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首页> 外文期刊>Journal of biomedical informatics. >Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies.
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Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies.

机译：统计推断方法和新型控制措施的集成，可提高表达谱研究中数据分析的敏感性和特异性。

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摘要

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.
机译：统计方法已证明是提高微阵列分析质量的宝贵工具。在这项研究中，我们使用了不同的方法，例如微阵列的显着性分析（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，以尝试最大程度地鉴定其表达显着的基因。胃癌发生改变。此外，我们检查了一组附加参考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的优势在于可以检测到微小但具有统计学意义的差异，这可能具有生物学意义。此外，在BAGEL中引入了一个额外的控件，我们能够最大程度地减少方差的影响并显着减少潜在的误报率。 BAGEL引入了一种新型对照，可显着提高基因表达谱分析的敏感性和特异性。

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