Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.
We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.

分析高维功能基因组学（functional genomics）数据的首要步骤之一，便是对这类数据开展探索性分析。聚类分析（Cluster Analysis）与主成分分析（Principal Component Analysis）通常便是首选方法。尽管二者应用场景广泛，却存在显著缺陷：它们并非总能生成简洁且可解释的结果。基于功能基因组学数据往往同时包含信息性变异与非信息性变异这一观测结论，我们提出了一种可识别携带信息性变异的变量集合的方法。该方法随后会将这些信息性变异转化为易于解释的简化多元（simplivariate）分量。
我们针对新近提出的简化多元（simplivariate）模型提出了一种全新的实现方案。在该实现方案中，信息性变异通过乘法模型进行描述，这类模型可充分表征功能基因组学数据间的关联关系。通过模拟数据集与两个真实代谢组学（metabolomics）数据集的验证，该方法展现出了优异的性能表现。