Recovering gene interactions from single-cell data using data diffusion

By David van Dijk, Roshan Sharma, J. Nainys, Kristina M. Yim, Pooja Kathail, Ambrose J. Carr, Cassandra Burdziak, Kevin R. Moon, Christine L. Chaffer, D. Pattabiraman and 5 more. Cell, 2018

1,612 citations recorded by the source corpus.

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. MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures and uncovers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/855cb54a89f46aaa1fee64a0eb32f062812a0913

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In Molecular Biology

Citations in the corpus

Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape (2018, methodology): “A proposed solution to this problem is expression imputation 36,37 , which we performed by creating ‘pseudo-cells’ as averages of the expression proﬁles of nearest neighbour cells in UMAP space.”
Best practices for single-cell analysis across modalities (2023, background): “To gain insights into whether the local or global chromatin landscape influences the expression of a gene in a specific cell state, the predictability of expression based on the local neighbourhood Fig.”
Gene regulatory network inference in the era of single-cell multi-omics (2023, methodology): “Imputation methods can be used to reduce the number of ‘dropouts’ (caused by under-sampling of mRNAs or accessible DNA reads) 153–155 , although it has been shown that they might have detrimental effects on GRN reconstruction 156 .”
Challenges in unsupervised clustering of single-cell RNA-seq data (2019, methodology): “There are several statistical methods available for imputing zeros 25,47,48 , but they all rely on pre existing cell–cell or gene–gene correlations in the data to infer the appropriate imputed value.”