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 article, we develop selection-aware Bayesian methods which: (i) 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 $\ell_1$-regularized algorithms; (ii) strike an inevitable tradeoff between the quality of model selection and inferential power when the same dataset 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 notably tractable updates when gradient-based 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.
翻译:根据基因组学和大量的中间苯型(如成像)之间具有统计相关性的关联进行综合分析,从病理机制的临床相关性的角度,对由此产生的综合模型的不确定性的估计数至关重要。然而,除非推断精确地计算出这些协会的选择情况,否则由此产生的综合模型的不确定性的估计数是不可靠的。在本篇文章中,我们开发了有选择意识的贝叶斯人方法,这些方法:(一)通过在一种灵活的混合贝叶斯人模型类别中采用“有选择意识的子孙”来抵消模型选择偏差的影响,在选择有希望的变数后,通过$@ell_1$1美元正规化算法进行选择;(二)在选择和估算不确定性时,在模型选择质量和推断能力之间作出不可避免的权衡。对于我们的方法发展来说,一个经过仔细构建的有条件可能性功能,加上一个重新测量的绘图,在使用基于梯度的MCMC取样来评估选择有意识的后,提供了显著的易感性更新。我们将方法应用于放射性基因组分析,我们成功地恢复了几种重要的基因路径,并估计其与病人存活期的联系的不确定性。