Iacono, GiovanniMassoni-Badosa, RamonHeyn, Holger2019-09-202019-09-202019Iacono G, Massoni-Badosa R, Heyn H. Single-cell transcriptomics unveils gene regulatory network plasticity. Genome Biol. 2019;20(1):110. DOI: 10.1186/s13059-019-1713-41474-7596http://hdl.handle.net/10230/42304Background: 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. 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. 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.application/pdfeng© 2019, Giovanni Iacono, Ramon Massoni-Badosa, Holger Heyn. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s13059-019-1713-4info:eu-repo/semantics/openAccess