An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.
An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies.
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DOI：
10.1186/s13059-016-0935-y
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发表时间：
2016-05-03
期刊：
影响因子：
12.3
通讯作者：
Greenwood CM
中科院分区：
文献类型：
--
作者：
McGregor K;Bernatsky S;Colmegna I;Hudson M;Pastinen T;Labbe A;Greenwood CM
在分析DNA甲基化研究时，存在许多不同的方法来调整细胞类型混合比例的可变性。在这里，我们展示了一项广泛的模拟研究的结果，该研究基于来自Illumina Infinium 450K甲基化数据的细胞分离DNA甲基化谱，以比较八种方法的性能，包括最常用的方法。我们设计了一个丰富的多层模拟，其中包含一组探针，这些探针与二元或连续表型、细胞类型混淆、种群参数的均值和标准差变异性、个体细胞类型特异性样本水平上的额外变异性以及样本混合比例的变异性具有真正的关联。不同方法和模拟的性能差异很大。特别是，误报的数量有时高得不切实际，这表明通过混淆区分真实信号和那些看起来很重要的信号的能力有限。因此，过滤探针的方法功率很差。所有测试探针的p值QQ图表明，调整并不总是改善分布。同样的方法被用于检查来自结直肠癌病例对照研究的吸烟和甲基化数据之间的关联，我们还探讨了细胞类型调整对类风湿关节炎病例和对照组之间关联的影响。我们建议对细胞类型混合调整进行替代变量分析，因为在我们所有的模拟场景下，性能都是稳定的。本文的在线版本（doi:10.1186/s13059-016-0935-y）包含补充材料，仅供授权用户使用。
Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches. We designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameters, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the number of false positives was sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ plots of p values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case–control study of colorectal cancer, and we also explored the effect of cell-type adjustments on associations between rheumatoid arthritis cases and controls. We recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios. The online version of this article (doi:10.1186/s13059-016-0935-y) contains supplementary material, which is available to authorized users.