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EpiScanpy: integrated single-cell epigenomic analysis
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DOI：10.1038/s41467-021-25131-3.png)
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摘要
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EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types. The authors present epiScanpy: a computational framework for the analysis of single-cell epigenomic data, both ATAC-seq and DNA methylation data, with examples for clustering, cell type identification, trajectory learning and atlas integration - and show its performance in distinguishing cell types.
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15.7
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被引数:
91.2W
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分享收藏Unsupervised clustering and epigenetic classification of single cells.png)
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