Sample size calculation for differential expression analysis of RNA-seq data

Stephanie Page Hoskins, Derek Shyr, Yu Shyr

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationFrontiers of Biostatistical Methods and Applications in Clinical Oncology
PublisherSpringer Singapore
Pages359-379
Number of pages21
ISBN (Electronic)9789811001260
ISBN (Print)9789811001246
DOIs
Publication statusPublished - 2017 Oct 3

Fingerprint

Sample Size Calculation
Precision Medicine
Differential Expression
Sample Size
Research Design
Binomial Distribution
RNA
Life Cycle Stages
Experimental design
Medicine
medicine
Technology
data capture
Costs and Cost Analysis
ability
statistical method
Negative binomial distribution
life cycle
popularity
Life Cycle

All Science Journal Classification (ASJC) codes

  • Medicine(all)
  • Mathematics(all)
  • Social Sciences(all)

Cite this

Hoskins, S. P., Shyr, D., & Shyr, Y. (2017). Sample size calculation for differential expression analysis of RNA-seq data. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology (pp. 359-379). Springer Singapore. https://doi.org/10.1007/978-981-10-0126-0_22
Hoskins, Stephanie Page ; Shyr, Derek ; Shyr, Yu. / Sample size calculation for differential expression analysis of RNA-seq data. Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore, 2017. pp. 359-379
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Hoskins, SP, Shyr, D & Shyr, Y 2017, Sample size calculation for differential expression analysis of RNA-seq data. in Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore, pp. 359-379. https://doi.org/10.1007/978-981-10-0126-0_22

Sample size calculation for differential expression analysis of RNA-seq data. / Hoskins, Stephanie Page; Shyr, Derek; Shyr, Yu.

Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore, 2017. p. 359-379.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Hoskins SP, Shyr D, Shyr Y. Sample size calculation for differential expression analysis of RNA-seq data. In Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer Singapore. 2017. p. 359-379 https://doi.org/10.1007/978-981-10-0126-0_22