基于回归模型的时间序列基因表达数据混合方法：初始化和随机效应评估。
Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects.
机构信息
Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, A-1040 Vienna, Austria.
出版信息
Bioinformatics. 2010 Feb 1;26(3):370-7. doi: 10.1093/bioinformatics/btp686. Epub 2009 Dec 29.
SUMMARY
Finite mixture models are routinely applied to time course microarray data. Due to the complexity and size of this type of data, the choice of good starting values plays an important role. So far initialization strategies have only been investigated for data from a mixture of multivariate normal distributions. In this work several initialization procedures are evaluated for mixtures of regression models with and without random effects in an extensive simulation study on different artificial datasets. Finally, these procedures are also applied to a real dataset from Escherichia coli.
AVAILABILITY
The latest release versions of R packages flexmix, gcExplorer and kernlab are always available from CRAN (http://cran.r-project.org/).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
有限混合模型通常应用于时间序列微阵列数据。由于这种类型数据的复杂性和规模，良好的起始值选择起着重要作用。到目前为止，初始化策略仅针对多元正态分布混合数据进行了研究。在这项工作中，在不同的人工数据集上进行的广泛模拟研究中，评估了具有和不具有随机效应的回归模型混合物的几种初始化程序。最后，这些程序也应用于来自大肠杆菌的真实数据集。
可用性
R 包 flexmix、gcExplorer 和 kernlab 的最新版本始终可从 CRAN（http://cran.r-project.org/）获得。
补充信息
补充数据可在 Bioinformatics 在线获得。
Zotero 插件