Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
BMC Bioinformatics. 2011;12 Suppl 5(Suppl 5):S3. doi: 10.1186/1471-2105-12-S5-S3. Epub 2011 Jul 27.
As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets.
The proposed linear mixed effect model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed linear mixed effect model approach with the meta-analysis and the ANOVA model approaches. The linear mixed effect model approach was shown to provide higher powers than the other approaches.
The linear mixed effect model has advantages of allowing for various types of covariance structures over ANOVA model. Further, it can handle easily the correlated microarray data such as paired microarray data and repeated microarray data from the same subject.
随着实验规模的扩大，通常需要将来自不同实验室或医院的各种类型的微阵列（如配对和非配对微阵列）组合在一起。因此，在考虑数据集之间的异质性后，将微阵列数据一起进行分析以得出综合结论非常重要。微阵列实验的主要目的之一是在不同的实验组之间识别差异表达基因。我们提出了用于异构微阵列数据集综合分析的线性混合效应模型。
使用来自三个不同医院的 133 个微阵列的数据说明了所提出的线性混合效应模型。通过仿真研究，我们将所提出的线性混合效应模型方法与荟萃分析和 ANOVA 模型方法进行了比较。结果表明，线性混合效应模型方法比其他方法具有更高的功效。
线性混合效应模型相对于 ANOVA 模型具有允许使用各种类型的协方差结构的优点。此外，它可以轻松处理相关的微阵列数据，如配对微阵列数据和来自同一主体的重复微阵列数据。
