The LZIP: A Bayesian latent factor model for correlated zero‐inflated counts
The LZIP: A Bayesian latent factor model for correlated zero‐inflated counts
复制标题
LZIP：相关零膨胀计数的贝叶斯潜在因子模型
作者：
B. Neelon;Dongjun Chung
受乳腺癌患者分子差异研究的启发，我们开发了贝叶斯潜在因子零膨胀泊松（LZIP）模型，用于分析相关零膨胀计数。以一组受试者特异性潜在因素为条件，将响应建模为独立零膨胀泊松分布。对于每个结果，我们将LZIP模型表示为两个离散随机变量的函数：第一个捕获处于潜在“风险”状态的倾向，而第二个表示处于风险状态的计数响应条件。潜在的因素和负荷分配有条件的共轭伽玛先验，以适应过度分散和依赖的结果。对于后验计算，我们提出了一种有效的数据增强算法，主要依赖于容易采样的吉布斯步骤。我们进行模拟研究，调查模型的推理性能和所提出的采样算法的计算能力。我们将该方法应用于癌症基因组图谱中乳腺癌基因组学数据的分析。
Motivated by a study of molecular differences among breast cancer patients, we develop a Bayesian latent factor zero‐inflated Poisson (LZIP) model for the analysis of correlated zero‐inflated counts. The responses are modeled as independent zero‐inflated Poisson distributions conditional on a set of subject‐specific latent factors. For each outcome, we express the LZIP model as a function of two discrete random variables: the first captures the propensity to be in an underlying “at‐risk” state, while the second represents the count response conditional on being at risk. The latent factors and loadings are assigned conditionally conjugate gamma priors that accommodate overdispersion and dependence among the outcomes. For posterior computation, we propose an efficient data‐augmentation algorithm that relies primarily on easily sampled Gibbs steps. We conduct simulation studies to investigate both the inferential properties of the model and the computational capabilities of the proposed sampling algorithm. We apply the method to an analysis of breast cancer genomics data from The Cancer Genome Atlas.
影响因子：
45.3
作者：
Parker, Joel S.;Mullins, Michael;Bernard, Philip S.
通讯作者：
Bernard, Philip S.