Bayesian regularization for a nonstationary Gaussian linear mixed effects model.
Bayesian regularization for a nonstationary Gaussian linear mixed effects model.
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DOI：
10.1002/sim.9279
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发表时间：
2022-02-20
影响因子：
2
通讯作者：
Szczesniak RD
中科院分区：
文献类型：
--
作者：
Gecili E;Sivaganesan S;Asar O;Clancy JP;Ziady A;Szczesniak RD
在组学实验中，估计和变量选择可能涉及从相对少量的受试者中观察到的数千种蛋白质/基因。许多回归正则化程序已经开发出估计和变量选择在这样的高维问题。然而，方法主要集中在线性回归模型，忽略了相关性所产生的长期序列的重复测量的结果。我们的工作的动机是需要确定蛋白质组学生物标志物，以改善囊性纤维化（CF）肺病患者肺功能快速下降的预测。我们扩展了四种贝叶斯惩罚回归方法，用于具有非平稳协方差结构的高斯线性混合效应模型，以考虑纵向肺功能数据的复杂结构，同时估计未知参数并选择重要的蛋白质异构体以提高预测性能。不同类型的收缩先验进行评估，以诱导变量选择在一个完全贝叶斯框架。仿真研究的方法。我们将所提出的方法应用于我们激励CF研究的真实的蛋白质组学和肺功能结果数据，确定了一组相关的临床/人口统计学预测因子和肺功能快速下降的蛋白质组学生物标志物。我们还说明了CD 4酵母细胞周期基因组数据的方法，确认所提出的方法识别的转录因子，已在文献中强调其重要性作为细胞周期转录因子。
In omics experiments, estimation and variable selection can involve thousands of proteins/genes observed from a relatively small number of subjects. Many regression regularization procedures have been developed for estimation and variable selection in such high-dimensional problems. However, approaches have predominantly focused on linear regression models that ignore correlation arising from long sequences of repeated measurements on the outcome. Our work is motivated by the need to identify proteomic biomarkers that improve the prediction of rapid lung-function decline for individuals with cystic fibrosis (CF) lung disease. We extend four Bayesian penalized regression approaches for a Gaussian linear mixed effects model with non-stationary covariance structure to account for the complicated structure of longitudinal lung function data while simultaneously estimating unknown parameters and selecting important protein isoforms to improve predictive performance. Different types of shrinkage priors are evaluated to induce variable selection in a fully Bayesian framework. The approaches are studied with simulations. We apply the proposed method to real proteomics and lung-function outcome data from our motivating CF study, identifying a set of relevant clinical/demographic predictors and a proteomic biomarker for rapid decline of lung function. We also illustrate the methods on CD4 yeast cell-cycle genomic data, confirming that the proposed method identifies transcription factors that have been highlighted in the literature for their importance as cell cycle transcription factors.
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影响因子：
1.8
作者：
Stingo, Francesco C.;Chen, Yian A.;Vannucci, Marina
通讯作者：
Vannucci, Marina
影响因子：
3.3
作者：
Spellman, PT;Sherlock, G;Futcher, B
通讯作者：
Futcher, B
影响因子：
2
作者：
Szczesniak, Rhonda D.;Su, Weiji;Clancy, John P.
通讯作者：
Clancy, John P.
影响因子：
1.9
作者：
LAIRD, NM;WARE, JH
通讯作者：
WARE, JH
DOI：
10.1198/016214501753382273
发表时间：
2001-12-01
影响因子：
3.7
作者：
Fan, JQ;Li, RZ
通讯作者：
Li, RZ