.png){kind=icon}
返回
Sequential Design for Microarray Experiments
.png){kind=icon}
OA
AI
DOI：10.1198/jasa.2009.0135.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A critical aspect in the design of microarray studies is the determination of the sample size necessary to declare genes differentially expressed across different experimental conditions. In this article. we propose a sequential approach where the decision to stop the experiment depends on the accumulated microarray data. The study could stop whenever sufficient data have been accumulated to identify gene expression changes across several experimental conditions. The gene expression response is modeled by a robust linear regression model. We then construct a sequential confidence interval for the intercept of this model, which represents the median gene expression at a given experimental condition. We derive the stopping rule of the experiment for both continuous and discrete sequential approaches and give the asymptotic properties of the stopping variable. We demonstrate the desirable properties of our sequential approach, both theoretically and numerically. In our application to a study of hormone responsive breast cancer cell lines, we estimated the stopping variable for the sample size determination to be smaller than the actual sample size available to conduct the experiment. This means that we call obtain all accurate assessment of differential gene expression without compromising, the cost and size of the study. Altogether, we anticipate that this approach could have ail important contribution to microarray Studies by improving the usual experimental designs and methods of analysis.
Keyword:
Dose-response
Gene expression
Robust regression
Sample size
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
.png){kind=icon}
A Novel Vector-Based Approach to Color Image Retrieval Using a Vector Angular-Based Distance Measure
PREAI
分享收藏
查看更多内容