分离多个混合类型数据集中的共同（全局和局部）和不同变化
Separating common (global and local) and distinct variation in multiple mixed types data sets
摘要：
从不同平台获得的相同对象的多组测量可以部分地反映所研究系统的互补信息。对这些数据集的综合分析不仅为我们提供了更深入了解所研究系统的机会，而且也带来了一些新的统计挑战。首先，将所有或部分数据集共有的信息与每个数据集特有的信息分开是有问题的。此外，这些数据集通常是定量和离散（二进制或分类）数据类型的混合，而常用的数据融合方法要求所有数据集都是定量的。本文提出了一种指数族同时成分分析（ESCA）模型来解决多数据集潜在的混合数据类型问题。此外，通过一个近似无偏的组凹罚函数，构造了一个负载矩阵的结构化稀疏模式，以分离多个数据集的全局、局部公共和不同信息.一个基于优化-最小化算法推导出适合所提出的模型。该算法在每次迭代中更新模型的所有参数，并使目标函数单调递减。对于模型选择，实施基于缺失值的交叉验证程序。与其他方法相比，所提出的方法的优点进行评估，使用全面的模拟，以及从慢性淋巴细胞白血病（CLL）研究的真实数据的分析。可用性：可在https://gitlab.com/uvabda上获得用于复制本文中结果的代码。
英文摘要：
Multiple sets of measurements on the same objects obtained from different platforms may reflect partially complementary information of the studied system. The integrative analysis of such data sets not only provides us with the opportunity of a deeper understanding of the studied system, but also introduces some new statistical challenges. First, the separation of information that is common across all or some of the data sets, and the information that is specific to each data set is problematic. Furthermore, these data sets are often a mix of quantitative and discrete (binary or categorical) data types, while commonly used data fusion methods require all data sets to be quantitative. In this paper, we propose an exponential family simultaneous component analysis (ESCA) model to tackle the potential mixed data types problem of multiple data sets. In addition, a structured sparse pattern of the loading matrix is induced through a nearly unbiased group concave penalty to disentangle the global, local common and distinct information of the multiple data sets. A Majorization-Minimization based algorithm is derived to fit the proposed model. Analytic solutions are derived for updating all the parameters of the model in each iteration, and the algorithm will decrease the objective function in each iteration monotonically. For model selection, a missing value based cross validation procedure is implemented. The advantages of the proposed method in comparison with other approaches are assessed using comprehensive simulations as well as the analysis of real data from a chronic lymphocytic leukaemia (CLL) study. Availability: the codes to reproduce the results in this article are available at https://gitlab.com/uvabda.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
