  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.
