Muir W M, Rosa G J M, Pittendrigh B R, Xu S, Rider S D, Fountain M, Ogas J
Dept. Animal Sciences, Purdue University, W. Lafayette IN 47907.
Comput Stat Data Anal. 2009 Mar 15;53(5):1566-1576. doi: 10.1016/j.csda.2008.06.011.
The microarray is an important and powerful tool for prescreening of genes for further research. However, alternative solutions are needed to increase power in small microarray experiments. Use of traditional parametric and even non-parametric tests for such small experiments lack power and have distributional problems. A mixture model is described that is performed directly on expression differences assuming that genes in alternative treatments are expressed or not in all combinations (i) not expressed in either condition, (ii) expressed only under the first condition, (iii) expressed only under the second condition, and (iv) expressed under both conditions, giving rise to 4 possible clusters with two treatments. The approach is termed a Mean-Difference-Mixture-Model (MD-MM) method. Accuracy and power of the MD-MM was compared to other commonly used methods, using both simulations, microarray data, and quantitative real time PCR (qRT-PCR). The MD-MM was found to be generally superior to other methods in most situations. The advantage was greatest in situations where there were few replicates, poor signal to noise ratios, or non-homogenous variances.
微阵列是用于基因预筛选以进行进一步研究的重要且强大的工具。然而，需要其他解决方案来提高小型微阵列实验的效能。对于此类小型实验，使用传统的参数检验甚至非参数检验缺乏效能且存在分布问题。本文描述了一种混合模型，该模型直接对表达差异进行分析，假设在不同处理中基因以所有组合方式表达或不表达：（i）在两种条件下均不表达，（ii）仅在第一种条件下表达，（iii）仅在第二种条件下表达，以及（iv）在两种条件下均表达，从而在两种处理下产生4种可能的聚类。该方法被称为平均差异混合模型（MD-MM）方法。使用模拟、微阵列数据和定量实时PCR（qRT-PCR），将MD-MM的准确性和效能与其他常用方法进行了比较。发现在大多数情况下，MD-MM通常优于其他方法。在重复次数少、信噪比低或方差不均匀的情况下，其优势最为明显。
