Zhang Li, Wang Long, Ravindranathan Ajay, Miles Michael F
The Ernest Gallo Clinic and Research Center and Department of Neurology, University of California at San Francisco, 5858 Horton Street Suite 200 Emeryville, CA 94608, USA.
J Mol Biol. 2002 Mar 22;317(2):225-35. doi: 10.1006/jmbi.2001.5350.
Oligonucleotide arrays are a powerful technology for measuring the expression of thousands of genes simultaneously. Improvements in the sensitivity and precision of the measurements, which often pose a challenge to users, would assist the practical application of the technology. Here, we describe a new analysis algorithm for assessing changes in gene expression using oligonucleotide arrays. Changes in expression are detected in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. We show that the S-score is sensitive and reliable, enabling us to obtain more consistent results than with existing methods. Cluster analysis of S-score data of four brain regions exhibits patterns that are more distinctive because of improved data quality. In our case study of two mouse brain regions, over 200 genes were identified to have detectable changes between the ventral tegmental area and the prefrontal cortex. The genes with the most distinctive changes are found to be related to myelin or neurofilament synthesis, calcium signaling, and transcription factors. Many of these findings are in agreement with previous studies, using other techniques, such as in situ hybridization. Overall, our findings suggest that this new algorithm may have broad applicability for improving the analysis of oligonucleotide array data.
寡核苷酸阵列是一种强大的技术，可同时测量数千个基因的表达。测量的灵敏度和精度的提高对用户来说往往是一项挑战，而这将有助于该技术的实际应用。在这里，我们描述了一种使用寡核苷酸阵列评估基因表达变化的新分析算法。根据变化的统计显著性（S分数）检测表达变化，该分数根据寡核苷酸阵列的误差模型特征，结合多个探针组检测到的信号。我们表明，S分数灵敏且可靠，与现有方法相比，能让我们获得更一致的结果。对四个脑区的S分数数据进行聚类分析，由于数据质量提高，呈现出更明显的模式。在我们对两个小鼠脑区的案例研究中，超过200个基因被确定在腹侧被盖区和前额叶皮层之间有可检测到的变化。发现变化最明显的基因与髓磷脂或神经丝合成、钙信号传导和转录因子有关。这些发现中的许多与以前使用其他技术（如原位杂交）的研究一致。总体而言，我们的研究结果表明，这种新算法可能在广泛应用于改进寡核苷酸阵列数据分析方面具有广泛适用性。
