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A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2020-10-27 , DOI: 10.1080/02664763.2020.1837083
B Bastien 1 , T Boukhobza 2 , H Dumond 2 , A Gégout-Petit 3 , A Muller-Gueudin 3 , C Thiébaut 2
Affiliation  

ABSTRACT

We propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. A simulation study shows the interest of the decorrelation inside the different clusters of covariates. We first apply our method to transcriptomic data of 37 patients with advanced non-small-cell lung cancer who have received chemotherapy, to select the transcriptomic covariates that explain the survival outcome of the treatment. Secondly, we apply our method to 79 breast tumor samples to define patient profiles for a new metastatic biomarker and associated gene network in order to personalize the treatments.



中文翻译:

一种在依赖的高维数据中选择协变量的统计方法。在肿瘤学中遗传谱分类的应用

摘要

我们提出了一种新的方法,用于在依赖但很少观察的高维数据的上下文中选择和排序与感兴趣的变量相关的协变量。该方法依次将协变量的聚类、使用因子潜在分析的协变量去相关、使用自适应方法的聚合进行选择以及最后的排序交织在一起。一项模拟研究显示了不同协变量集群内的去相关性的兴趣。我们首先将我们的方法应用于接受化疗的 37 名晚期非小细胞肺癌患者的转录组数据,以选择解释治疗生存结果的转录组协变量。第二,

更新日期:2020-10-27
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