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Single-cell transcriptomics unveils gene regulatory network plasticity.
- Source :
-
Genome biology [Genome Biol] 2019 Jun 04; Vol. 20 (1), pp. 110. Date of Electronic Publication: 2019 Jun 04. - Publication Year :
- 2019
-
Abstract
- Background: Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.<br />Results: We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.<br />Conclusions: Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.
Details
- Language :
- English
- ISSN :
- 1474-760X
- Volume :
- 20
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Genome biology
- Publication Type :
- Academic Journal
- Accession number :
- 31159854
- Full Text :
- https://doi.org/10.1186/s13059-019-1713-4