Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University and Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21231, USA.
Biostatistics. 2011 Apr;12(2):197-210. doi: 10.1093/biostatistics/kxq055. Epub 2010 Sep 21.
DNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in their infancy. Methylation sensitive restriction enzymes and microarrays provide a potential high-throughput, low-cost platform to allow methylation profiling. However, accurate absolute methylation estimates have been elusive due to systematic errors and unwanted variability. Previous microarray preprocessing procedures, mostly developed for expression arrays, fail to adequately normalize methylation-related data since they rely on key assumptions that are violated in the case of DNA methylation. We develop a normalization strategy tailored to DNA methylation data and an empirical Bayes percentage methylation estimator that together yield accurate absolute methylation estimates that can be compared across samples. We illustrate the method on data generated to detect methylation differences between tissues and between normal and tumor colon samples.
DNA 甲基化是许多正常和异常生物过程中基因功能的关键调节剂，但阐明其在全基因组范围内作用的工具仍处于起步阶段。甲基化敏感的限制性内切酶和微阵列为进行甲基化分析提供了一个潜在的高通量、低成本平台。然而，由于系统误差和不必要的可变性，准确的绝对甲基化估计仍然难以实现。先前的微阵列预处理程序主要是为表达阵列开发的，由于它们依赖于在 DNA 甲基化情况下被违反的关键假设，因此无法充分规范与甲基化相关的数据。我们开发了一种专门针对 DNA 甲基化数据的归一化策略和一个经验贝叶斯百分比甲基化估计器，它们共同产生可在样本之间进行比较的准确的绝对甲基化估计值。我们在为检测组织之间以及正常和肿瘤结肠样本之间的甲基化差异而生成的数据上说明了该方法。
