相依条件下高维数据协变量选择的统计方法。遗传图谱分类在肿瘤学中的应用
A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology
摘要：
我们提出了一种新的方法来选择和排序与一个变量的利益相关的高维数据的背景下，依赖，但很少的意见。该方法先后交织的协变量的聚类，协变量的去相关因子潜在分析，选择使用自适应方法的聚合，最后排名。仿真研究表明，利益的去相关内的不同集群的协变量。我们首先将我们的方法应用于37例接受化疗的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局的转录组协变量。其次，我们将我们的方法应用于79个乳腺肿瘤样本，以定义新的转移性生物标志物和相关基因网络的患者特征，以便个性化治疗。
英文摘要：
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. Simulations 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.
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