Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays
Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays
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DOI：
10.1186/1471-2105-5-54
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发表时间：
2004-05-05
影响因子：
3
通讯作者：
Townsend, JP
中科院分区：
文献类型：
--
作者：
Townsend, JP
背景：在斑点DNA微阵列研究中检测微小但有统计学意义的基因表达差异是一个持续的挑战。要应对这一挑战，需要仔细检查一系列统计模型的性能，以及对重复对解决这些差异的能力的影响进行实证检验。结果：推导出了新的模型，并开发了用于分析微阵列比率数据的软件。这些模型结合了乘性小误差项和与表达式水平成正比的误差标准偏差。最快和最强大的方法结合了相加的小误差项和与表达式水平成比例的误差标准偏差。四项研究的数据描述了它们揭示基因表达在统计学上显着差异的程度。对于检测基因表达微小差异的能力，有50%的经验性调用概率的基因表达水平是一个汇总统计。结论：了解可检测到的显著差异的基因表达差异的解决是实验设计和评估的重要组成部分。基因表达水平的这些微小差异很容易通过基因表达水平的贝叶斯分析来检测，该分析具有相加的误差项，并约束样本具有共同的误差变异系数。然后，可以通过Logistic回归来确定在研究中发现微小差异的能力。
Background: The detection of small yet statistically significant differences in gene expression in spotted DNA microarray studies is an ongoing challenge. Meeting this challenge requires careful examination of the performance of a range of statistical models, as well as empirical examination of the effect of replication on the power to resolve these differences.Results: New models are derived and software is developed for the analysis of microarray ratio data. These models incorporate multiplicative small error terms, and error standard deviations that are proportional to expression level. The fastest and most powerful method incorporates additive small error terms and error standard deviations proportional to expression level. Data from four studies are profiled for the degree to which they reveal statistically significant differences in gene expression. The gene expression level at which there is an empirical 50% probability of a significant call is presented as a summary statistic for the power to detect small differences in gene expression.Conclusions: Understanding the resolution of difference in gene expression that is detectable as significant is a vital component of experimental design and evaluation. These small differences in gene expression level are readily detected with a Bayesian analysis of gene expression level that has additive error terms and constrains samples to have a common error coefficient of variation. The power to detect small differences in a study may then be determined by logistic regression.