Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cell Line
  • Epistasis, Genetic / genetics
  • Gene Expression Profiling / methods*
  • Gene Regulatory Networks / genetics
  • Humans
  • Markov Chains
  • MicroRNAs / genetics
  • RNA, Messenger / genetics
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*
  • Software


  • MicroRNAs
  • RNA, Messenger