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


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
Number of pages21
ISBN (Electronic)9789811001260
ISBN (Print)9789811001246
Publication statusPublished - 2017 Oct 3

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Sample size calculation for differential expression analysis of RNA-seq data'. Together they form a unique fingerprint.

Cite this