Wang Lily, Chen Xi, Wolfinger Russell D, Franklin Jeffrey L, Coffey Robert J, Zhang Bing
Vanderbilt University, USA.
Stat Appl Genet Mol Biol. 2009;8(1):Article 47. doi: 10.2202/1544-6115.1484. Epub 2009 Nov 7.
Methods for gene set analysis test for coordinated changes of a group of genes involved in the same biological process or molecular pathway. Higher statistical power is gained for gene set analysis by combining weak signals from a number of individual genes in each group. Although many gene set analysis methods have been proposed for microarray experiments with two groups, few can be applied to time course experiments. We propose a unified statistical model for analyzing time course experiments at the gene set level using random coefficient models, which fall into the more general class of mixed effects models. These models include a systematic component that models the mean trajectory for the group of genes, and a random component (the random coefficients) that models how each gene's trajectory varies about the mean trajectory. We show that the proposed model (1) outperforms currently available methods at discriminating gene sets differentially changed over time from null gene sets; (2) provides more stable results that are less affected by sampling variations; (3) models dependency among genes adequately and preserves type I error rate; and (4) allows for gene ranking based on predicted values of the random effects. We describe simulation studies using gene expression data with "real life" correlations and we demonstrate the proposed random coefficient model using a mouse colon development time course dataset. The agreement between results of the proposed random coefficient model and the previous reports for this proof-of-concept trial further validates this methodology, which provides a unified statistical model for systems analysis of microarray experiments with complex experimental designs when re-sampling based methods are difficult to apply.
基因集分析方法用于检测参与同一生物过程或分子途径的一组基因的协同变化。通过合并每组中多个单个基因的微弱信号，基因集分析获得了更高的统计功效。尽管已经提出了许多用于两组微阵列实验的基因集分析方法，但很少有方法可应用于时间进程实验。我们提出了一种统一的统计模型，使用随机系数模型在基因集水平上分析时间进程实验，随机系数模型属于更一般的混合效应模型类别。这些模型包括一个系统成分，用于模拟基因组的平均轨迹，以及一个随机成分（随机系数），用于模拟每个基因的轨迹相对于平均轨迹的变化情况。我们表明，所提出的模型（1）在区分随时间差异变化的基因集与无效基因集方面优于现有方法；（2）提供更稳定的结果，受抽样变异的影响较小；（3）充分模拟基因间的依赖性并保持I型错误率；（4）允许基于随机效应的预测值对基因进行排序。我们描述了使用具有“实际”相关性的基因表达数据进行的模拟研究，并使用小鼠结肠发育时间进程数据集展示了所提出的随机系数模型。所提出的随机系数模型的结果与该概念验证试验的先前报告之间的一致性进一步验证了这种方法，当基于重采样的方法难以应用时，该方法为具有复杂实验设计的微阵列实验的系统分析提供了一个统一的统计模型。
