Multi-omic data analysis should start by evaluating each individual layer separately to gain insight into its structure and variability, before combining all layers. Despite the varying nature of the seven omic layers discussed in this guidebook, they all possess the common attribute of being multivariate, meaning they consist of multiple features, such as genomic variants, genes, metabolic pathways, proteins or metabolites, collected from multiple observations. This section contains the following three chapters:
- Data transformations: multivariate datasets consist of different data types (e.g., presence-absence of taxa, counts of genes, community-level metabolic capacity index of a function, concentrations of metabolites across samples) that may require specific transformation before applying statistical techniques.
- Unsupervised exploration of omic layers: include exploratory techniques, such as cluster analysis and ordination-based visualisation methods, which reveal the structure and main patterns of the omic datasets without prior information about experimental design. These procedures might reveal that the observations are structured into meaningful groups or that variables can be reduced to fewer dimensions.
- Supervised analysis of omic layers: this type of analyses incorporate information of experimental design and aim at testing and estimating the effects of the experimental factors (e.g., dietary treatment, drug administration) or variables of interest (e.g., age of the experimental subjects, geographic location of studied populations) on different omic layers.