Statistics is probably the most challenging step of holo-omic studies, due to two main factors: the extreme complexity of the data, often containing thousands of features, and the limited sample size, often in the realm of the dozens of sampling units. This combination renders many holo-omic datasets rather statistics unfriendly.
In this workbook we strongly encourage researchers to proceed step-by-step when dealing with holo-omics data and biological questions.
The analysis of any multi-omic data should begin with independent analysis of each omic layer to learn about its structure and variability before jumping to multi-omic data integration.
- 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.
When it comes to multi-omic data integration, the approaches can be broadly categorised into two types: multi-staged analysis and meta-dimensional or simultaneous analysis.
Multi-staged integration: leverages the central dogma of molecular biology to assume that the variation in omic datasets is hierarchical, such that variation in DNA leads to variation in RNA and so on to determine the phenotype
Meta-dimensional integration: considers the possibility that the phenotype is the product of the combination of variation across all omic layers, with the presence of complex inter-omic interactions.
All statistical analyses included in the Holo-omics workbook are conducted in R environment. You can find the details to set-up your R environment in the section Prepare your R environment.