A random variance model for detection of differential gene expression in small microarray experiments
A random variance model for detection of differential gene expression in small microarray experiments
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DOI：
10.1093/bioinformatics/btg345
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发表时间：
2003-12-12
期刊：
影响因子：
5.8
通讯作者：
Simon, RM
中科院分区：
文献类型：
--
作者：
Wright, GW;Simon, RM
动机：微阵列技术为表征疾病的分子本质提供了一种有价值的方法。不幸的是，费用和有限的样本可获得性往往导致研究样本量小。这使得对变异的准确估计变得困难，因为基于基因的方差估计的自由度很少，并且所有基因都具有相等的方差的假设不太可能是正确的。结果：我们提出了一个模型，该模型从逆伽马分布中提取基因内的方差，该模型的参数是在所有基因上估计的。这导致测试统计量是标准线性模型中使用的统计量的微小变化。我们证明了模型假设在实验数据上是有效的，并且该模型比标准测试具有更强的能力来拾取表情的较大变化，而不会增加假阳性率。
Motivation: Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true.Results: We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives.