Infinium 甲基化 BeadChip 阵列仍然是表观基因组范围关联研究最受欢迎的平台之一，但下游通路分析工具有其局限性。功能类别评分 (FCS) 是一组通路富集技术，涉及基因排序和评估其在生物系统中的集体调控，但 Infinium 甲基化阵列数据描述的实现不保留方向信息，这对于机制理解很重要的基因组调控。在这里，我们评估了几种保留方向信息的候选 FCS 方法。根据模拟结果，性能最佳的方法涉及按基因对探针 limma t 统计量进行平均聚合，然后使用 mitch 包进行排名方差分析富集测试。这种方法，我们称之为“LAM”，在模拟中优于现有的过度表征分析方法，并且在真实肺肿瘤-正常配对数据集的分析中表现出更高的灵敏度和鲁棒性。使用匹配的 RNA-seq 数据，我们检查了启动子和基因体的甲基化差异与肺癌通路水平的 RNA 表达的关系。为了证明我们的方法的实用性，我们将其应用于可获得公共数据的其他三个环境。首先，我们检查与实际年龄相关的差异途径甲基化。其次，我们研究了体外受精婴儿的通路甲基化差异。最后，我们分析了 19 种疾病状态下的差异通路甲基化，确定了数百种新的关联。这些结果表明 LAM 是一种检测差异途径甲基化的强大方法，是对现有方法的补充。提供了可重现的小插图来说明如何实现此方法。

Infinium Methylation BeadChip arrays remain one of the most popular platforms for epigenome-wide association studies, but tools for downstream pathway analysis have their limitations. Functional class scoring (FCS) is a group of pathway enrichment techniques that involve the ranking of genes and evaluation of their collective regulation in biological systems, but the implementations described for Infinium methylation array data do not retain direction information, which is important for mechanistic understanding of genomic regulation. Here, we evaluate several candidate FCS methods that retain directional information. According to simulation results, the best-performing method involves the mean aggregation of probe limma t-statistics by gene followed by a rank-ANOVA enrichment test using the mitch package. This method, which we call 'LAM,' outperformed an existing over-representation analysis method in simulations, and showed higher sensitivity and robustness in an analysis of real lung tumour-normal paired datasets. Using matched RNA-seq data, we examine the relationship of methylation differences at promoters and gene bodies with RNA expression at the level of pathways in lung cancer. To demonstrate the utility of our approach, we apply it to three other contexts where public data were available. First, we examine the differential pathway methylation associated with chronological age. Second, we investigate pathway methylation differences in infants conceived with in vitro fertilization. Lastly, we analyse differential pathway methylation in 19 disease states, identifying hundreds of novel associations. These results show LAM is a powerful method for the detection of differential pathway methylation complementing existing methods. A reproducible vignette is provided to illustrate how to implement this method.