Aflakparast Mehran, de Gunst Mathisca C M, van Wieringen Wessel N
Department of Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands.
Department of Epidemiology and Biostatistics, VUmc University Medical Center, P. O. Box 7057, 1007 MB, Amsterdam, The Netherlands.
Biom J. 2018 May;60(3):547-563. doi: 10.1002/bimj.201700102. Epub 2018 Jan 10.
Cross-sectional studies may shed light on the evolution of a disease like cancer through the comparison of patient traits among disease stages. This problem is especially challenging when a gene-gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional data. These mixtures are fitted by a (fused) ridge penalized EM algorithm. The fused ridge penalty shrinks GGMs of contiguous stages. The (fused) ridge penalty parameters are chosen through cross-validation. The proposed estimation procedures are shown to be consistent and their performance in other respects is studied in simulation. The down-stream exploitation of the fitted GGMs is outlined. In a data illustration the methodology is employed to identify gene-gene interaction network changes in the transition from normal to cancer prostate tissue.
横断面研究可以通过比较疾病各阶段患者的特征，来揭示像癌症这样的疾病的演变。当需要从组学数据重建基因-基因相互作用网络时，这个问题尤其具有挑战性，此外，每个阶段的患者不一定构成一个同质群体。在这里，该问题被转化为从高维数据估计高斯图形模型（GGM）的阶段混合模型。这些混合模型通过（融合）岭罚EM算法进行拟合。融合岭罚使相邻阶段的GGM收缩。通过交叉验证选择（融合）岭罚参数。所提出的估计程序被证明是一致的，并在模拟中研究了它们在其他方面的性能。概述了拟合GGM的下游应用。在一个数据示例中，该方法被用于识别从正常前列腺组织到癌组织转变过程中的基因-基因相互作用网络变化。
