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- W2952382191 abstract "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.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.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." @default.
- W2952382191 created "2019-06-27" @default.
- W2952382191 creator A5063551418 @default.
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- W2952382191 date "2019-06-04" @default.
- W2952382191 modified "2023-10-17" @default.
- W2952382191 title "Single-cell transcriptomics unveils gene regulatory network plasticity" @default.
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- W2952382191 doi "https://doi.org/10.1186/s13059-019-1713-4" @default.
- W2952382191 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6547541" @default.
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