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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

          Yoav Benjamini, Yosef Hochberg (1995)
          Article 0 comments Cited 23666 times – based on 0 reviews     Review now
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            Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

            Saiful Islam, Una Kjällquist, Annalena Moliner … (2011)
            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.
            Article 0 comments Cited 400 times – based on 0 reviews     Review now
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              BASiCS: Bayesian Analysis of Single-Cell Sequencing Data

              Catalina A. Vallejos, John Marioni, Sylvia Richardson (2015)
              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.
              Article 0 comments Cited 129 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 101215604
                Journal ID (pubmed-jr-id): 32338
                Journal ID (nlm-ta): Nat Methods
                Journal ID (iso-abbrev): Nat. Methods
                Title: Nature methods
                ISSN (Print): 1548-7091
                ISSN (Electronic): 1548-7105
                Publication date Nihms-submitted: 12 April 2017
                Publication date (Electronic): 17 April 2017
                Publication date (Print): June 2017
                Publication date PMC-release: 17 October 2017
                Volume: 14
                Issue: 6
                Pages: 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
                Manuscript ID: NIHMS862746
                DOI: 10.1038/nmeth.4263
                PMC ID: 5473255
                PubMed ID: 28418000
                SO-VID: fff28db3-403b-4c8c-ba24-0e7ce5b503ee
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                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                ScienceOpen disciplines: Life sciences

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