应用贝叶斯荟萃分析识别裂殖酵母细胞周期性表达基因
Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle
摘要：
从全基因组微阵列时间序列数据中识别细胞周期中具有周期性表达的基因的努力已经持续了十年。然而，缺乏严格的建模的周期性表达，以及缺乏一个全面的模型，整合信息的基因和实验，削弱了努力的准确识别周期性表达的基因。为了解决这个问题，我们引入贝叶斯模型来整合多个独立的微阵列数据集，从三个最近的全基因组细胞周期研究裂变酵母。数据集成采用层次模型。为了便于从联合后验分布中进行有效的蒙特卡罗抽样，我们提出了一种新的Metropolis-Hastings群移动。从我们的综合分析中，一个令人惊讶的发现是，在裂殖酵母中超过40%的基因是显著周期性表达的，大大增强了目前文献中报道的10- 15%的基因。它要求重新考虑周期性表达的基因检测问题。
英文摘要：
The effort to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data has been ongoing for a decade. However, the lack of rigorous modeling of periodic expression as well as the lack of a comprehensive model for integrating information across genes and experiments has impaired the effort for the accurate identification of periodically expressed genes. To address the problem, we introduce a Bayesian model to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis--Hastings group move. A surprising finding from our integrated analysis is that more than 40% of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported 10--15% of the genes in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem.
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