false discovery rate

错误发现率
  • 文章类型: Journal Article
    DNA甲基化改变已被广泛研究为环境诱导的疾病风险的介体。随着技术的新进展,表观基因组DNA甲基化数据(EWAS)已成为人类群体表观遗传学研究的新标准。然而,迄今为止,大多数关于中介效应的表观遗传学研究仅涉及选定的(基因特异性)候选甲基化标记.EWAS中介分析迫切需要适当的分析方法。在这一章中,我们概述了高维调解分析的最新进展,应用于两个DNA甲基化数据。
    DNA methylation alterations have been widely studied as mediators of environmentally induced disease risks. With new advances in technique, epigenome-wide DNA methylation data (EWAS) have become the new standard for epigenetic studies in human populations. However, to date most epigenetic studies of mediation effects only involve selected (gene-specific) candidate methylation markers. There is an urgent need for appropriate analytical methods for EWAS mediation analysis. In this chapter, we provide an overview of recent advances on high-dimensional mediation analysis, with application to two DNA methylation data.
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  • 文章类型: Journal Article
    With the increased availability of survival datasets, that comprise both molecular information (e.g., gene expression), and clinical information (e.g., patient survival), numerous genes are proposed as prognostic biomarkers. Despite efforts and money invested, very few of these biomarkers have been clinically validated and are used routinely. A high false discovery rate is assumed to be largely responsible for this, in particular as the number of tested genes is extremely high relative to the number of patients followed. Here, after describing the historical methodologies on which recent developments have often been based, this review describes studies that have been performed in the last few years. The concepts will be illustrated for a renal cancer dataset, and the corresponding scripts are provided (Supporting Information). These new developments belong to three main fields of applications. First, variable selection concerns various improvements to lasso penalization. Second, accurate definition of p-values and control of the false discovery rate have also been the subject of many studies. Third, the incorporation of biological knowledge, often through the form of networks or pathways, can be used as an a priori and/or to reduce dimensionality. These new and promising developments deserve benchmarking by independent groups not involved in their development, with various independent datasets. Further work on the methodologies is also still required.
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