Ghosh Riddhi Pratim, Maity Arnab K, Pourahmadi Mohsen, Mallick Bani K
Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, Ohio, USA.
Early Clinical Development Oncology Statistics, San Diego, California, USA.
Genet Epidemiol. 2023 Feb;47(1):95-104. doi: 10.1002/gepi.22507. Epub 2022 Nov 15.
The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.
蛋白质的聚类在癌细胞生物学中备受关注。本文提出了一种基于相关结构的蛋白质（变量）聚类层次贝叶斯模型。从多元正态似然出发，我们通过对相关性进行基于角度的无约束重新参数化的先验建模来实施聚类，并假设截断泊松分布（以惩罚大量聚类）作为聚类数量的先验。参数的后验分布没有显式形式，我们使用基于可逆跳跃马尔可夫链蒙特卡罗的技术从后验中模拟参数。所提出方法的最终产物是蛋白质（变量）的估计聚类配置以及聚类数量。贝叶斯方法足够灵活，既可以对蛋白质进行聚类，也可以估计聚类数量。通过广泛的模拟研究以及一个具有乳腺癌遗传倾向的蛋白质表达数据（其中蛋白质来自不同途径），证实了所提出方法的性能。
