基于基因组学和大量中间表型(如成像)之间的统计相关关联的综合分析提供了对其在疾病机制方面的临床相关性的重要见解。但是,除非推理能够准确地选择这些关联,否则对所得综合模型中不确定性的估计是不可靠的。在本文中,我们开发了选择感知贝叶斯方法,其中(1)通过一类灵活的综合贝叶斯模型中的“选择感知后验”来抵消模型选择偏差的影响,该模型通过1正则化算法选择了有希望的变量;(2)当相同的数据集用于选择和不确定性估计时,在模型选择的质量和推理能力之间进行不可避免的权衡。我们方法论发展的核心,当使用基于梯度的马尔可夫链蒙特卡罗(MCMC)采样来估计选择感知后验的不确定性时,精心构建的条件似然函数使用重新参数化映射提供了易于处理的更新。将我们的方法应用于放射性基因组分析,我们成功恢复了几个重要的基因通路,并估计了它们与患者生存时间之间的不确定性.
Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates for uncertainty in the resulting integrative models are however unreliable unless inference accounts for the selection of these associations with accuracy. In this paper, we develop selection-aware Bayesian methods, which (1) counteract the impact of model selection bias through a \"selection-aware posterior\" in a flexible class of integrative Bayesian models post a selection of promising variables via ℓ1 -regularized algorithms; (2) strike an inevitable trade-off between the quality of model selection and inferential power when the same data set is used for both selection and uncertainty estimation. Central to our methodological development, a carefully constructed conditional likelihood function deployed with a reparameterization mapping provides tractable updates when gradient-based Markov chain Monte Carlo (MCMC) sampling is used for estimating uncertainties from the selection-aware posterior. Applying our methods to a radiogenomic analysis, we successfully recover several important gene pathways and estimate uncertainties for their associations with patient survival times.