It is becoming increasingly common to study the complex association between multiple outcomes and high-dimensional predictors in biomedicine. However, the related multiple outcomes and related high-dimensional predictors require flexible and efficient joint statistical models. We propose a multivariate structured Bayesian variable selection model to identify sparse predictors associated with multiple outcomes. The approach uses a known structure prior between the multiple outcomes and predictors via a Markov random field (MRF) prior for the latent indicator variables of the coefficient matrix of a sparse seemingly unrelated regression. The prior knowledge in the MRF prior has the potential to generate new structure knowledge. In addition, we employ random effects to capture sample heterogeneity of grouped data. The proposed approach is validated by simulation studies and a large-scale pharmacogenomic study which includes a pharmacological profiling and multi-omics data from in vitro anti-cancer drug screening.
翻译:研究生物医学中多重结果和高维预测体之间的复杂联系越来越普遍,然而,相关的多重结果和相关的高维预测体需要灵活而高效的联合统计模型。我们提出了一个多变结构的贝叶斯变数选择模型,以确定与多重结果有关的稀有预测体。该方法在多种结果和预测体之间之前通过Markov随机字段(MRF)使用一个已知结构,用于稀疏似乎不相干回归的系数矩阵的潜在指标变量。以前在MRF中的知识有可能产生新的结构知识。此外,我们利用随机效应来捕捉组群数据的样本异性。拟议方法通过模拟研究和大规模药源学研究加以验证,其中包括一种药理特征分析以及来自体外抗癌药物筛查的多组性数据。