A Comprehensive Protocol and Step-by-Step Guide for Multi-Omics Integration in Biological Research

This manuscript provides a comprehensive step-by-step guide for integrating multi-omics data in biological research. Multi-omics data integration refers to the process of combining and analyzing data measured on the same set of biological samples with different omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomes, lipidomics, and glycomics. Even though multi-omics approaches have similar objectives as single-block or single-omics analyses (for instance, description, discrimination, classification, or prediction), they are able to capture a broader spectrum of molecular information, thus providing a deeper understanding of biological systems and their complex interactions. Indeed, the combination of multiple-omics datasets enables the improvement of prediction accuracy and yields more robust results, especially in cases where the number of available samples is limited. Moreover, thanks also to the most recent development of machine learning techniques, multi-omics analyses are nowadays suitable to uncover hidden patterns and complex phenomena arising among different biological compounds. The primary aim of this work is to present the full protocol that is commonly used in multi-omics studies, from the initial formulation of the problem to the tools useful for the biological interpretation of the results. The manuscript describes in detail the various methods of integrating multi-omics data, including concatenation-based (low-level), transformation-based (mid-level), and model-based (high-level) approaches, and highlights their limitations and advantages, along with the presentation of general visualization and diagnostic tools.