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      SCnorm: robust normalization of single-cell RNA-seq data

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          Summary

          Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

            Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.
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              Is Open Access

              BASiCS: Bayesian Analysis of Single-Cell Sequencing Data

              Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell’s lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                12 April 2017
                17 April 2017
                June 2017
                17 October 2017
                : 14
                : 6
                : 584-586
                Affiliations
                [1 ]Department of Statistics, University of Wisconsin–Madison, Madison, WI
                [2 ]Morgridge Institute for Research, Madison, WI, USA
                [3 ]Laboratory of Genetics, University of Wisconsin–Madison, Madison, WI, USA
                [4 ]Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
                Author notes
                [* ]Corresponding author: Christina Kendziorski: kendzior@ 123456biostat.wisc.edu
                [5]

                Equal contributors

                Article
                NIHMS862746
                10.1038/nmeth.4263
                5473255
                28418000
                fff28db3-403b-4c8c-ba24-0e7ce5b503ee

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                Life sciences

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