Integrating single-cell transcriptomic data across different conditions, technologies, and species View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-04-02

AUTHORS

Andrew Butler, Paul Hoffman, Peter Smibert, Efthymia Papalexi, Rahul Satija

ABSTRACT

A new computational approach enables integrative analysis of disparate single-cell RNA–sequencing data sets by identifying shared patterns of variation between cell subpopulations.

PAGES

411-420

References to SciGraph publications

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  • 2016-02-17. Classification of low quality cells from single-cell RNA-seq data in GENOME BIOLOGY
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  • 2014-05-18. Bayesian approach to single-cell differential expression analysis in NATURE METHODS
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  • 2017-03-27. SC3: consensus clustering of single-cell RNA-seq data in NATURE METHODS
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  • 2014-01-02. Full-length RNA-seq from single cells using Smart-seq2 in NATURE PROTOCOLS
  • 2017-03-17. Correction: Corrigendum: Nuclear RNA-seq of single neurons reveals molecular signatures of activation in NATURE COMMUNICATIONS
  • 2017-03-28. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data in GENOME BIOLOGY
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  • 2015-12-10. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data in GENOME BIOLOGY
  • 2017-03-06. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning in NATURE METHODS
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  • 2017-12-11. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation in NATURE BIOTECHNOLOGY
  • Journal

    TITLE

    Nature Biotechnology

    ISSUE

    5

    VOLUME

    36

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nbt.4096

    DOI

    http://dx.doi.org/10.1038/nbt.4096

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1101885942

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29608179


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