Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, Minas Gerais 36570-000, Brasil.
Bioinformatics. 2012 Aug 1;28(15):2004-7. doi: 10.1093/bioinformatics/bts322. Epub 2012 Jun 4.
In a microarray time series analysis, due to the large number of genes evaluated, the first step toward understanding the complex time network is the clustering of genes that share similar expression patterns over time. Up until now, the proposed methods do not point simultaneously to the temporal autocorrelation of the gene expression and the model-based clustering. We present a Bayesian method that considers jointly the fit of autoregressive panel data models and hierarchical gene clustering.
The proposed methodology was able to cluster genes that share similar expression over time, which was determined jointly by the estimates of autoregression parameters, by the average level of expression) and by the quality of the fitted model.
The R codes for implementation of the proposed clustering method and for simulation study, as well as the real and simulated datasets, are freely accessible on the Web http://www.det.ufv.br/~moyses/links.php.
在微阵列时间序列分析中，由于评估的基因数量众多，理解复杂时间网络的第一步是对随时间具有相似表达模式的基因进行聚类。到目前为止，所提出的方法并没有同时指向基因表达的时间自相关性和基于模型的聚类。我们提出了一种贝叶斯方法，该方法同时考虑了自回归面板数据模型和层次基因聚类的拟合。
所提出的方法能够对随时间具有相似表达模式的基因进行聚类，这是由自回归参数的估计、平均表达水平和拟合模型的质量共同决定的。
用于实现所提出的聚类方法以及模拟研究的 R 代码，以及真实和模拟数据集，可在 Web 上免费访问 http://www.det.ufv.br/~moyses/links.php。
