Zang Shizhu, Guo Ruifang, Zhang Liang, Lu Youyong
Beijing Molecular Oncology Laboratory, Beijing Institute for Cancer Research, School of Oncology, Peking University, Haidian District, Beijing, PR China.
J Biomed Inform. 2007 Oct;40(5):552-60. doi: 10.1016/j.jbi.2007.01.002. Epub 2007 Jan 19.
Statistical methods have proven invaluable tools for enhancing the quality of microarray analysis. In this study, we used different methods such as significance analysis of microarrays (SAM) and Bayesian analysis of gene expression levels (BAGEL), to analyze the same set of raw data in an attempt to maximize the chance of identifying genes whose expression were significantly altered in gastric cancers. In addition, we examined the utility of an additional set of reference in controlling the variances and enhancing the quality of the results. Our results showed that BAGEL has the advantage of detecting small yet statistically significant differences, which might be of biological significance. Furthermore, introducing an additional control into the BAGEL, we were able to minimize the influence of the variances and significantly reduce number of potential false positive hits. BAGEL incorporates a novel control significantly improve the sensitivity and specificity of gene expression profiling analysis.
统计方法已被证明是提高微阵列分析质量的宝贵工具。在本研究中，我们使用了不同的方法，如微阵列显著性分析（SAM）和基因表达水平的贝叶斯分析（BAGEL），来分析同一组原始数据，试图最大限度地提高识别在胃癌中表达有显著改变的基因的机会。此外，我们还研究了另一组参考数据在控制方差和提高结果质量方面的效用。我们的结果表明，BAGEL具有检测微小但具有统计学显著性差异的优势，这些差异可能具有生物学意义。此外，在BAGEL中引入额外的对照，我们能够将方差的影响降至最低，并显著减少潜在假阳性命中的数量。BAGEL纳入了一种新型对照，显著提高了基因表达谱分析的灵敏度和特异性。
