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

Author

Listed:
  • 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
    Harvard Medical School
    Stanley Center at the Broad)

  • Kathryn Allaway

    (New York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research Center
    Harvard Medical School
    Stanley Center at the Broad)

  • Nicholas Carriero

    (Simons Foundation
    Simons Foundation)

  • Gordon J. Fishell

    (New York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research Center
    Harvard Medical School
    Stanley Center at the Broad)

  • Richard Bonneau

    (Simons Foundation
    New York University, Center for Data Science
    New York University, Department of Biology)

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.

Suggested Citation

  • Mariano I. Gabitto & Anders Rasmussen & Orly Wapinski & Kathryn Allaway & Nicholas Carriero & Gordon J. Fishell & Richard Bonneau, 2020. "Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14497-5
    DOI: 10.1038/s41467-020-14497-5
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    Cited by:

    1. Samir Rachid Zaim & Mark-Phillip Pebworth & Imran McGrath & Lauren Okada & Morgan Weiss & Julian Reading & Julie L. Czartoski & Troy R. Torgerson & M. Juliana McElrath & Thomas F. Bumol & Peter J. Ske, 2024. "MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts," Nature Communications, Nature, vol. 15(1), pages 1-24, December.

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