DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

中文翻译：

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EBADIMEX：一种经验贝叶斯方法，用于检测关节的差异表达和甲基化并对样品进行分类。

DNA甲基化和基因表达是相互依赖的，并且都与癌症的发生和发展有关，并发现了许多单独的生物标记。两种数据类型的联合分析可能会导致无法通过单独的分析发现的生物学见解。为了最佳地利用联合数据来识别受干扰的基因和对临床癌症样本进行分类，准确建模两种数据类型之间的相互作用非常重要。在这里，我们介绍了EBADIMEX，用于共同识别差异表达和甲基化并对样品进行分类。在当前的差异表达方法中，与经验贝叶斯先验一起广泛使用的温和t检验通过开发以下方法而推广到多变量设置：（1）均等Welch t检验用于均等相等且方差不相等；（2）进行方差均等的温和F检验；（3）方差相等的均值均值的多元检验。这导致参数模型具有参数的先验分布，从而可以快速评估和对小数据集进行可靠的分析。EBADIMEX在模拟数据以及TCGA的大型乳腺癌（BRCA）研究中得到了证明。我们证明，经验性贝叶斯先验和温和检验的使用在小数据集上效果特别好。