A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves
A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves
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DOI：
10.1198/016214505000000187
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发表时间：
2006-03-01
影响因子：
3.7
通讯作者：
Stephens, DA
中科院分区：
文献类型：
--
作者：
Heard, NA;Holmes, CC;Stephens, DA
疟疾是全球公共卫生面临的主要挑战之一。最近的一个突破，在研究的疾病如下注释的基因组的疟疾寄生虫恶性疟原虫和蚊子载体（一种有机体，传播传染病）按蚊。特别令人感兴趣的是按蚊免疫反应系统的分子生物学基础，该系统积极对抗疟原虫感染。本文报道了一个统计分析的基因表达时间分布从蚊子已感染的细菌剂。具体而言，我们引入了一个基于贝叶斯模型的层次聚类算法的曲线数据，以调查有关基因的调控机制，也就是说，我们的目标是聚类基因具有相似的表达谱。然后，显示相似的、有趣的图谱的基因可以被突出显示，以供实验者进一步研究。我们展示了我们的方法如何揭示其他方法无法捕获的数据结构。数据最相关的特征之一是样本量，它记录了2，771个基因在6个时间点的表达水平。此外，时间点的间隔不均匀，并且在基因谱中存在预期的非平稳行为。我们证明了我们的方法在这些条件下是容易实现的，并强调了一些关键的计算节省，可以在一个完整的贝叶斯分析的背景下。
Malaria represents one of the major worldwide challenges to public health. A recent breakthrough in the study of the disease follows the annotation of the genome of the malaria parasite Plasmodium falciparum and the mosquito vector (an organism that spreads an infectious disease) Anopheles. Of particular interest is the molecular biology underlying the immune response system of Anopheles, which actively fights against Plasmodium infection. This article reports a statistical analysis of gene expression time profiles from mosquitoes that have been infected with a bacterial agent. Specifically, we introduce a Bayesian model-based hierarchical clustering algorithm for curve data to investigate mechanisms of regulation in the genes concerned; that is, we aim to cluster genes having similar expression profiles. Genes displaying similar, interesting profiles can then be highlighted for further investigation by the experimenter. We show how our approach reveals structure within the data not captured by other approaches. One of the most pertinent features of the data is the sample size, which records the expression levels of 2,771 genes at 6 time points. Additionally, the time points are unequally spaced, and there is expected nonstationary behavior in the gene profiles. We demonstrate our approach to be readily implementable under these conditions, and highlight some crucial computational savings that can be made in the context of a fully Bayesian analysis.