Introduction

In the era of biological big data, multi-omics analysis represents a major challenge for researchers. How can we make sense of these gigantic biological datasets from different layers of cellular information? How can we integrate transcriptomic, proteomic, and metabolomic data to gain a comprehensive view of biological processes?

These are precisely the questions that my team and I attempted to answer through our project “Web Interface: Advanced Analysis of Multi-Omics Data.” In this article, I share our experience in creating an interactive web solution that facilitates the complex analysis of these datasets.

What are Multi-Omics Data?

Before diving into the technical details, let’s clarify what multi-omics data are. The suffix “-omics” refers to the comprehensive study of a specific biological system. Thus:

• Transcriptomics studies the entire set of messenger RNAs (gene expression)
• Proteomics focuses on the complete set of proteins
• Metabolomics analyzes all metabolites (small molecules)
• Phenomics measures the observable characteristics of organisms

Each of these information layers is represented by large data matrices. Multi-omic analysis aims to integrate these different matrices to understand the complex relationships between the various levels of biological organization.

The Challenge: WallOmics

Our project focused on WallOmics data, a dataset from the model plant Arabidopsis thaliana. These data were collected from two different organs (rosettes and stems) of five genetic variants (ecotypes), exposed to two different temperature conditions.

The main challenge was to explore and analyze this massive data coherently, using matrix factorization approaches and offering an intuitive web interface.

Our Approach: OmicsMatrix - The Matrixperience

Faced with this challenge, we developed “OmicsMatrix: The Matrixperience,” an interactive web interface based on R Shiny that integrates several advanced analysis methods. Here are the main features we implemented:

1. Intelligent Data Loading and Preprocessing

The first step was to enable easy data loading and immediate visualization. Our interface offers the ability to load data from WallOmics Data and automatically prepare it for analysis.

Our preprocessing pipeline automatically handles:

• Detection and treatment of missing values
• Data normalization
• Optional exclusion of irrelevant variables

2. In-depth Visual Exploration

Visual exploration is crucial for understanding data structure before applying more complex methods. Our “Exploration & PCA” panel offers:

• A global summary of datasets
• Visualizations of variable distributions
• Interactive correlation matrices
• Principal Component Analysis (PCA) for dimensionality reduction

PCA proved particularly useful for identifying the most important variables that explain data variance and for visualizing sample separation in a reduced space.

3. Advanced Data Integration Methods

Our interface offers four main methods of multi-omic analysis, each with specific advantages:

Regularized Canonical Correlation Analysis (rCCA)

This method allows for exploring correlations between two omics datasets. In our experience, rCCA was particularly effective in discovering relationships between transcriptomic and proteomic data.

One of the challenges encountered with rCCA was the choice of regularization parameters. We implemented two approaches:

• Cross-validation (resource-intensive but accurate)
• Shrinkage approach (faster but less optimal)

Partial Least Squares (PLS)

The PLS method allowed us to maximize covariance between different data matrices. Its main advantage is its ability to handle highly correlated data, a common characteristic of omics data.

We also implemented the sparse version (Sparse PLS) which automatically selects the most important variables, thus reducing model complexity.

PLS-DA for Classification

For classification questions (e.g., distinguishing samples according to their ecotype or growth condition), we used the PLS-DA (Partial Least Squares-Discriminant Analysis) method.

This method proved particularly useful for identifying biological markers that discriminate between different experimental conditions.

DIABLO for Multiple Integration

To simultaneously integrate more than two omics datasets, we implemented DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies).

DIABLO was the most powerful method in our arsenal, allowing us to discover biomarkers associated with the studied phenotypes by combining information from all available omic layers.

Key Strategies for Effective Multi-Omics Analysis

Based on extensive experience with the OmicsMatrix platform, four critical strategies emerge for successful multi-omics data integration:

1. Rigorous Exploratory Analysis as Foundation

Comprehensive exploratory analysis must precede any advanced analytical techniques. This foundational step reveals data structure, identifies outliers, and guides subsequent methodological choices. Investing time in thorough exploration consistently yields more interpretable and biologically meaningful final results.

2. Interactive Analysis Workflows

Multi-omics analysis demands an iterative approach with continuous parameter refinement. Real-time visualization of these adjustments’ impact is essential for optimal results. The R Shiny framework provides the necessary reactive environment to support this advanced analytical process.

3. Method Selection Driven by Biological Questions

Each analytical method serves distinct biological objectives. For general data structure understanding, PCA excels; for pairwise dataset relationships, rCCA provides optimal insights; for prediction models, standard PLS offers robust solutions; for classification tasks, PLS-DA delivers superior performance; while comprehensive integration across multiple omics layers requires DIABLO’s sophisticated approach.

4. Advanced Visualization for Biological Interpretation

Even the most sophisticated analytical results remain ineffective without appropriate visualization techniques. Strategic data visualization transforms complex statistical outputs into interpretable biological insights, facilitating both analysis and communication of findings to diverse stakeholders.

Conclusion

The analysis of multi-omics data represents a considerable challenge in bioinformatics, but our experience with OmicsMatrix shows that a well-designed web interface can greatly facilitate this process.

Our solution has made it possible to effectively analyze WallOmics data and draw relevant biological conclusions about Arabidopsis thaliana’s response to different environmental conditions.

The explosion of high-throughput biological data continues to transform biological research, and tools like OmicsMatrix serve as critical bridges between raw data complexity and actionable biological knowledge.

This article is based on a project carried out in collaboration with Zine-Eddine F, Mohammed I A, and Lounes M, under the supervision of Lazhar L, as part of the MLSD Master’s program at UFR Biomédical.