Characterizing the epigenetic landscape of cellular populations from bulk and single-cell ATAC-seq information.

Abstract

Given its ability to map chromatin accessibility with single base pair resolution, ATAC-seq has become a leading technology to probe the epigenomic landscape of single and aggregated cells. Understanding ATAC-seq data presents distinct analysis challenges, compared to RNA-seq technologies, because of the relative sparseness of the data produced and the interaction of complex noise with multiple chromatin structure scales. Methods commonly used to analyze chromatin accessibility datasets are adapted from algorithms designed to process different experimental technologies, disregarding the statistical and biological differences intrinsic to the ATAC-seq technology. Here, we present a Bayesian statistical approach, termed ChromA, to analyze ATAC-seq data. ChromA annotates the cellular epigenetic landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. Our method can analyze single cell ATAC-seq data, improving cell type identification and correcting many of the biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on several biological systems, including mouse and human immune cells and find it effective at recovering accessible chromatin, establishing ChromA as a top preforming general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs.