{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:40:38Z","timestamp":1775580038072,"version":"3.50.1"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"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":["11871026"],"award-info":[{"award-number":["11871026"]}],"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":["12271198"],"award-info":[{"award-number":["12271198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council","award":["11204821"],"award-info":[{"award-number":["11204821"]}]},{"name":"Hong Kong Innovation and Technology Commission"},{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["9610034"],"award-info":[{"award-number":["9610034"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Spatially resolved gene expression profiles are the key to exploring the cell type spatial distributions and understanding the architecture of tissues. Many spatially resolved transcriptomics (SRT) techniques do not provide single-cell resolutions, but they measure gene expression profiles on captured locations (spots) instead, which are mixtures of potentially heterogeneous cell types. Currently, several cell-type deconvolution methods have been proposed to deconvolute SRT data. Due to the different model strategies of these methods, their deconvolution results also vary.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Leveraging the strengths of multiple deconvolution methods, we introduce a new weighted ensemble learning deconvolution method, EnDecon, to predict cell-type compositions on SRT data in this work. EnDecon integrates multiple base deconvolution results using a weighted optimization model to generate a more accurate result. Simulation studies demonstrate that EnDecon outperforms the competing methods and the learned weights assigned to base deconvolution methods have high positive correlations with the performances of these base methods. Applied to real datasets from different spatial techniques, EnDecon identifies multiple cell types on spots, localizes these cell types to specific spatial regions and distinguishes distinct spatial colocalization and enrichment patterns, providing valuable insights into spatial heterogeneity and regionalization of tissues.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code is available at https:\/\/github.com\/Zhangxf-ccnu\/EnDecon.<\/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\/btac825","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T16:44:29Z","timestamp":1671641069000},"source":"Crossref","is-referenced-by-count":21,"title":["EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning"],"prefix":"10.1093","volume":"39","author":[{"given":"Jia-Juan","family":"Tu","sequence":"first","affiliation":[{"name":"Centre for Intelligent Multidimensional Data Analysis, Hong Kong Science Park , Hong Kong 999077, China"}]},{"given":"Hui-Sheng","family":"Li","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Multidimensional Data Analysis, Hong Kong Science Park , Hong Kong 999077, China"},{"name":"Department of Statistics, School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University , Wuhan 430079, China"}]},{"given":"Hong","family":"Yan","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Multidimensional Data Analysis, Hong Kong Science Park , Hong Kong 999077, China"},{"name":"Department of Electrical Engineering, City University of Hong Kong , Hong Kong 999077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-9725","authenticated-orcid":false,"given":"Xiao-Fei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics, School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University , Wuhan 430079, China"}]}],"member":"286","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"2023010801261893000_btac825-B1","doi-asserted-by":"crossref","first-page":"e107","DOI":"10.1093\/nar\/gkaa740","article-title":"SpaGE: spatial gene enhancement using scRNA-seq","volume":"48","author":"Abdelaal","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023010801261893000_btac825-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-020-01247-y","article-title":"Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography","volume":"3","author":"Andersson","year":"2020","journal-title":"Commun. 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