General Effect Modelling (GEM) is an umbrella over different methods that utilise effects in the analyses of data with multiple design variables and multivariate responses. To demonstrate the methodology, we here use GEM in gene expression data where we use GEM to combine data from different cohorts and apply multivariate analysis of the effects of the targeted disease across the cohorts. Omics data are by nature multivariate, yet univariate analysis is the dominating approach used for such data. A major challenge in omics data is that the number of features such as genes, proteins and metabolites are often very large, whereas the number of samples is limited. Furthermore, omics research aims to obtain results that are generically valid across different backgrounds. The present publication applies GEM to address these aspects. First, we emphasise the benefit of multivariate analysis for multivariate data. Then we illustrate the use of GEM to combine data from two different cohorts for multivariate analysis across the cohorts, and we highlight that multivariate analysis can detect information that is lost by univariate validation.

翻译：通用效应建模(GEM)是一个涵盖多种方法的统称，这些方法利用效应分析具有多设计变量和多变量响应的数据。为展示该方法，本文在基因表达数据中应用GEM，通过GEM整合不同队列的数据，并跨队列对目标疾病的效应进行多变量分析。组学数据本质上是多变量的，但单变量分析仍是处理此类数据的主导方法。组学数据的一大挑战是特征（如基因、蛋白质和代谢物）数量通常极其庞大，而样本数量有限。此外，组学研究旨在获得跨不同背景普遍有效的结果。本文应用GEM解决上述问题。首先，我们强调对多变量数据进行多变量分析的益处。接着，我们阐释了利用GEM整合两个不同队列数据进行跨队列多变量分析的方法，并指出多变量分析能够检测到单变量验证所丢失的信息。