Mediation analysis method review of high throughput data.

IF 0.9 4区 数学 Q3 Mathematics
Qiang Han, Yu Wang, Na Sun, Jiadong Chu, Wei Hu, Yueping Shen
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引用次数: 0

Abstract

High-throughput technologies have made high-dimensional settings increasingly common, providing opportunities for the development of high-dimensional mediation methods. We aimed to provide useful guidance for researchers using high-dimensional mediation analysis and ideas for biostatisticians to develop it by summarizing and discussing recent advances in high-dimensional mediation analysis. The method still faces many challenges when extended single and multiple mediation analyses to high-dimensional settings. The development of high-dimensional mediation methods attempts to address these issues, such as screening true mediators, estimating mediation effects by variable selection, reducing the mediation dimension to resolve correlations between variables, and utilizing composite null hypothesis testing to test them. Although these problems regarding high-dimensional mediation have been solved to some extent, some challenges remain. First, the correlation between mediators are rarely considered when the variables are selected for mediation. Second, downscaling without incorporating prior biological knowledge makes the results difficult to interpret. In addition, a method of sensitivity analysis for the strict sequential ignorability assumption in high-dimensional mediation analysis is still lacking. An analyst needs to consider the applicability of each method when utilizing them, while a biostatistician could consider extensions and improvements in the methodology.

高通量数据的中介分析方法综述。
高通量技术使得高维设置越来越普遍,为高维中介方法的发展提供了机会。本文旨在通过总结和讨论高维中介分析的最新进展,为研究人员使用高维中介分析提供有益的指导,并为生物统计学家发展高维中介分析提供思路。该方法在将单、多中介分析扩展到高维环境时仍面临许多挑战。高维中介方法的发展试图解决这些问题,如筛选真正的中介,通过变量选择估计中介效果,降低中介维度以解决变量之间的相关性,并利用复合零假设检验来检验它们。虽然这些问题在一定程度上得到了解决,但仍然存在一些挑战。首先,在选择变量进行中介时,很少考虑中介之间的相关性。其次,在没有纳入先前的生物学知识的情况下缩小规模会使结果难以解释。此外,对于高维中介分析中严格顺序可忽略性假设,还缺乏一种灵敏度分析方法。分析人员在使用每种方法时需要考虑它们的适用性,而生物统计学家可以考虑方法的扩展和改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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