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.