MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

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  • Welch, Joshua D
    • Affiliation: College of Arts and Sciences, Department of Computer Science, School of Medicine, Curriculum in Bioinformatics and Computational Biology
  • Hartemink, Alexander J
    • Other Affiliation: Department of Computer Science, Duke University, Durham, USA
  • Prins, Jan
    • Affiliation: College of Arts and Sciences, Department of Computer Science, School of Medicine, Curriculum in Bioinformatics and Computational Biology
Abstract
  • Abstract Single cell experimental techniques reveal transcriptomic and epigenetic heterogeneity among cells, but how these are related is unclear. We present MATCHER, an approach for integrating multiple types of single cell measurements. MATCHER uses manifold alignment to infer single cell multi-omic profiles from transcriptomic and epigenetic measurements performed on different cells of the same type. Using scM&T-seq and sc-GEM data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondences. MATCHER also reveals new insights into the dynamic interplay between the transcriptome and epigenome in single embryonic stem cells and induced pluripotent stem cells.
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  • Article
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  • In Copyright
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  • The Author(s).
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  • English
Bibliographic citation
  • Genome Biology. 2017 Jul 24;18(1):138
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  • BioMed Central

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