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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Gabitto MI,Rasmussen A,Wapinski O,Allaway K,Carriero N,Fishell GJ,Bonneau Rdoi
10.1038/s41467-020-14497-5subject
Has Abstractpub_date
2020-02-06 00:00:00pages
747issue
1issn
2041-1723pii
10.1038/s41467-020-14497-5journal_volume
11pub_type
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