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      SCENIC: Single-cell regulatory network inference and clustering

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          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 references 42

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          Is Open Access

          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

             R. Edgar (2002)
            The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at
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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at:

                Author and article information

                Nat Methods
                Nat. Methods
                Nature methods
                23 April 2018
                09 October 2017
                November 2017
                07 May 2018
                : 14
                : 11
                : 1083-1086
                [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

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