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Frontiers of Biostatistical Methods and Applications in Clinical Oncology pp 359–379Cite as

Sample Size Calculation for Differential Expression Analysis of RNA-Seq Data

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Abstract

The Holy Grail of precision medicine is the comprehensive integration of patient genotypic with phenotypic data to develop personalized disease prevention and treatment strategies. Next-generation sequencing technologies (NGS) and other types of high-throughput assays have exploded in popularity in recent years, thanks to their ability to produce an enormous volume of data quickly and at relatively low cost compared to more traditional laboratory methods. The ability to generate big data brings us one step closer to the realization of precision medicine; nevertheless, across the life cycle of such data, from experimental design to data capture, management, analysis, and utilization, many challenges remain. In this paper, we reviewed and discussed several statistical methods to estimate sample size based on the Poisson and Negative Binomial distributions for RNAseq experimental design.

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Authors and Affiliations

  1. Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA

    Stephanie Page Hoskins & Yu Shyr

  2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Derek Shyr

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  1. Stephanie Page Hoskins
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  2. Derek Shyr
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Correspondence to Stephanie Page Hoskins .

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Editors and Affiliations

  1. Graduate School of Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Shigeyuki Matsui

  2. Cancer Research and Biostatistics, Seattle, Washington, USA

    John Crowley

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Hoskins, S.P., Shyr, D., Shyr, Y. (2017). Sample Size Calculation for Differential Expression Analysis of RNA-Seq Data. In: Matsui, S., Crowley, J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0126-0_22

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