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sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression

Authors
  • He, Yuan1
  • Chhetri, Surya B.2, 3
  • Arvanitis, Marios1, 4
  • Srinivasan, Kaushik5
  • Aguet, François6
  • Ardlie, Kristin G.6
  • Barbeira, Alvaro N.7
  • Bonazzola, Rodrigo7
  • Im, Hae Kyung7
  • Brown, Christopher D.8
  • Battle, Alexis1, 5
  • 1 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA , Baltimore (United States)
  • 2 HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA , Huntsville (United States)
  • 3 Current Address: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA , Baltimore (United States)
  • 4 Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, 21287, USA , Baltimore (United States)
  • 5 Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA , Baltimore (United States)
  • 6 The Broad Institute of MIT and Harvard, Cambridge, MA, USA , Cambridge (United States)
  • 7 Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA , Chicago (United States)
  • 8 Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA , Philadelphia (United States)
Type
Published Article
Publication Date
Sep 11, 2020
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s13059-020-02129-6
Source
Springer Nature
Keywords
License
Green

Abstract

Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs, can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses.

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