We consider the assessment of DNA methylation profiles for sequencing-derived data from a single cell type or from cell lines. We derive a kernel smoothed EM-algorithm, capable of analyzing an entire chromosome at once, and to simultaneously correct for experimental errors arising from either the pre-treatment steps or from the sequencing stage and to take into account spatial correlations between DNA methylation profiles at neighbouring CpG sites. The outcomes of our algorithm are then used to (i) call the true methylation status at each CpG site, (ii) provide accurate smoothed estimates of DNA methylation levels, and (iii) detect differentially methylated regions. Simulations show that the proposed methodology outperforms existing analysis methods that either ignore the correlation between DNA methylation profiles at neighbouring CpG sites or do not correct for errors. The use of the proposed inference procedure is illustrated through the analysis of a publicly available data set from a cell line of induced pluripotent H9 human embryonic stem cells and also a data set where methylation measures were obtained for a small genomic region in three different immune cell types separated from whole blood.

中文翻译：

---

用于细胞系或单一细胞类型中基于测序的方法的 DNA 甲基化谱的平滑 EM 算法

我们考虑评估来自单一细胞类型或细胞系的测序衍生数据的 DNA 甲基化谱。我们推导出一个核平滑 EM 算法，能够一次分析整个染色体，同时纠正由预处理步骤或测序阶段引起的实验误差，并考虑到 DNA 甲基化谱之间的空间相关性。邻近的 CpG 站点。然后我们算法的结果用于（i）调用每个 CpG 位点的真实甲基化状态，（ii）提供 DNA 甲基化水平的准确平滑估计，以及（iii）检测差异甲基化区域。模拟表明，所提出的方法优于现有的分析方法，这些方法要么忽略相邻 CpG 位点的 DNA 甲基化谱之间的相关性，要么不纠正错误。通过分析来自诱导多能 H9 人类胚胎干细胞的细胞系的公开可用数据集以及在三个不同免疫细胞中为小基因组区域获得甲基化测量的数据集，说明了所提出的推理程序的使用从全血中分离出的类型。