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Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data. However, the performance of the reference-based cell type annotation tools on imaging-based spatial transcriptomics data has not been well studied yet.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We compared performance of five reference-based methods (<jats:italic>SingleR<\/jats:italic>, <jats:italic>Azimuth<\/jats:italic>, <jats:italic>RCTD<\/jats:italic>, <jats:italic>scPred<\/jats:italic> and <jats:italic>scmapCell<\/jats:italic>) with the marker-gene-based manual annotation method on an imaging-based Xenium data of human breast cancer. A practical workflow has been demonstrated for preparing a high-quality single-cell RNA reference, evaluating the accuracy, and estimating the running time for reference-based cell type annotation tools.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>\n              <jats:italic>SingleR<\/jats:italic> was the best performing reference-based cell type annotation tool for the Xenium platform, being fast, accurate and easy to use, with results closely matching those of manual annotation.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12859-025-06044-0","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T10:58:51Z","timestamp":1737370731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3806-4694","authenticated-orcid":false,"given":"Jinming","family":"Cheng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2222-0958","authenticated-orcid":false,"given":"Xinyi","family":"Jin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9221-2892","authenticated-orcid":false,"given":"Gordon K.","family":"Smyth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4911-5653","authenticated-orcid":false,"given":"Yunshun","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"issue":"10","key":"6044_CR1","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1038\/s41580-023-00615-w","volume":"24","author":"A Baysoy","year":"2023","unstructured":"Baysoy A, Bai Z, Satija R, Fan R. 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