Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA.
PLoS One. 2012;7(12):e52137. doi: 10.1371/journal.pone.0052137. Epub 2012 Dec 28.
Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. We assume that each study contains two experimental conditions: a treatment and control. We note that there exist environmental conditions for which genes that are supposed to be transcribed together lose their operon structure, and that our model is best carried out for known operon structures.
当前的贝叶斯微阵列模型在汇总多个研究时假设基因表达与其他基因无关。然而，在原核生物中，基因被排列在被共同调控的单元中（称为操纵子）。在这里，我们引入了一种新的贝叶斯模型，用于汇总基因表达研究，该模型将操纵子信息纳入模型中。我们的贝叶斯模型从同一操纵子中的其他基因中借用信息，以提高基因表达的估计。该模型生成基因特异性差异表达的后验概率，这是推理的基础。我们在模拟和生物学研究中发现，纳入共调控信息可以提高独立性模型的效果。我们假设每个研究包含两种实验条件：处理和对照。我们注意到，存在环境条件，在这些条件下，应该一起转录的基因失去了它们的操纵子结构，并且我们的模型最适合已知的操纵子结构。
