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

Wenjing Ma, Jiaying Lu and Hao Wu ()
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Wenjing Ma: Emory University
Jiaying Lu: Emory University
Hao Wu: Shenzhen University Town

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Computational cell type identification 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. Here we develop Cellcano, a 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. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/ .

Date: 2023
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DOI: 10.1038/s41467-023-37439-3

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