放射生物组学数据分析中的多重假设检验免费

Multiple hypothesis testing in radiobiological omics study

随着高通量测序技术的发展,组学研究中获得的高维海量数据对统计分析提出了新的要求。在此情况下,传统的单个假设检验理论不再适用,多重假设检验问题日益得到重视。本文着重介绍多重假设检验中常用的3种错误测度——总体/族错误率(FWER)、错误发现率(FDR)和阳性错误发现率(pFDR)及其控制过程在放射生物组学数据分析中的应用,以期为放射生物学数据的统计分析提供参考。

With the development of high-throughput sequencing technology,the high-dimensional massive data obtained in omics study puts forward new requirements for statistical analysis.In this case,the traditional theory of single hypothesis testing is no longer applicable,and the issue of multiple hypothesis testing has received increasing attention.This paper introduced three commonly used error measures in multiple testing-family-wise error rate(FWER),false discovery rate(FDR),and positive false discovery rate(pFDR),and the control process in radiobiological omics data analysis,in order to provide a reference for statistical analysis of radiobiological data.