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Predicting Gene Expression Noise from Gene Expression Variations

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Transcriptome Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1751))

Abstract

The level of gene expression is known to vary from cell to cell and even in the same cell over time. This variability provides cells with the ability to mitigate environmental stresses and genetic perturbations, and facilitates gene expression evolution. Recently, many valuable gene expression noise data measured at the single-cell level and gene expression variation measured for cell populations have become available. In this chapter, we show how to perform integrative analysis using these data. Specifically, we introduce how to apply a machine learning technique (support vector regression) to explore the relationship between gene expression variations and stochastic noise.

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Author information

Authors and Affiliations

  1. Department of Human Genetics, McGill University, Montréal, Canada

    Xiaojian Shao

  2. The McGill University and Génome Québec Innovation Centre, Montréal, QC, Canada

    Xiaojian Shao

  3. Epigenomics and Computational Biology Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA

    Ming-an Sun

Authors
  1. Xiaojian Shao
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  2. Ming-an Sun
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Editors and Affiliations

  1. Department of Cell Biology and Genetics, School of Basic Medicine, Shenzhen University Health, Science Center, Shenzhen, China

    Yejun Wang

  2. Epigenomics and Computational Biology Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, Virginia, USA

    Ming-an Sun

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Shao, X., Sun, Ma. (2018). Predicting Gene Expression Noise from Gene Expression Variations. In: Wang, Y., Sun, Ma. (eds) Transcriptome Data Analysis. Methods in Molecular Biology, vol 1751. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7710-9_13

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  • DOI: https://doi.org/10.1007/978-1-4939-7710-9_13

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7709-3

  • Online ISBN: 978-1-4939-7710-9

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