Sanchez P S, Glonek G F V
Discipline of Statistics, School of Mathematical Sciences, The University of Adelaide, SA 5005, Australia.
Biostatistics. 2009 Jul;10(3):561-74. doi: 10.1093/biostatistics/kxp012. Epub 2009 Apr 28.
Statisticians can play a crucial role in the design of gene expression studies to ensure the most effective allocation of available resources. This paper considers Pareto optimal designs for gene expression studies involving 2-color microarrays. Pareto optimality enables the recommendation of designs that are particularly efficient for the effects of most interest to biologists. This is relevant in the microarray context where analysis is typically carried out separately for those effects. Our approach will allow for effects of interest that correspond to contrasts rather than solely considering parameters of the linear model. We further develop the approach to cater for additional experimental considerations such as contrasts that are of equal scientific interest. This amounts to partitioning all relevant contrasts into subsets of effects that are of equal importance. Based on the partitions, a penalty is employed in order to recommend designs for complex and varied microarray experiments. Finally, we address the issue of gene-specific dye bias. We illustrate using studies of leukemia and breast cancer.
统计学家在基因表达研究设计中可发挥关键作用，以确保有效分配可用资源。本文考虑了涉及双色微阵列的基因表达研究的帕累托最优设计。帕累托最优性能够推荐出对生物学家最感兴趣的效应特别有效的设计。这在微阵列背景下是相关的，因为通常会针对这些效应分别进行分析。我们的方法将考虑与对比相对应的感兴趣效应，而不是仅仅考虑线性模型的参数。我们进一步发展该方法，以适应其他实验考虑因素，如具有同等科学重要性的对比。这相当于将所有相关对比划分为同等重要的效应子集。基于这些划分，采用一种惩罚措施来为复杂多样的微阵列实验推荐设计。最后我们解决基因特异性染料偏差问题。我们通过白血病和乳腺癌研究进行说明。
