期刊
GENOME BIOLOGY
卷 20, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13059-019-1713-4
关键词
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资金
- Spanish Institute of Health Carlos III (ISCIII) [CP14/00229]
- Ministerio de Ciencia, Innovacion y Universidades [SAF2017-89109-P]
- AEI/FEDER, UE
- ISCIII
- Generalitat de Catalunya
- Spanish Ministry of Economy, Industry and Competitiveness(MEIC)
- Centro de Excelencia Severo Ochoa
- CERCA Programme/Generalitat de Catalunya
- Spanish Ministry of Economy, Industry and Competitiveness (MEIC) through the Instituto de Salud Carlos III
- Generalitat de Catalunya through Departament deSalut and Departament d'Empresa i Coneixement
- Spanish Ministry of Economy, Industry and Competitiveness (MEIC)
- European Regional Development Fund (ERDF)
BackgroundSingle-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.ResultsWe 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.ConclusionsOur 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.
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