Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA.
BMC Bioinformatics. 2009 Dec 9;10:404. doi: 10.1186/1471-2105-10-404.
DNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.
We propose a new method for identifying differentially methylated (DM) genes by identifying the associated DM CGI(s). At each CGI, we implement four different mixed effect and generalized least square models to identify DM genes between two groups. We compare four models with a simple least square regression model to study the impact of incorporating random effects and correlations.
We demonstrate that the inclusion (or exclusion) of random effects and the choice of correlation structures can significantly affect the results of the data analysis. We also assess the false discovery rate of different models using CGIs associated with housekeeping genes.
DNA 甲基化在肿瘤发生过程中起着重要作用。因此，鉴定与两种肿瘤亚型之间的基因相关的差异甲基化基因或 CpG 岛（CGI）是一个重要的生物学问题。全基因组中的所有 CGI 的甲基化状态都可以通过差异甲基化杂交（DMH）微阵列进行检测。然而，患者样本或细胞系是异质的，因此它们的甲基化模式可能非常不同。此外，每个 CGI 附近的探针是相关的。这些因素如何影响 DMH 数据的分析尚不清楚。
我们提出了一种通过鉴定相关的 DM CGI（s）来识别差异甲基化（DM）基因的新方法。在每个 CGI 上，我们实现了四个不同的混合效应和广义最小二乘模型，以鉴定两组之间的 DM 基因。我们将四个模型与简单的最小二乘回归模型进行比较，以研究纳入随机效应和相关性的影响。
我们证明了随机效应的纳入（或排除）以及相关结构的选择会显著影响数据分析的结果。我们还使用与管家基因相关的 CGIs 评估了不同模型的假发现率。
