Recovering gene interactions from single-cell data using data diffusion Journal Article

Authors: van Dijk, D.; Sharma, R.; Nainys, J.; Yim, K.; Kathail, P.; Carr, A. J.; Burdziak, C.; Moon, K. R.; Chaffer, C. L.; Pattabiraman, D.; Bierie, B.; Mazutis, L.; Wolf, G.; Krishnaswamy, S.; Pe'er, D.
Article Title: Recovering gene interactions from single-cell data using data diffusion
Abstract: Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed “dropout,” which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations. A new algorithm overcomes limitations of data loss in single-cell sequencing experiments. © 2018 Elsevier Inc.
Keywords: emt; imputation; regulatory networks; manifold learning; single-cell rna sequencing
Journal Title: Cell
Volume: 174
Issue: 3
ISSN: 0092-8674
Publisher: Cell Press  
Date Published: 2018-07-26
Start Page: 716
End Page: 729.e27
Language: English
DOI: 10.1016/j.cell.2018.05.061
PROVIDER: scopus
PUBMED: 29961576
Notes: Article -- Export Date: 3 December 2018 -- Source: Scopus
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MSK Authors
  1. Dana Pe'er
    12 Pe'er
  2. Roshan Sharma
    2 Sharma
  3. Ambrose James Carr
    2 Carr