Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data
Abstract Background Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. 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 goo...
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06044-0 |
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| author | Jinming Cheng Xinyi Jin Gordon K. Smyth Yunshun Chen |
| author_facet | Jinming Cheng Xinyi Jin Gordon K. Smyth Yunshun Chen |
| author_sort | Jinming Cheng |
| collection | DOAJ |
| description | Abstract Background Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. 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. Results We compared performance of five reference-based methods (SingleR, Azimuth, RCTD, scPred and scmapCell) 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. Conclusions SingleR 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. |
| format | Article |
| id | doaj-art-b80071a2f7c34f1b960eb329d3e33e53 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-b80071a2f7c34f1b960eb329d3e33e532025-01-26T12:54:52ZengBMCBMC Bioinformatics1471-21052025-01-0126111510.1186/s12859-025-06044-0Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics dataJinming Cheng0Xinyi Jin1Gordon K. Smyth2Yunshun Chen3Bioinformatics Division, The Walter and Eliza Hall Institute of Medical ResearchBioinformatics Division, The Walter and Eliza Hall Institute of Medical ResearchBioinformatics Division, The Walter and Eliza Hall Institute of Medical ResearchBioinformatics Division, The Walter and Eliza Hall Institute of Medical ResearchAbstract Background Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. 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. Results We compared performance of five reference-based methods (SingleR, Azimuth, RCTD, scPred and scmapCell) 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. Conclusions SingleR 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.https://doi.org/10.1186/s12859-025-06044-0Spatial transcriptomicsImaging-basedCell type annotationReference-based annotationXeniumSingle-cell |
| spellingShingle | Jinming Cheng Xinyi Jin Gordon K. Smyth Yunshun Chen Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data BMC Bioinformatics Spatial transcriptomics Imaging-based Cell type annotation Reference-based annotation Xenium Single-cell |
| title | Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data |
| title_full | Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data |
| title_fullStr | Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data |
| title_full_unstemmed | Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data |
| title_short | Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data |
| title_sort | benchmarking cell type annotation methods for 10x xenium spatial transcriptomics data |
| topic | Spatial transcriptomics Imaging-based Cell type annotation Reference-based annotation Xenium Single-cell |
| url | https://doi.org/10.1186/s12859-025-06044-0 |
| work_keys_str_mv | AT jinmingcheng benchmarkingcelltypeannotationmethodsfor10xxeniumspatialtranscriptomicsdata AT xinyijin benchmarkingcelltypeannotationmethodsfor10xxeniumspatialtranscriptomicsdata AT gordonksmyth benchmarkingcelltypeannotationmethodsfor10xxeniumspatialtranscriptomicsdata AT yunshunchen benchmarkingcelltypeannotationmethodsfor10xxeniumspatialtranscriptomicsdata |