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MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data

David van Dijk, Juozas Nainys, Roshan Sharma, Pooja Kaithail, Ambrose J. Carr, Kevin R. Moon, Linas Mazutis, Guy Wolf, Smita Krishnaswamy, Dana Pe'er
doi: https://doi.org/10.1101/111591
David van Dijk
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Juozas Nainys
2Sector of Microtechnologies, Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
4Department of Biological Sciences, Columbia University, New York, NY, USA
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Roshan Sharma
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
3Department of Applied Physics and Applied Math, Columbia University, New York, NY, USA
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Pooja Kaithail
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
4Department of Biological Sciences, Columbia University, New York, NY, USA
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Ambrose J. Carr
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
4Department of Biological Sciences, Columbia University, New York, NY, USA
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Kevin R. Moon
5Applied Mathematics Program, Yale University, New Haven, CT, USA
6Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA
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Linas Mazutis
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
2Sector of Microtechnologies, Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
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Guy Wolf
5Applied Mathematics Program, Yale University, New Haven, CT, USA
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Smita Krishnaswamy
6Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA
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Dana Pe'er
1Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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  • For correspondence: peerd@mskcc.org smita.krishnaswamy@yale.edu
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ABSTRACT

Single-cell RNA-sequencing is fast becoming a major technology that is revolutionizing biological discovery in fields such as development, immunology and cancer. The ability to simultaneously measure thousands of genes at single cell resolution allows, among other prospects, for the possibility of learning gene regulatory networks at large scales. However, scRNA-seq technologies suffer from many sources of significant technical noise, the most prominent of which is ‘dropout’ due to inefficient mRNA capture. This results in data that has a high degree of sparsity, with typically only ~10% non-zero values. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method for imputing missing values, and restoring the structure of the data. After MAGIC, we find that two- and three-dimensional gene interactions are restored and that MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and our newly generated epithelial-to-mesenchymal transition dataset.

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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 February 25, 2017.
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MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data
David van Dijk, Juozas Nainys, Roshan Sharma, Pooja Kaithail, Ambrose J. Carr, Kevin R. Moon, Linas Mazutis, Guy Wolf, Smita Krishnaswamy, Dana Pe'er
bioRxiv 111591; doi: https://doi.org/10.1101/111591
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MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data
David van Dijk, Juozas Nainys, Roshan Sharma, Pooja Kaithail, Ambrose J. Carr, Kevin R. Moon, Linas Mazutis, Guy Wolf, Smita Krishnaswamy, Dana Pe'er
bioRxiv 111591; doi: https://doi.org/10.1101/111591

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