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      SCENIC: Single-cell regulatory network inference and clustering

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          Abstract

          Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.

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          Most cited references17

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          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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            MapReduce

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              Is Open Access

              A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

              Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                23 April 2018
                09 October 2017
                November 2017
                07 May 2018
                : 14
                : 11
                : 1083-1086
                Affiliations
                [1 ]VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
                [2 ]KU Leuven, Department of Human Genetics, Leuven, Belgium
                [3 ]KU Leuven ESAT/STADIUS, VDA-lab. Leuven, Belgium
                [4 ]IMEC Smart Applications and Innovation Services. Leuven, Belgium
                [5 ]KU Leuven, Department of Imaging and Pathology, Translational Cell and Tissue Research, Leuven, Belgium
                [6 ]University of Liège, Department of Electrical Engineering and Computer Science, Liège, Belgium
                [7 ]VIB Center for Cancer Biology, Laboratory for Molecular Cancer Biology, Leuven, Belgium
                [8 ]KU Leuven, Department of Oncology, Leuven, Belgium
                Author notes
                Article
                EMS74166
                10.1038/nmeth.4463
                5937676
                28991892
                ece5d067-710f-4e1f-b86c-6b25ff7c46cc

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                Life sciences
                Life sciences

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