An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays.
An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays.
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DOI：
10.1093/bib/bby085
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发表时间：
2019-11-27
影响因子：
9.5
通讯作者：
Wang L
中科院分区：
文献类型：
--
作者：
Mallik S;Odom GJ;Gao Z;Gomez L;Chen X;Wang L
表观基因组关联研究（EWASs）在研究复杂疾病中DNA甲基化（DNAm）变异方面越来越受欢迎。Illumina甲基化阵列提供了一个经济、高通量和全面的平台来测量EWASs的甲基化状态。已经开发了许多软件工具来识别表观基因组中与疾病相关的差异甲基化区域（DMRs）。然而，在实践中，我们发现这些工具通常具有需要指定的多个参数设置，并且软件工具在不同参数下的性能通常不清楚。为了帮助用户在使用DNAm分析工具时更好地理解和选择最佳参数设置，我们对4种流行的DMR分析工具在60种不同的参数设置下进行了综合评估。除了评估功率、精密度、精密度-召回率曲线下面积、马修斯相关系数、F1得分和I型错误率外，我们还比较了分析结果的几个附加特征，包括DMRs的大小、方法之间的重叠和执行时间。结果表明，没有一个软件工具在默认参数设置下表现最好，当参数改变时，功率变化很大。总的来说，这些软件工具的精度是不错的。相反，当效应大小一致但较小时，所有方法都缺乏效力。在所有模拟场景中，comb-p始终具有最佳的灵敏度以及对假阳性率的良好控制。
Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.
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影响因子：
4.4
作者：
Bibikova, Marina;Barnes, Bret;Shen, Richard
通讯作者：
Shen, Richard
从头开始鉴定人基因组中差异甲基化区域。
影响因子：
3.9
作者：
Peters TJ;Buckley MJ;Statham AL;Pidsley R;Samaras K;V Lord R;Clark SJ;Molloy PL
通讯作者：
Molloy PL
影响因子：
12.3
作者：
Jaffe AE;Irizarry RA
通讯作者：
Irizarry RA
影响因子：
11
作者：
Ladd-Acosta, C.;Hansen, K. D.;Briem, E.;Fallin, M. D.;Kaufmann, W. E.;Feinberg, A. P.
通讯作者：
Feinberg, A. P.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y