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

Summary

This work details methods for integrating multi-omics data (concatenation, transformation, and model-based). By combining data from genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, lipidomics, and glycomics, a comprehensive understanding of biological systems is achieved. The manuscript provides step-by-step guidelines, highlighting limitations, advantages, and visualization tools for multi-omics integration.

Abstract

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.

Introduction

The field of biological research has witnessed significant advancements in recent years, particularly in the area of omics technologies. These technologies provide valuable insights into the complex nature of biological systems. However, each omics technology offers a unique perspective on biological components, necessitating the integration of multi-omics data to obtain a comprehensive understanding.

Multi-omics encompasses various classes of biomolecules that can be quantitatively defined thanks to the advent of new and powerful high-throughput sequencing techniques. Among the different types of omics technologies are genomics, epigenomic....

Protocol

1. Research question(s) definition

1. Clearly articulate the specific research question(s) that will be addressed through multi-omics integration. E.g., Research question 1: What are the changes in protein expression and metabolite profiles that correlate with treatment response? Research question 2: How do genetic variations influence gene expression patterns in patients with a given disease? Research question 3: How does the integration of specific omic layers provide a comprehensive understanding of a specific biological process or disease mechanism?
2. Consider the inclusion of multiple omics technologies to search for biomarkers o....

Results

As in single-omics analysis, visualization is key for data exploration, data integration, pattern recognition, hypothesis generation, and communication of results. Specifically, good visualization of large data is very important during data pre-processing steps, aiding in the verification of normalization, identification of outliers, and many more. In multi-omics, even more so, visualization is vital, as it can aid in the evaluation of trends in the different omic layers/blocks, and the overlaying of information from eac.......

Discussion

Identifying the most relevant omics dataset is one of the first steps in a multi-omics integration study. In nutritional research, metabolomics represents one of the first omics layers to look at, since it can highlight the metabolic pathways and biochemical processes at the basis of dietary intervention or metabolic response to food intake. On the other hand, for example, in cancer biology, genomics and transcriptomics which give information about DNA, genetic variants, and gene expression dysregulations, can help under.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Apple M2 Pro macOS	Apple	14.3 (23D56)	Processing computer
ggplot2 R package	r-project.org	3.4.4	Create data visualizations
ggpubr R package	r-project.org	0.6.0	Create publication ready plots
ggrepel R package	r-project.org	0.9.5	Automatically position non-overlapping text labels with ggplot2
lattice R package	r-project.org	0.22-5	Tellis graphics for R
limma R package	r-project.org	3.58.1	Linear models for microarray data
mixOmics R package	r-project.org	6.25.1	Omic data integration project
NEMO R package	r-project.org	0.1.0	Neighbourhood-based multi-omics clustering
R	r-project.org	4.3.2 (2023-10-31)	Programming language for statistical computing and graphics
R Studio	RStudio	2023.12.1+402 (2023.12.1+402)	Integrated development environment for R
SNFtool R package	r-project.org	2.3.1	Similarity network fusion
tidyr R package	r-project.org	1.3.1	Tidy messy data
yardstick R package	r-project.org	1.3.0	Tidy characterizations of model performance