Power and Sample Size for Microarray Studies

描述（由申请人提供）：微阵列研究旨在发现 基因在不同的实验条件下差异表达， 生物样本在任何微阵列研究开始之前，研究者应该 仔细计划并回答以下重要问题： (a)实验中应包括多少样本组织？(b)多少 实验应该重复多少次？(c)有什么统计力量 实验必须揭示特定水平遗传差异 表情？ 微阵列研究的实验设计差异很大。因此， 这类问题对于任何类型的微阵列实验都很重要， study.在这个建议中，我们讨论了概念问题，并提出了计算 微阵列研究中统计功效和样本大小的方法。的 拟议的研究计划将包括实验设计的选择， 复制一项研究。建议的分析方法避免了使用 观察到的均方误差，因此，不使用t或F统计， 个体基因水平。提案中的陈述提到 cDNA阵列进行说明和讨论，但建议的方法， 同样适用于来自寡核苷酸阵列的表达数据。的 具体目标如下。 目标1。推导不同类型的计算能力和样本量公式 在微阵列研究中被验证的假设。目标2.定量评估 实验设计和重复对统计功效的影响 用于微阵列研究。目标3。开发计算能力和样本的软件 不同的实验设计。目标4。演示应用 在一些实际研究中推导出的方法。

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.