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

Stephanie Page Hoskins, Derek Shyr, Yu Shyr

研究成果: Chapter

摘要

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.

原文English
主出版物標題Frontiers of Biostatistical Methods and Applications in Clinical Oncology
發行者Springer Singapore
頁面359-379
頁數21
ISBN(電子)9789811001260
ISBN(列印)9789811001246
DOIs
出版狀態Published - 2017 十月 3

All Science Journal Classification (ASJC) codes

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

指紋 深入研究「Sample size calculation for differential expression analysis of RNA-seq data」主題。共同形成了獨特的指紋。

  • 引用此

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