Department of Statistics, National Cheng Kung University in Taiwan.
Department of Molecular Physiology and Biophysics, Vanderbilt University, USA.
Brief Bioinform. 2018 Nov 27;19(6):1247-1255. doi: 10.1093/bib/bbx061.
Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.
功效/样本量（功效）分析估计在数据集成功发现统计显著性的可能性。人们越来越认识到在实验的正确设计中进行功效分析的重要性。功效分析虽然复杂，但对于大型研究的成功却是必要的。设计出能够产生准确可靠结果的研究是很重要的。微阵列基因表达研究和基于基因分型的全基因组关联研究的功效计算方法已经得到很好的确立。高通量测序（HTS）大大提高了我们在尽可能高的分辨率（每个核苷酸）下进行生物医学研究的能力。然而，测序数据的功效计算复杂性比简单的基因分型阵列数据要大得多。最近已经对基于 HTS 的研究的功效计算方法进行了研究，但尚未广为人知或广泛使用。在本文中，我们描述了目前可用于一系列基于 HTS 的研究的功效计算方法，包括 DNA 测序、RNA 测序、微生物组测序和染色质免疫沉淀测序。最重要的是，我们回顾了几种测序数据的功效分析方法，并为每种数据类型引导读者了解相关方法。
