Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, USA.
BMC Bioinformatics. 2011 Nov 8;12:433. doi: 10.1186/1471-2105-12-433.
Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and null densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the null density. Thus, there is no guarantee that the estimated null component will be no greater than the mixture density as it should be.
We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and null densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the null density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels.
The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical null density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical null component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.
二维聚丙烯酰胺凝胶电泳（2D 凝胶、2D PAGE、2-DE）是分析生物机体蛋白质组的强大工具。2D 凝胶图像的差异分析旨在寻找在不同条件下发生变化的蛋白质，这导致了大规模的假设检验，如在微阵列数据分析中。双组份经验贝叶斯（EB）模型已广泛讨论用于大规模假设检验，并应用于基因组数据分析。然而，它们尚未在 2D 凝胶数据的差异分析中实施。在文献中，测试统计量的混合和零密度分别进行估计。混合密度的估计没有考虑到关于零密度的假设。因此，不能保证所估计的零分量不会大于应该的混合密度。
我们提出了一种用于分析 2D 凝胶图像的双组份 EB 模型的实现。与已发表的估计方法不同，我们建议使用受约束的估计方法同时估计混合和零密度，该方法依赖于迭代重加权最小二乘法算法。在混合密度的估计中，自然考虑了零密度的假设。该策略使用来自因子实验的一组 2D 凝胶图像进行了说明。使用一组模拟凝胶验证了所提出的方法。
双组份 EB 模型对于大规模假设检验非常有用。在蛋白质组学分析中，理论上的零密度通常不合适。我们演示了如何为分析一组 2D 凝胶图像实现双组份 EB 模型。我们表明，有必要同时估计混合密度和经验零分量。所提出的受约束估计方法总是产生有效的估计和更稳定的结果。所提出的估计方法可应用于其他发生大规模假设检验的情况。
