BACKGROUND Umbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age-appropriate cell-specific DNAm profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450 K and EPIC arrays. These datasets are biologically and technically different, and currently, there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution. RESULTS We first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n = 191) with matched Fluorescence-Activated Cell Sorting measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes. CONCLUSION These results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

中文翻译：

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对脐带血 DNA 甲基化数据去卷积的参考和文库选择方法进行系统评估和验证。


背景脐带血（UCB）通常用于产前暴露的表观基因组范围关联研究。在此类研究中，考虑细胞类型组成至关重要，因为它可以减少由于 DNA 甲基化 (DNAm) 的细胞特异性而产生的混杂因素。在缺乏细胞分选信息的情况下，可以应用统计方法对异质细胞混合物进行去卷积。在这些方法中，基于参考的方法利用适合年龄的细胞特异性 DNAm 谱来估计细胞组成。在 UCB 中，已使用 Illumina 450 K 和 EPIC 阵列发布了四个参考数据集，其中包含在纯化细胞群中分析的 DNAm 特征。这些数据集在生物学和技术上都是不同的，目前，对于如何最好地应用它们还没有达成共识。在这里，我们系统地评估和比较这些数据集，并为基于参考的 UCB 反卷积提供建议。结果我们首先评估了四个参考数据集，以确定样品的纯度和潜在的细胞交叉污染。我们过滤样本并组合数据集以获得联合 UCB 参考。我们使用两种不同的方法选择反卷积文库：使用 minfi 中 pickCompProbes 函数中的顶级差异甲基化探针自动选择，以及使用 IDOL（识别最佳文库）迭代算法选择标准化文库。我们使用两种参考文库选择方法分别和组合比较了每个参考的性能，并使用匹配的荧光激活细胞分选测量的细胞计数验证了独立队列（R 代研究，n = 191）中的结果。 严格的筛选和参考文献的组合显着提高了细胞类型估计的准确性和效率。最终，IDOL 库的性能优于 pickCompProbes 中实现的自动选择方法的库。结论 这些结果对于 UCB 的表观遗传学研究具有重要意义，因为实施这种方法将最大程度地减少由于细胞异质性引起的混杂因素。这项工作为未来基于参考的 UCB 反卷积提供了指导，并建立了一个用于组合其他组织中的参考数据集的框架。