Aimone James B, Gage Fred H
Laboratory of Genetics, The Salk Institute of Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA.
J Neurosci Methods. 2004 May 30;135(1-2):27-33. doi: 10.1016/j.jneumeth.2003.11.016.
Affymetrix GeneChips are being used increasingly for quantitative monitoring of gene expression in a variety of biological systems. Depending on the experiment, the analysis of Affymetrix results can have several different goals ranging from calculation of signal strength for a variety of inter-gene comparisons to the determination of which genes show significant differential expression between sample conditions. There have been several proposed methods for precise quantification of expression signal with promising results; however the question of what constitutes a significant change between replicate groups still remains. We have designed a method which performs statistical analysis on the differential expression of genes in the Affymetrix GeneChip system at the probe level in order to bypass the assumptions made in other analysis techniques. Validation using both spike-in data and real experimental data proves the method is effective at isolating differentially expressed genes statistically, thereby eliminating the need for arbitrary restrictions such as fold change. Application to an existing neural stem cell data set demonstrates the method's applicability to highly complex systems and its ability to detect very low expression differences (<1.2-fold change), providing resolution which may be of significant interest in neural systems such as this.
Affymetrix基因芯片越来越多地用于各种生物系统中基因表达的定量监测。根据实验的不同，对Affymetrix结果的分析可以有几个不同的目标，从计算各种基因间比较的信号强度到确定哪些基因在样本条件之间显示出显著的差异表达。已经有几种用于精确量化表达信号的方法并取得了有前景的结果；然而，重复组之间显著变化的构成问题仍然存在。我们设计了一种方法，该方法在探针水平对Affymetrix基因芯片系统中基因的差异表达进行统计分析，以绕过其他分析技术中所做的假设。使用掺入数据和实际实验数据进行验证证明，该方法在统计学上有效分离差异表达基因，从而无需诸如倍数变化等任意限制。将该方法应用于现有的神经干细胞数据集，证明了该方法对高度复杂系统的适用性及其检测非常低表达差异（<1.2倍变化）的能力，提供了在这样的神经系统中可能具有重要意义的分辨率。
