EpiScanpy: integrated single-cell epigenomic analysis

Danese A, Richter ML, Chaichoompu K, Fischer DS, Theis FJ, Colome-Tatche M (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 12

Article Number: 5228

Journal Issue: 1

DOI: 10.1038/s41467-021-25131-3

Abstract

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Danese, A., Richter, M.L., Chaichoompu, K., Fischer, D.S., Theis, F.J., & Colome-Tatche, M. (2021). EpiScanpy: integrated single-cell epigenomic analysis. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-25131-3

MLA:

Danese, Anna, et al. "EpiScanpy: integrated single-cell epigenomic analysis." Nature Communications 12.1 (2021).

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