Cited by 1,829Single-cell and spatial transcriptomicsGene expression and cancer classificationGene Regulatory Network Analysis

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

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van Dijk, David, Sharma, Roshan, Nainys, Juozas, Yim, Kristina, Kathail, Pooja, Carr, Ambrose J., Burdziak, Cassandra, Moon, Kevin R. +7 more·2018·Cell

Published version of a preprint

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.

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Crossref · 10.1016/j.cell.2018.05.061

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  • The cellular transcriptomic state space can be modeled as a smooth, low-dimensional manifold or a set of manifolds.

    “We begin by modeling the cellular transcriptomic state space as a smooth, low-dimensional manifold or set of manifolds.”
  • Simple models are often sufficient for cell-type annotation, and smoothing gene activity scores among neighbouring cells, often using MAGIC, can enhance visualization.

    “To guide cell-type annotation, simple models are often sufficient, and visualization can be enhanced by smoothing gene activity scores among neighbouring cells, which is often performed using MAGIC152.”

    From Best practices for single-cell analysis across modalities (2023) · cites this paper as [152]

  • Preprocessing with data diffusion can reduce sparseness and potentially improve visualization and clustering.

    “Pre-processing by data diffusion can help to combat sparseness and may improve visualization and cluster analysis (van Dijk et al., 2018).”

    From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as van Dijk et al., 2018

  • Performing expression recovery has been shown to improve the estimation of gene-gene correlations.

    “Performing expression recovery has been shown to improve the estimation of gene–gene correlations (van Dijk et al, 2018; Eraslan et al, 2019).”

    From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as van Dijk et al, 2018

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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.
Affiliations
  • Memorial Sloan Kettering Cancer Center (US)
  • Yale University (US)
  • Garvan Institute of Medical Research (AU)
  • Whitehead Institute for Biomedical Research (US)
Corresponding author
  • Krishnaswamy, Smita
  • Pe’er, Dana
Venue
Cell
Publisher
Elsevier BV
Published
2018-07
Volume
174
Issue
3
Pages
716-729.e27
ISSN
0092-8674 (ISSN-L)
OpenAlex
W2805619986
Cited by
1,829
References
61
Citations by year
2026: 178 · 2025: 250 · 2024: 237 · 2023: 251 · 2022: 247 · 2021: 220 · 2020: 218 · 2019: 136 · 2018: 72 · 2017: 15 · 2016: 1 · 2013: 1 · 2012: 1
FWCI
58.43
Type
journal article
Language
en
Open access
Locations
  • Cell (publishedVersion)
  • SSRN Electronic Journal (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • NIH (DP1-HD084071, R01CA164729, U54 CA209975, P30 CA008748)
  • Gerry Center for Metastasis and Tumor Ecosystems
  • American Cancer Society (IRG-58-012-58)
  • DRC (3P30DK045735)
  • Simons SFARI (504633)
  • NWO Rubicon Fellowship (825.14.016)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
magic, data diffusion, single-cell RNA sequencing, gene-gene interaction, epithelial-mesenchymal transition, gene regulatory interactions, phenotypic continuum
MeSH terms
Algorithms, Cell Line, Epistasis, Genetic, Humans, Markov Chains, RNA, Messenger, Software, Sequence Analysis, RNA, Gene Expression Profiling, MicroRNAs, Gene Regulatory Networks, Single-Cell Analysis

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