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Cellcano: supervised cell type identification for single cell ATAC-seq data

 1 year ago
source link: https://www.researchsquare.com/article/rs-1717357/v1
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https://doi.org/10.21203/rs.3.rs-1717357/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted 11 Aug, 2022

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Computational cell type identification (celltyping) is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. In this work, we develop Cellcano, a novel computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. We systematically benchmark Cellcano on 50 well-designed experiments from various datasets and show that Cellcano is accurate, robust, and computational efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/.


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