GEE-TGDR：一种纵向特征选择算法及其在免疫治疗银屑病患者lncRNA表达谱中的应用
GEE-TGDR: A longitudinal feature selection algorithm and its application to lncRNA expression profiles for psoriasis patients treated with immune therapies
摘要：
随着高通量技术的快速发展，纵向基因表达实验在生物医学领域变得越来越普遍。广义估计方程（GEE）方法是一种广泛应用于纵向数据分析的统计方法。在纵向组学数据分析中，特征选择是必不可少的。在现有的各种特征选择方法中，一种嵌入式方法，即阈值梯度下降正则化（TGDR），由于其优良的特性而脱颖而出。将全球平等与发展战略相结合是一个很有前途的领域，可以确定相关的标志，解释随着时间的推移结果的动态变化。在这项研究中，我们提出了一种新的纵向结果的特征选择算法：GEE-TGDR。在GEE-TGDR方法中，GEE模型对应的拟似然函数是待优化的目标函数，通过TGDR方法完成优化和特征选择。我们应用GEE-TGDR方法纵向lncRNA基因表达数据集，检查银屑病患者对免疫治疗的治疗反应。在不同的工作相关性结构下，鉴定了包括10个相关lncRNA的列表，预测准确率为80%，并具有有意义的生物学解释。最后，广泛应用的建议GEE-TGDR方法在组学数据分析。
英文摘要：
With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method, namely, threshold gradient descent regularization (TGDR) stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. In this study, we proposed a new novel feature selection algorithm for longitudinal outcomes:GEE-TGDR. In the GEE-TGDR method, the corresponding quasi-likelihood function of a GEE model is the objective function to be optimized and the optimization and feature selection are accomplished by the TGDR method. We applied the GEE-TGDR method a longitudinal lncRNA gene expression dataset that examined the treatment response of psoriasis patients to immune therapy. Under different working correlation structures, a list including 10 relevant lncRNAs were identified with a predictive accuracy of 80 % and meaningful biological interpretation. To conclude, a widespread application of the proposed GEE-TGDR method in omics data analysis is anticipated.
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