Data from: Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies

Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment.基于一项大规模模拟研究，McGregor及其团队近期提出建议：在无法获取细胞类型比例的DNA甲基化（DNA methylation）关联研究中，可采用替代变量分析（surrogate variable analysis, SVA）控制细胞类型异质性带来的混杂效应。由于该建议主要基于模拟数据，本研究尝试在两项大规模实证研究中复现其结论。在我们的实证数据中，SVA无法完全校正细胞类型带来的效应，其性能表现存在一定程度的不稳定性，且存在因剔除可能与实际疾病进程相关的变异而丢失真实信号的风险。与之相比，基于参考甲基化组（reference methylomes）的校正方法表现优异，未出现上述局限性。该方法的一项缺陷在于：若参考甲基化组未公开可用，则需先针对少量样本生成对应的参考甲基化组。然而，鉴于我们观察到细胞类型混杂带来的显著风险，本研究认为，为避免将假阳性结果引入学术文献，投入该成本是十分值得的。