Jaffrézic Florence, Marot Guillemette, Degrelle Séverine, Hue Isabelle, Foulley Jean-Louis
INRA, UR337 Station de Génétique Quantitative et Appliquée, Jouy-en-Josas, France.
Genet Res. 2007 Feb;89(1):19-25. doi: 10.1017/S0016672307008646.
The importance of variance modelling is now widely known for the analysis of microarray data. In particular the power and accuracy of statistical tests for differential gene expressions are highly dependent on variance modelling. The aim of this paper is to use a structural model on the variances, which includes a condition effect and a random gene effect, and to propose a simple estimation procedure for these parameters by working on the empirical variances. The proposed variance model was compared with various methods on both real and simulated data. It proved to be more powerful than the gene-by-gene analysis and more robust to the number of false positives than the homogeneous variance model. It performed well compared with recently proposed approaches such as SAM and VarMixt even for a small number of replicates, and performed similarly to Limma. The main advantage of the structural model is that, thanks to the use of a linear mixed model on the logarithm of the variances, various factors of variation can easily be incorporated in the model, which is not the case for previously proposed empirical Bayes methods. It is also very fast to compute and is adapted to the comparison of more than two conditions.
方差建模在微阵列数据分析中的重要性如今已广为人知。特别是，用于差异基因表达的统计检验的功效和准确性高度依赖于方差建模。本文的目的是使用一个关于方差的结构模型，该模型包括一个条件效应和一个随机基因效应，并通过处理经验方差为这些参数提出一种简单的估计程序。将所提出的方差模型与各种方法在真实数据和模拟数据上进行了比较。结果表明，它比逐个基因分析更具功效，并且比齐次方差模型对假阳性数量更具稳健性。即使对于少量重复样本，与最近提出的方法如SAM和VarMixt相比，它也表现良好，并且与Limma表现相似。结构模型的主要优点是，由于在方差的对数上使用了线性混合模型，可以轻松地将各种变异因素纳入模型，而先前提出的经验贝叶斯方法则并非如此。它的计算速度也非常快，并且适用于两个以上条件的比较。
