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Cell. 2018 Jul 26;174(3):716-729.e27. doi: 10.1016/j.cell.2018.05.061. Epub 2018 Jun 28.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.

Author information

1
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
2
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Applied Physics and Applied Math, Columbia University, New York, NY, USA.
3
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Institute of Biotechnology, Vilnius University, Vilnius, Lithuania.
4
Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA.
5
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Biological Sciences, Columbia University, New York, NY, USA.
6
Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA; Applied Mathematics Program, Yale University, New Haven, CT, USA.
7
Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
8
Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA.
9
Applied Mathematics Program, Yale University, New Haven, CT, USA.
10
Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA; Applied Mathematics Program, Yale University, New Haven, CT, USA. Electronic address: smita.krishnaswamy@yale.edu.
11
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address: peerd@mskcc.org.

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.

KEYWORDS:

EMT; imputation; manifold learning; regulatory networks; single-cell RNA sequencing

PMID:
29961576
DOI:
10.1016/j.cell.2018.05.061
[Indexed for MEDLINE]

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