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Statistical Modeling of High Dimensional Counts

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RNA Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2284))

Abstract

Statistical modeling of count data from RNA sequencing (RNA-seq) experiments is important for proper interpretation of results. Here I will describe how count data can be modeled using count distributions, or alternatively analyzed using nonparametric methods. I will focus on basic routines for performing data input, scaling/normalization, visualization, and statistical testing to determine sets of features where the counts reflect differences in gene expression across samples. Finally, I discuss limitations and possible extensions to the models presented here.

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Author information

Authors and Affiliations

  1. Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA

    Michael I. Love

  2. Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA

    Michael I. Love

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  1. Michael I. Love
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Editors and Affiliations

  1. Dipartimento Di Bioscienze Biotecnologie E Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Ernesto Picardi

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Love, M.I. (2021). Statistical Modeling of High Dimensional Counts. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 2284. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1307-8_7

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  • DOI: https://doi.org/10.1007/978-1-0716-1307-8_7

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1306-1

  • Online ISBN: 978-1-0716-1307-8

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