SANGO efficiently removed batch effects between the query and reference single-cell ATAC signals through the underlying genome sequences, to enable cell type assignment according to the reference data. The method achieved superior performance on diverse datasets and could detect unknown tumor cells, providing valuable functional biological signals.
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This is a summary of: Zeng, Y. et al. Deciphering cell types by integrating scATAC-seq data with genome sequences. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00622-7 (2024).
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Annotating cell types in single-cell ATAC data via the guidance of the underlying DNA sequences. Nat Comput Sci 4, 261–262 (2024). https://doi.org/10.1038/s43588-024-00626-3
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DOI: https://doi.org/10.1038/s43588-024-00626-3