Chiogna Monica, Massa Maria Sofia, Risso Davide, Romualdi Chiara
Department of Statistical Sciences, University of Padova, Padova, Italy.
BMC Bioinformatics. 2009 Feb 13;10:61. doi: 10.1186/1471-2105-10-61.
Various normalisation techniques have been developed in the context of microarray analysis to try to correct expression measurements for experimental bias and random fluctuations. Major techniques include: total intensity normalisation; intensity dependent normalisation; and variance stabilising normalisation. The aim of this paper is to discuss the impact of normalisation techniques for two-channel array technology on the process of identification of differentially expressed genes.
Through three precise simulation plans, we quantify the impact of normalisations: (a) on the sensitivity and specificity of a specified test statistic for the identification of deregulated genes, (b) on the gene ranking induced by the statistic.
Although we found a limited difference of sensitivities and specificities for the test after each normalisation, the study highlights a strong impact in terms of gene ranking agreement, resulting in different levels of agreement between competing normalisations. However, we show that the combination of two normalisations, such as glog and lowess, that handle different aspects of microarray data, is able to outperform other individual techniques.
在微阵列分析的背景下，已经开发了各种归一化技术，试图校正实验偏差和随机波动对表达测量的影响。主要技术包括：总强度归一化；强度依赖归一化；以及方差稳定归一化。本文的目的是讨论双通道阵列技术的归一化技术对差异表达基因鉴定过程的影响。
通过三个精确的模拟方案，我们量化了归一化的影响：(a) 对用于鉴定失调基因的特定检验统计量的敏感性和特异性的影响，(b) 对该统计量诱导的基因排名的影响。
虽然我们发现每次归一化后检验的敏感性和特异性差异有限，但该研究突出了在基因排名一致性方面的强烈影响，导致竞争归一化之间存在不同程度的一致性。然而，我们表明，处理微阵列数据不同方面的两种归一化方法（如glog和lowess）的组合能够优于其他单一技术。
