Hsu Jessie J, Finkelstein Dianne M, Schoenfeld David A
Genentech, San Francisco, CA, USA.
Massachusetts General Hospital and Harvard University, Boston MA, USA.
Commun Stat Case Stud Data Anal Appl. 2018;4(1):18-27. doi: 10.1080/23737484.2018.1455542. Epub 2018 Apr 9.
The goal of this research is to discover what groups of genes are associated with the disease process. We use binary and failure time outcomes to inform the clustering of longitudinally-collected microarray data. We propose a linear model with normally distributed cluster-specific random effects for the longitudinal gene expression trajectory. The random effects are linearly related to a latent continuous representation of the outcome, where the probability or hazard of the outcome depends on these latent variables. We apply our method to microarray data collected from trauma patients in the Inflammation and Host Response to Injury project.
本研究的目标是发现哪些基因群体与疾病进程相关。我们使用二元和生存时间结果来指导对纵向收集的微阵列数据进行聚类。我们针对纵向基因表达轨迹提出了一个具有正态分布的聚类特定随机效应的线性模型。随机效应与结果的潜在连续表示线性相关，其中结果的概率或风险取决于这些潜在变量。我们将我们的方法应用于从炎症与宿主对损伤反应项目中的创伤患者收集的微阵列数据。
