A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Computational Bioscience Research Center (CBRC)
KAUST Grant NumberFCC/1/1976-44-01
Permanent link to this recordhttp://hdl.handle.net/10754/690546
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AbstractSpatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.
CitationLi, H., Zhou, J., Li, Z., Chen, S., Liao, X., Zhang, B., Zhang, R., Wang, Y., Sun, S., & Gao, X. (2023). A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-37168-7
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award Nos. FCC/1/1976-44-01, FCC/1/1976-45-01, URF/1/4663-01-01, REI/1/5202-01-01, REI/1/5234-01-01, REI/1/4940-01-01, and RGC/3/4816-01-01. We thank Hanmin Li for helping running some experiments.
PublisherSpringer Science and Business Media LLC
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Title: leihouyeung/STdeconv_benchmark: The benchmarking of spatial transcriptomics deconvolution methods. Publication Date: 2022-08-01. github: leihouyeung/STdeconv_benchmark Handle: 10754/691606
Except where otherwise noted, this item's license is described as Archived with thanks to Nature Communications under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0
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