Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling

Mariano I. Gabitto (), Anders Rasmussen, Orly Wapinski, Kathryn Allaway, Nicholas Carriero, Gordon J. Fishell and Richard Bonneau ()
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Mariano I. Gabitto: Simons Foundation
Anders Rasmussen: Simons Foundation
Orly Wapinski: New York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research Center
Kathryn Allaway: New York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research Center
Nicholas Carriero: Simons Foundation
Gordon J. Fishell: New York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research Center
Richard Bonneau: Simons Foundation

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract ATAC-seq has become a leading technology for probing the chromatin landscape of single and aggregated cells. Distilling functional regions from ATAC-seq presents diverse analysis challenges. 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 that uses latent space models to better model accessible regions, termed ChromA. ChromA annotates chromatin landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. ChromA can analyze single cell ATAC-seq data, correcting many biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on multiple technologies and biological systems, including mouse and human immune cells, establishing ChromA as a top performing general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs.

Date: 2020
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DOI: 10.1038/s41467-020-14497-5

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