A Bayesian mixture model for differential gene expression
A Bayesian mixture model for differential gene expression
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DOI：
10.1111/j.1467-9876.2005.05593.x
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发表时间：
2005-01-01
影响因子：
1.6
通讯作者：
Tang, F
中科院分区：
文献类型：
--
作者：
Do, KA;Müller, P;Tang, F
我们提出了基于模型的差异基因表达推理，使用非参数贝叶斯概率模型在不同条件下的基因强度分布。概率模型是正态分布的混合。由此产生的推理类似于用于相同推理问题的流行经验贝叶斯方法。完全基于模型的推理的使用减轻了经验贝叶斯方法的一些必要的局限性。我们认为在传统的非参数正态混合模型中，推理并不比后验模拟更困难。提出的方法是由一项微阵列实验所激发的，该实验旨在识别正常组织和结肠癌组织样本之间差异表达的基因。此外，我们还进行了小型模拟研究来验证所提出的方法。在激励案例研究中，我们展示了非参数贝叶斯方法如何促进后验期望错误发现率的评估。我们还展示了即使在没有已知非差分表示分数的零样本的情况下，推理如何进行。这突出了基于插件估计的其他经验贝叶斯方法的区别。
We propose model-based inference for differential gene expression, using a nonparametric Bayesian probability model for the distribution of gene intensities under various conditions. The probability model is a mixture of normal distributions. The resulting inference is similar to a popular empirical Bayes approach that is used for the same inference problem. The use of fully model-based inference mitigates some of the necessary limitations of the empirical Bayes method. We argue that inference is no more difficult than posterior simulation in traditional nonparametric mixture-of-normal models. The approach proposed is motivated by a microarray experiment that was carried out to identify genes that are differentially expressed between normal tissue and colon cancer tissue samples. Additionally, we carried out a small simulation study to verify the methods proposed. In the motivating case-studies we show how the nonparametric Bayes approach facilitates the evaluation of posterior expected false discovery rates. We also show how inference can proceed even in the absence of a null sample of known non-differentially expressed scores. This highlights the difference from alternative empirical Bayes approaches that are based on plug-in estimates.