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Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets

Rossin Erbe, Michael D. Kessler, Alexander V. Favorov, Hariharan Easwaran, Daria A. Gaykalova, Elana J. Fertig
doi: https://doi.org/10.1101/2020.01.30.927129
Rossin Erbe
The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Michael D. Kessler
The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Alexander V. Favorov
The Johns Hopkins University School of Medicine, Baltimore, MD, USAVavilov Institute of General Genetics, Moscow, RussiaResearch Institute of Genetics and Selection of Industrial Microorganisms, Moscow, Russia
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Hariharan Easwaran
The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Daria A. Gaykalova
The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Elana J. Fertig
The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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  • For correspondence: ejfertig@jhmi.edu
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Abstract

While single-cell ATAC-seq analysis methods allow for robust clustering of cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. Using publicly available scATAC-seq data, we find patterns that accurately characterize cell types both within and across data sets. Furthermore, we demonstrate that these patterns are both consistent with current biological understanding and reflective of novel regulatory biology.

Footnotes

  • rerbe1{at}jhmi.edu, mkessl11{at}jhmi.edu, avf{at}jhmi.edu, Heaswar2{at}jhmi.edu, dgaykal{at}jhmi.edu, ejfertig{at}jhmi.edu

  • https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99172

  • https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96769

  • https://github.com/loosolab/cardiac-progenitors

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 31, 2020.
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Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets
Rossin Erbe, Michael D. Kessler, Alexander V. Favorov, Hariharan Easwaran, Daria A. Gaykalova, Elana J. Fertig
bioRxiv 2020.01.30.927129; doi: https://doi.org/10.1101/2020.01.30.927129
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Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets
Rossin Erbe, Michael D. Kessler, Alexander V. Favorov, Hariharan Easwaran, Daria A. Gaykalova, Elana J. Fertig
bioRxiv 2020.01.30.927129; doi: https://doi.org/10.1101/2020.01.30.927129

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