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Experimental design for two-color microarrays applied in a pre-existing split-plot experiment.

机译:在预先存在的分裂图实验中应用的双色微阵列的实验设计。

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摘要

Microarray applications for the study of gene expression are becoming accessible for researchers in more and more systems. Applications from field or laboratory experiments are often complicated by the need to superimpose sample pairing for two-color arrays on experimental designs that may already be complex. For example, split-plot designs are commonly used in biologicalsystems where experiments involve two types of treatments that are not readily applied atthe same scale. We demonstrate how effects that are confounded with arrays can still be estimated when there is sufficient replication. To illustrate, we evaluate three methods of sample pairing superimposed on a split-plot design with two treatments, deriving the variance associated withparameter estimates for each. Design A has levels of the whole plot treatment paired on the same microarray within a level of the subplot treatment. Design B has crossed levels paired on the same microarray. Design C has levels of the treatment applied to subplots paired on the same microarraywithin a whole plot. Designs A and B have lower variance than design C for comparing the levels of the whole plot treatment. Designs B and C have lower variance for comparing the levels of the subplot treatment and design C has lower variance for comparing the levels of the subplottreatment within each level of the whole plot treatment. We provide SAS code for the analyses of variance discussed.
机译:在越来越多的系统中,研究人员可以访问用于基因表达研究的微阵列应用程序。由于需要将可能是复杂的实验设计中的双色阵列的样品配对叠加在一起,因此来自野外或实验室实验的应用通常很复杂。例如,分裂图设计通常用于生物系统,其中实验涉及两种类型的处理,这些处理不容易以相同的规模应用。我们演示了当有足够的复制时如何仍然可以估计与数组混淆的效果。为了说明这一点,我们评估了在两种情况下叠加在分割图设计上的三种样品配对方法,得出了每种方法与参数估计值相关的方差。设计A在子图处理级别内将整个图处理级别与同一微阵列上的配对。设计B的交叉水平在同一微阵列上配对。设计C的处理水平适用于在整个样图中在同一微阵列上配对的子图。在比较整个样地处理的水平时,设计A和B的方差低于设计C。设计B和C具有较低的方差,用于比较子图处理的水平,而设计C具有较低的方差,用于在整个图处理的每个水平内比较子图处理的水平。我们提供SAS代码用于讨论的方差分析。

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