检索结果 - Mark D. Robinson

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  1. 1
    A scaling normalization method for differential expression analysis of RNA-seq data

    A scaling normalization method for differential expression analysis of RNA-seq data 由 Mark D. Robinson, Alicia Oshlack

    出版 2010
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  2. 2
    iCOBRA: open, reproducible, standardized and live method benchmarking

    iCOBRA: open, reproducible, standardized and live method benchmarking 由 Charlotte Soneson, Mark D. Robinson

    出版 2016
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  3. 3
    DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

    DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics 由 Małgorzata Nowicka, Mark D. Robinson

    出版 2016
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  4. 4
    Towards unified quality verification of synthetic count data with <i>countsimQC</i>

    Towards unified quality verification of synthetic count data with <i>countsimQC</i> 由 Charlotte Soneson, Mark D. Robinson

    出版 2017
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  5. 5
    Bias, robustness and scalability in single-cell differential expression analysis

    Bias, robustness and scalability in single-cell differential expression analysis 由 Charlotte Soneson, Mark D. Robinson

    出版 2018
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  6. 6
    DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

    DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics 由 Małgorzata Nowicka, Mark D. Robinson

    出版 2016
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  7. 7
    A comparison of Affymetrix gene expression arrays

    A comparison of Affymetrix gene expression arrays 由 Mark D. Robinson, Terence P. Speed

    出版 2007
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  8. 8
    Moderated statistical tests for assessing differences in tag abundance

    Moderated statistical tests for assessing differences in tag abundance 由 Mark D. Robinson, Gordon K. Smyth

    出版 2007
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  9. 9
    Comparison of clustering methods for high‐dimensional single‐cell flow and mass cytometry data

    Comparison of clustering methods for high‐dimensional single‐cell flow and mass cytometry data 由 Lukas M. Weber, Mark D. Robinson

    出版 2016
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  10. 10
    Robustly detecting differential expression in RNA sequencing data using observation weights

    Robustly detecting differential expression in RNA sequencing data using observation weights 由 Xiaobei Zhou, Helen Lindsay, Mark D. Robinson

    出版 2014
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  11. 11
    Electronic Health Record Effects on Work-Life Balance and Burnout Within the I3 Population Collaborative

    Electronic Health Record Effects on Work-Life Balance and Burnout Within the I3 Population Collaborative 由 Sandy Robertson, Mark D. Robinson, Alfred Reid

    出版 2017
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  12. 12
    A systematic performance evaluation of clustering methods for single-cell RNA-seq data

    A systematic performance evaluation of clustering methods for single-cell RNA-seq data 由 Angelo Duò, Mark D. Robinson, Charlotte Soneson

    出版 2018
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  13. 13
    A systematic performance evaluation of clustering methods for single-cell RNA-seq data

    A systematic performance evaluation of clustering methods for single-cell RNA-seq data 由 Angelo Duò, Mark D. Robinson, Charlotte Soneson

    出版 2020
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  14. 14
    A systematic performance evaluation of clustering methods for single-cell RNA-seq data

    A systematic performance evaluation of clustering methods for single-cell RNA-seq data 由 Angelo Duò, Mark D. Robinson, Charlotte Soneson

    出版 2018
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  15. 15
    From RNA-seq reads to differential expression results

    From RNA-seq reads to differential expression results 由 Alicia Oshlack, Mark D. Robinson, Matthew D. Young

    出版 2010
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  16. 16
    Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

    Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences 由 Charlotte Soneson, Michael I. Love, Mark D. Robinson

    出版 2015
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  17. 17
    pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools

    pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools 由 Pierre‐Luc Germain, Anthony Sonrel, Mark D. Robinson

    出版 2020
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  18. 18
    Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

    Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences 由 Charlotte Soneson, Michael I. Love, Mark D. Robinson

    出版 2016
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  19. 19
    stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage

    stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage 由 Koen Van den Berge, Charlotte Soneson, Mark D. Robinson

    出版 2017
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  20. 20
    FunSpec: a web-based cluster interpreter for yeast

    FunSpec: a web-based cluster interpreter for yeast 由 Mark D. Robinson, Jörg Grigull, Naveed Mohammad, Timothy R. Hughes

    出版 2002
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