记忆性CD8 T细胞分化的统计分析：分层状态空间模型在短时程微阵列实验中的应用
A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment
摘要：
CD8 T细胞是特化的免疫细胞，在抗病毒免疫应答的调节和保护性免疫的产生中发挥重要作用。在本文中，我们研究了记忆性CD8 T细胞的分化在免疫应答中使用短时程芯片实验。在结构上，这个实验与许多实验相似，因为它涉及在一个生物群体中的独立样本上进行的测量，在少数不规则间隔的时间点，并表现出时间非平稳性的模式。为了分析这个CD8 T细胞实验，我们开发了一个分层状态空间模型，这样我们就可以：（1）检测时间差异表达的基因，（2）识别随时间推移的连续变化的方向，以及（3）评估随时间推移的连续变化的幅度。我们将隐马尔可夫模型到我们的模型，利用嵌入在时间序列的信息，并建立了建议的层次状态空间模型的经验贝叶斯框架，利用人口信息的大规模数据。使用所提出的模型对CD8 T细胞实验进行分析，得到了具有生物学意义的结果。分别总结了记忆CD8 T细胞分化中所涉及的时间模式，并在模拟研究中说明了所提出的模型的性能。
英文摘要：
CD8 T cells are specialized immune cells that play an important role in the regulation of antiviral immune response and the generation of protective immunity. In this paper we investigate the differentiation of memory CD8 T cells in the immune response using a short time course microarray experiment. Structurally, this experiment is similar to many in that it involves measurements taken on independent samples, in one biological group, at a small number of irregularly spaced time points, and exhibiting patterns of temporal nonstationarity. To analyze this CD8 T-cell experiment, we develop a hierarchical state space model so that we can: (1) detect temporally differentially expressed genes, (2) identify the direction of successive changes over time, and (3) assess the magnitude of successive changes over time. We incorporate hidden Markov models into our model to utilize the information embedded in the time series and set up the proposed hierarchical state space model in an empirical Bayes framework to utilize the population information from the large-scale data. Analysis of the CD8 T-cell experiment using the proposed model results in biologically meaningful findings. Temporal patterns involved in the differentiation of memory CD8 T cells are summarized separately and performance of the proposed model is illustrated in a simulation study.
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