Dimension reduction methods for microarrays with application to censored survival data
Dimension reduction methods for microarrays with application to censored survival data
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DOI：
10.1093/bioinformatics/bth415
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发表时间：
2004-12-12
期刊：
影响因子：
5.8
通讯作者：
Li, HZ
中科院分区：
文献类型：
--
作者：
Li, LX;Li, HZ
动机：最近的研究表明，基因表达谱可以潜在地用于预测各种临床表型，如肿瘤类型、药物反应和生存时间。虽然对肿瘤分类进行了广泛的研究，但对其他表型特征的重视程度较低，特别是患者生存时间或癌症复发时间，这些都受到右删失的影响。我们认为在本文中的分析删失生存时间的基础上的微阵列基因expression profiles.Results：我们提出了一种降维策略，它结合了主成分分析和切片逆回归，以确定基因的线性组合，既占基因表达水平的变异性和保留的表型信息。然后将提取的基因组合用作预测存活模型制剂中的协变量。我们将所提出的方法应用于一个大型弥漫性大B细胞淋巴瘤数据集，其中包括240例患者和7399个基因，并建立一个考克斯比例风险模型的基础上派生的基因表达组件。所提出的方法被证明是一个很好的预测性能为患者的生存，所证明的两个显着的生存差异之间的预测风险组和接收器操作员的特征分析。
Motivation: Recent research has shown that gene expression profiles can potentially be used for predicting various clinical phenotypes, such as tumor class, drug response and survival time. While there has been extensive studies on tumor classification, there has been less emphasis on other phenotypic features, in particular, patient survival time or time to cancer recurrence, which are subject to right censoring. We consider in this paper an analysis of censored survival time based on microarray gene expression profiles.Results: We propose a dimension reduction strategy, which combines principal components analysis and sliced inverse regression, to identify linear combinations of genes, that both account for the variability in the gene expression levels and preserve the phenotypic information. The extracted gene combinations are then employed as covariates in a predictive survival model formulation. We apply the proposed method to a large diffuse large-B-cell lymphoma dataset, which consists of 240 patients and 7399 genes, and build a Cox proportional hazards model based on the derived gene expression components. The proposed method is shown to provide a good predictive performance for patient survival, as demonstrated by both the significant survival difference between the predicted risk groups and the receiver operator characteristics analysis.