EpiScanpy: integrated single-cell epigenomic analysis

Anna Danese,Maria L Richter,David S Fischer,Fabian J Theis,Kridsadakorn Chaichoompu,Maria Colomé-Tatché

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.

Highlights

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data
EpiScanpy is a fast and versatile tool for the analysis of singlecell epigenomic data, and it offers the common framework for the analysis of both single-cell DNA methylation and scATAC-seq data, as well as single-cell transcriptomic data thanks to its embedding in the scanpy platform
EpiScanpy performs common analysis like low-dimensional data visualisation, clustering, single-cell graph abstraction, trajectory inference, and differential calling, based solely on epigenomic features. It features a series of useful downstream functions, such as the mapping of epigenomic features of interest to their closest gene, or the construction of gene activity matrices based on promoter openness

Summary

Introduction

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy enables pre-processing of epigenomic data and building of count matrices considering any genomic feature of interest, from open chromatin peaks to whole genome (i.e., windows), as well as any genomic annotation provided as a coordinate or .bed file (genes, enhancers, TFBS, promoters, etc.). Using these constructed count matrices, epiScanpy performs quality control and different downstream analyses such as clustering, marker identification, manifold learning, visualisation and lineage estimation. Since its downstream analyses extend the popular scanpy framework, it inherits properties such as fast and scalable runtime behaviour and modular extensibility

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