A data-driven approach to preprocessing Illumina 450K methylation array data.
A data-driven approach to preprocessing Illumina 450K methylation array data.
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DOI：
10.1186/1471-2164-14-293
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发表时间：
2013-05-01
期刊：
影响因子：
4.4
通讯作者：
Schalkwyk LC
中科院分区：
文献类型：
--
作者：
Pidsley R;Y Wong CC;Volta M;Lunnon K;Mill J;Schalkwyk LC
作为最稳定且实验上最易获取的表观遗传标记，DNA甲基化引起了研究界的极大兴趣。DNA甲基化在不同组织、发育过程以及疾病发病机制中的情况尚未得到很好的表征。因此，需要有快速且成本效益高的方法来评估全基因组DNA甲基化水平。Illumina Infinium HumanMethylation450（450K）芯片是DNA甲基化分析现有方法中非常有用的补充，但其复杂的设计（包含两种不同的检测方法）需要仔细考虑。相应地，已经发表了几种归一化方案。我们利用了与基因组印记和X染色体失活（XCI）相关的已知DNA甲基化模式，以及芯片上存在的单核苷酸多态性（SNP）基因分型检测的性能，得出了三个独立的指标，我们用这些指标来测试校正和归一化的替代方案。这些指标作为数据集的质量分数也具有潜在的实用性。
任何特定CpG位点的DNA甲基化标准指数是β = M/(M + U + 100)，其中M和U分别是甲基化和未甲基化的信号强度。由原始信号强度计算得出的β值（GenomeStudio的默认行为）表现良好，但我们利用11个甲基化组数据集证明，分位数归一化方法通过所有三个指标都能带来显著的改进，即使在高度一致的数据中也是如此。常用的对β值进行归一化的方法不如对M和U分别进行归一化，并且对I型和II型检测分别进行归一化也是有利的。对分位数进行更复杂的操作被证明是适得其反的。
仔细选择预处理步骤可以最小化方差，从而提高统计功效，特别是对于检测可能与复杂疾病表型相关的微小的绝对DNA甲基化变化。为了方便研究界，我们创建了一个用户友好的R软件包，名为wateRmelon，可从bioconductor下载，与现有的methylumi、minfi和IMA软件包兼容，它允许其他人在450K数据上使用相同的归一化方法和数据质量测试。
As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. The standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas (βs) calculated from raw signal intensities (the default GenomeStudio behavior) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. Careful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created a user-friendly R software package called wateRmelon, downloadable from bioConductor, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilize the same normalization methods and data quality tests on 450K data.
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影响因子：
3.5
作者：
Dempster EL;Pidsley R;Schalkwyk LC;Owens S;Georgiades A;Kane F;Kalidindi S;Picchioni M;Kravariti E;Toulopoulou T;Murray RM;Mill J
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