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首页> 外文期刊>Genetical Research >Extreme value theory in analysis of differential expression in microarrays where either only up- or down-regulated genes are relevant or expected
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Extreme value theory in analysis of differential expression in microarrays where either only up- or down-regulated genes are relevant or expected

机译：在仅与上调或下调基因相关或预期的微阵列差异表达分析中的极值理论

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摘要

We propose an empirical Bayes method based on the extreme value theory (EVT) (BE) for the analysis of data from spotted microarrays where the interest of the investigator (e.g. to identify up-regulated gene markers of a disease) or the design of the experiment (e.g. in certain 'wild-type versus mutant' experiments) limits identification of differentially expressed genes to those regulated in a single direction (either up or down). In such experiments, unlike in genome-wide microarrays, analysis is restricted to the tail of the distribution (extremes) of all the genes in the genome. The EVT provides a platform to account for this extreme behaviour, and is therefore a natural candidate for inference about differential expression. We compared the performance of the developed BE method with two other empirical Bayes methods on two real 'wild-type versus mutant' datasets where a single direction of regulation was expected due to experimental design, and in a simulation study. The BE method appears to have a better fit to the real data. In the analysis of simulated data, the BE method showed better accuracy and precision while being robust to different characteristics of microarray experiments. The BE method, therefore, seems promising and useful for inference about differential expression in microarrays where either only up- or down-regulated genes are relevant or expected.
机译：我们提出了一种基于极值理论（EVT）（BE）的经验贝叶斯方法，用于分析斑点微阵列中的数据，研究人员对此感兴趣（例如，识别疾病的上调基因标记）或设计实验（例如，在某些“野生型与突变”实验中）将差异表达基因的鉴定限制为在单个方向（向上或向下）调控的基因。在这种实验中，与全基因组微阵列不同，分析仅限于基因组中所有基因的分布尾部。 EVT提供了一个解决此极端行为的平台，因此是推断差异表达的自然候选者。我们在两个真实的“野生型与突变”数据集上比较了开发的BE方法与其他两种经验贝叶斯方法的性能，该数据集由于实验设计和模拟研究而预期会有一个单一的调节方向。 BE方法似乎更适合实际数据。在模拟数据分析中，BE方法显示出更好的准确性和精密度，同时对微阵列实验的不同特征具有鲁棒性。因此，BE方法似乎很有前途，可用于推断微阵列中差异表达的情况，其中仅上调或下调的基因是相关的或预期的。

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