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      CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data

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          Abstract

          We develop CellSIUS (Cell Subtype Identification from Upregulated gene Sets) to fill a methodology gap for rare cell population identification for scRNA-seq data. CellSIUS outperforms existing algorithms for specificity and selectivity for rare cell types and their transcriptomic signature identification in synthetic and complex biological data. Characterization of a human pluripotent cell differentiation protocol recapitulating deep-layer corticogenesis using CellSIUS reveals unrecognized complexity in human stem cell-derived cellular populations. CellSIUS enables identification of novel rare cell populations and their signature genes providing the means to study those populations in vitro in light of their role in health and disease.

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          The online version of this article (10.1186/s13059-019-1739-7) contains supplementary material, which is available to authorized users.

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            Neuronal subtype specification in the cerebral cortex.

            In recent years, tremendous progress has been made in understanding the mechanisms underlying the specification of projection neurons within the mammalian neocortex. New experimental approaches have made it possible to identify progenitors and study the lineage relationships of different neocortical projection neurons. An expanding set of genes with layer and neuronal subtype specificity have been identified within the neocortex, and their function during projection neuron development is starting to be elucidated. Here, we assess recent data regarding the nature of neocortical progenitors, review the roles of individual genes in projection neuron specification and discuss the implications for progenitor plasticity.
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              Accounting for technical noise in single-cell RNA-seq experiments.

              Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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                Author and article information

                Contributors
                wegmann@imsb.biol.ethz.ch
                marilisa.neri@novartis.com
                sven.schuierer@novartis.com
                bbilican@FL60inc.com
                huyen.nguyen@novartis.com
                florian.nigsch@novartis.com
                felipa.mapa@novartis.com
                annick.waldt@novartis.com
                rachel.cuttat@novartis.com
                max@insitro.com
                joe.raymond@novartis.com
                akaykas@insitro.com
                guglielmo.roma@novartis.com
                caroline.gubser_keller@novartis.com
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                17 July 2019
                17 July 2019
                2019
                : 20
                : 142
                Affiliations
                [1 ]ISNI 0000 0001 1515 9979, GRID grid.419481.1, Novartis Institutes for Biomedical Research, ; Basel, Switzerland
                [2 ]ISNI 0000 0004 0439 2056, GRID grid.418424.f, Novartis Institutes for Biomedical Research, ; Cambridge, USA
                [3 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, Present Address: Institute of Molecular Systems Biology, , ETH Zurich, ; Zurich, Switzerland
                [4 ]Present Address: Insitro, San Francisco, USA
                [5 ]Present Address: Flagship Pioneering, Cambridge, USA
                Author information
                http://orcid.org/0000-0001-9459-6076
                Article
                1739
                10.1186/s13059-019-1739-7
                6637521
                31315641
                45a4ee78-f635-44f1-9c2b-124c7c3f0c48
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 7 May 2019
                : 13 June 2019
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                © The Author(s) 2019

                Genetics
                single-cell rna sequencing,data analysis,rare cell types,clustering,software,benchmarking,human pluripotent stem cells,cortical development,choroid plexus,lineage mapping

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