一种评估微阵列实验样本量的简单方法。
A simple method for assessing sample sizes in microarray experiments.
作者信息
Tibshirani Robert
机构信息
Health Research & Policy, Stanford University, Stanford, CA 94305, USA.
出版信息
BMC Bioinformatics. 2006 Mar 2;7:106. doi: 10.1186/1471-2105-7-106.
Abstract
BACKGROUND
In this short article, we discuss a simple method for assessing sample size requirements in microarray experiments.
RESULTS
Our method starts with the output from a permutation-based analysis for a set of pilot data, e.g. from the SAM package. Then for a given hypothesized mean difference and various samples sizes, we estimate the false discovery rate and false negative rate of a list of genes; these are also interpretable as per gene power and type I error. We also discuss application of our method to other kinds of response variables, for example survival outcomes.
CONCLUSION
Our method seems to be useful for sample size assessment in microarray experiments.
摘要
背景
在这篇短文中，我们讨论一种评估微阵列实验中样本量需求的简单方法。
结果
我们的方法始于对一组先导数据（例如来自SAM软件包）进行基于排列分析的输出。然后，对于给定的假设均值差和各种样本量，我们估计基因列表的错误发现率和假阴性率；这些也可解释为每个基因的检验功效和I型错误。我们还讨论了该方法在其他类型反应变量（例如生存结果）中的应用。
结论
我们的方法似乎对微阵列实验中的样本量评估有用。
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