813027\/btac605.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/21\/4878\/48411558\/btac605.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/21\/4878\/48411558\/btac605.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,24]],"date-time":"2022-12-24T17:41:18Z","timestamp":1671903678000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/21\/4878\/6692424"}},"subtitle":[],"editor":[{"given":"Anthony","family":"Mathelier","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,9,5]]},"references-count":31,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,9,5]]},"published-print":{"date-parts":[[2022,10,31]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac605","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,11,1]]},"published":{"date-parts":[[2022,9,5]]}}}