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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >New variable selection strategy for analysis of high-dimensional DNA methylation data
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New variable selection strategy for analysis of high-dimensional DNA methylation data

机译：高维DNA甲基化数据分析的新变量选择策略

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摘要

In genetic association studies, regularization methods are often used due to their computational efficiency for analysis of high-dimensional genomic data. DNA methylation data generated from Infinium HumanMethylation450 BeadChip Kit have a group structure where an individual gene consists of multiple Cytosine-phosphate-Guanine (CpG) sites. Consequently, group-based regularization can precisely detect outcome-related CpG sites. Representative examples are sparse group lasso (SGL) and network-based regularization. The former is powerful when most of the CpG sites within the same gene are associated with a phenotype outcome. In contrast, the latter is preferred when only a few of the CpG sites within the same gene are related to the outcome. In this paper, we propose new variable selection strategy based on a selection probability that measures selection frequency of individual variables selected by both SGL and network-based regularization. In extensive simulation study, we demonstrated that the proposed strategy can show relatively outstanding selection performance under any situation, compared with both SGL and network-based regularization. Also, we applied the proposed strategy to identify differentially methylated CpG sites and their corresponding genes from ovarian cancer data.
机译：在遗传关联研究中，通常使用正则化方法是由于它们的计算效率进行了分析的高维基因组数据。从1英尼诺替纳米甲基化450珠芯片试剂盒产生的DNA甲基化数据具有组结构，其中单个基因由多种胞嘧啶 - 磷酸胍（CPG）位点组成。因此，基于组的正则化可以精确地检测与结果相关的CPG站点。代表性示例是稀疏组套索（SGL）和基于网络的正则化。当同一基因的大多数CPG位点与表型结果相关时，前者是强大的。相反，当同一基因中只有少数CpG位点与结果有关时，后者是优选的。在本文中，我们提出了基于选择概率的新的变量选择策略，这些概率测量由SGL和基于网络的正规化选择的各个变量的选择频率。在广泛的仿真研究中，我们证明，与SGL和基于网络的正规化相比，拟议的策略可以在任何情况下都显示出相对突出的选择性能。此外，我们应用了所提出的策略来鉴定卵巢癌数据的差异甲基化的CPG位点及其相应的基因。

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