The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond View Full Text


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Article Info

DATE

2019-02

AUTHORS

Jessica C. Mar

ABSTRACT

The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more. More... »

PAGES

89-94

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  • Journal

    TITLE

    Biophysical Reviews

    ISSUE

    1

    VOLUME

    11

    Author Affiliations

    From Grant

  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12551-018-0494-4

    DOI

    http://dx.doi.org/10.1007/s12551-018-0494-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111243197

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30617454


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