Power and Sample Size for Microarray Studies

描述(由申请人提供)：基因芯片研究旨在发现 基因在不同实验条件下的差异表达 生物样本。在任何微阵列研究开始之前，研究人员应该 仔细计划并回答以下重要问题： (A)实验应包括多少样本组织？(B)多少 《泰晤士报》：这个实验应该复制吗？(C)统计权力有多大？ 实验必须发现特定水平的遗传差异 表情？ 微阵列研究的实验设计差异很大。因此，答案是 这些问题对于任何类型的微阵列实验都是重要的 学习。在这个提案中，我们讨论了概念问题，并提出了计算 微阵列研究中统计能力和样本量的方法。这个 拟议的研究计划将包括实验设计和实验设计的选择 研究的复制。建议的分析方法避免了使用 观测到的均方误差，因此不使用t或F统计量 个体基因的水平。建议书中的表述是参考的 以用于说明和讨论，但建议的方法学 同样适用于来自寡核苷酸阵列的表达数据。这个 具体目标如下所示。 目标1.推导不同类型的计算能力和样本量的公式 在微阵列研究中验证假说的可能性。目标2.定量评估 实验设计和重复对统计能力的影响 用于微阵列研究。目标3.开发计算能力和样本软件 不同实验设计的尺寸。目标4.演示应用程序 在几个实践研究中得出了方法论。

DESCRIPTION (provided by applicant): Microarray studies aim to discover which genes are differentially expressed under different experimental conditions in biological samples. Before any microarray study begins, the investigator should plan carefully and answer important questions like the following: (a) How many sample tissues should be included in the experiment? (b) How many times should the experiment be replicated? (c) What statistical power does the experiment have to uncover a specified level of genetic differential expression? Experimental designs for microarray studies vary widely. Hence, the answers to these kinds of questions are important for any type of microarray experimental study. In this proposal, we discuss conceptual issues and present computational methods for statistical power and sample size in microarray studies. The proposed research program will encompass choices of experimental design and replication for a study. The proposed analytical approach avoids the use of the observed mean square error and, hence, makes no use of t or F statistics at the level of the individual gene. The presentation in the proposal makes reference to cDNA arrays for illustrations and discussion but the suggested methodology is equally applicable to expression data from oligonucleotide arrays. The specific aims are listed below. Aim 1. Derive formulas for computing power and sample size for different types of hypotheses being tested in microarray studies. Aim 2. Quantitatively assess the effect of experimental design and replication in terms of statistical power for microarray studies. Aim 3. Develop software for computing power and sample size for different experimental designs. Aim 4. Demonstrate application of the derived methodology in several practical studies.