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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.

Type Journal
ISBN 1097-4172 (Electronic) 0092-8674 (Linking)
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.
Responsible Garvan Author Dr Christine Chaffer
Publisher Name CELL
Published Date 2018-07-01
Published Volume 174
Published Issue 3
Published Pages 716-729 e27
Status Published in-print
DOI 10.1016/j.cell.2018.05.061
URL link to publisher's version https://www.ncbi.nlm.nih.gov/pubmed/29961576