Partial least squares Cox regression for genome-wide data
Partial least squares Cox regression for genome-wide data
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DOI：
10.1007/s10985-007-9076-7
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发表时间：
2008-06-01
影响因子：
1.3
通讯作者：
Storvold, Hege Leite
中科院分区：
文献类型：
--
作者：
Nygard, Stale;Borgan, Ornulf;Storvold, Hege Leite
大多数基于高维基因组数据的生存预测方法将Cox比例风险模型与偏最小二乘回归（PLS）等降维技术相结合。将PLS应用于Cox模型并不完全简单，已经提出了多种方法。Park等人的方法（生物信息学18（增刊））。1):S120-S127, 2002)将Cox似然重新表述为泊松似然，从而可以通过迭代重新加权的偏最小二乘对广义线性模型进行估计。我们建议对park等人（2002）的方法进行修改，以便分步骤获得基线危害和基因效应的估计。与park等人（2002）的方法和其他现有的Cox PLS方法相比，该方法有几个优点，因为它允许估计新患者的生存概率，实现更少的记忆要求的估计过程，并允许纳入低维非基因组变量，如疾病等级和肿瘤厚度。我们还建议将我们的Cox PLS方法与初始基因选择步骤相结合，其中基因按其Cox评分排序，仅保留排名最高的k%的基因，从而获得所谓的监督偏最小二乘回归方法。在模拟中，无监督和有监督版本都优于其他Cox PLS方法。
Most methods for survival prediction from high-dimensional genomic data combine the Cox proportional hazards model with some technique of dimension reduction, such as partial least squares regression (PLS). Applying PLS to the Cox model is not entirely straightforward, and multiple approaches have been proposed. The method of Park et al. (Bioinformatics 18(Suppl. 1):S120-S127, 2002) uses a reformulation of the Cox likelihood to a Poisson type likelihood, thereby enabling estimation by iteratively reweighted partial least squares for generalized linear models. We propose a modification of the method of park et al. (2002) such that estimates of the baseline hazard and the gene effects are obtained in separate steps. The resulting method has several advantages over the method of park et al. (2002) and other existing Cox PLS approaches, as it allows for estimation of survival probabilities for new patients, enables a less memory-demanding estimation procedure, and allows for incorporation of lower-dimensional non-genomic variables like disease grade and tumor thickness. We also propose to combine our Cox PLS method with an initial gene selection step in which genes are ordered by their Cox score and only the highest-ranking k% of the genes are retained, obtaining a so-called supervised partial least squares regression method. In simulations, both the unsupervised and the supervised version outperform other Cox PLS methods.