标题： 自适应gPCA：一种用于结构化降维的方法 显示英文标题

标题： Adaptive gPCA: A method for structured dimensionality reduction

摘要： 在处理大型生物数据集时，探索性分析是理解潜在结构以及生成将在后续分析中测试的假设的重要第一步。然而，当变量的数量相对于样本数量较大时，标准方法（如主成分分析）给出的结果往往不稳定且难以解释。 为了缓解这些问题，我们开发了一种方法，允许分析人员以一种鼓励相关变量在主轴上有相似载荷的方式结合关于变量之间关系的辅助信息。 这导致了样本的一个低维表示，该表示既描述了潜在结构，又使得轴可以用一组密切相关变量来解释。 该方法通过对数据施加一个编码变量间关系的先验，并在样本后验分布上进行分析推导而来。 我们展示了我们的方法在模拟数据中很好地重建了真实的潜在结构，并且还在研究抗生素对人类肠道细菌组成影响的数据集中演示了该方法。 显示英文摘要

摘要： When working with large biological data sets, exploratory analysis is an important first step for understanding the latent structure and for generating hypotheses to be tested in subsequent analyses. However, when the number of variables is large compared to the number of samples, standard methods such as principal components analysis give results which are unstable and difficult to interpret. To mitigate these problems, we have developed a method which allows the analyst to incorporate side information about the relationships between the variables in a way that encourages similar variables to have similar loadings on the principal axes. This leads to a low-dimensional representation of the samples which both describes the latent structure and which has axes which are interpretable in terms of groups of closely related variables. The method is derived by putting a prior encoding the relationships between the variables on the data and following through the analysis on the posterior distributions of the samples. We show that our method does well at reconstructing true latent structure in simulated data and we also demonstrate the method on a dataset investigating the effects of antibiotics on the composition of bacteria in the human gut.