Sparse group penalized integrative analysis of multiple cancer prognosis datasets.
Sparse group penalized integrative analysis of multiple cancer prognosis datasets.
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DOI：
10.1017/s0016672313000086
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发表时间：
2013-06
影响因子：
1.5
通讯作者：
Ma, Shuangge
中科院分区：
文献类型：
--
作者：
Liu, Jin;Huang, Jian;Xie, Yang;Ma, Shuangge
在癌症研究中，已经广泛进行了高通量分析研究，寻找与预后相关的标志物。由于“大d，小n”的特性，从单个数据集的分析生成的结果可能是不令人满意的。最近的研究表明，综合分析，同时分析多个数据集，可以比单数据集分析和经典的荟萃分析更有效。在现有的大多数综合分析中，均假设同质模型，假设不同的数据集共享相同的标记集。已经设计了几种方法来加强这一假设。在实践中，不同的数据集可能在患者选择标准、分析技术和许多其他方面有所不同。这种差异可能会使同质性模型受到太多限制。在这项研究中，我们假设的异质性模型，在该模型下，不同的数据集被允许有不同的标记集。对于多个癌症预后数据集，我们采用AFT（加速失败时间）模型来描述生存率。该模型可能是流行的半参数生存模型中计算成本最低的。对于标记选择，我们采用稀疏组MCP（最小最大凹罚）的方法。这种方法有一个直观的公式，可以使用一个有效的组坐标下降算法计算。仿真研究表明，该方法在同质和异质模型下均优于现有方法。数据分析进一步证明了异质性模型和所提出的方法的优点。
In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach.