Multi-Omics Data Integration: A Practical Guide

Why Multi-Omics Integration Matters

Laboratory scientist analyzing protein samples with mass spectrometry equipment

Protein structure visualization for proteomics research

Modern biological research generates data across multiple molecular layers — genomics, transcriptomics, proteomics, metabolomics, and epigenomics. Each omics layer provides a partial view of cellular function. Genomics reveals what could happen, transcriptomics shows what is being expressed, proteomics captures the functional machinery, and metabolomics reflects the actual biochemical state. Integrating these layers is essential for a comprehensive understanding of biological systems.

However, multi-omics integration is far from trivial. Each data type has different scales, noise characteristics, missing data patterns, and dimensionalities. This guide walks you through the practical aspects of integrating multi-omics datasets, from preprocessing to biological interpretation.

Types of Multi-Omics Integration Strategies

1. Early Integration (Concatenation-Based)

The simplest approach is to concatenate all omics matrices into a single large feature matrix after appropriate normalization. Each sample is represented by a combined feature vector spanning all omics layers. This unified matrix can then be analyzed using standard machine learning or statistical methods.

Pros: Simple to implement; captures cross-omics correlations directly.

Cons: The curse of dimensionality; one noisy layer can dominate; requires all samples to have all data types.

2. Late Integration (Decision-Level)

In late integration, each omics dataset is analyzed independently to generate predictions or features, which are then combined at the decision level. For example, separate classifiers might be trained on transcriptomic and proteomic data, and their predictions fused using ensemble methods like voting, stacking, or Bayesian model averaging.

Pros: Each layer analyzed with appropriate methods; robust to missing layers.

Cons: Misses cross-layer interactions; integration is superficial.

3. Intermediate Integration (Transformation-Based)

Intermediate approaches transform each omics layer into a common latent space before integration. Methods like Multi-Omics Factor Analysis (MOFA/MOFA+), Joint and Individual Variation Explained (JIVE), and tensor decomposition fall into this category. These methods identify shared and omics-specific sources of variation.

Pros: Captures shared biology; handles heterogeneous data types; identifies both common and unique variation.

Cons: More complex; interpretation of latent factors requires expertise.

Essential Preprocessing Steps

Normalization

Each omics type requires appropriate normalization:

RNA-seq: DESeq2's variance-stabilizing transformation (VST) or TMM normalization from edgeR
Proteomics: Median centering, quantile normalization, or variance stabilization for label-free data; internal standard normalization for TMT/iTRAQ
Metabolomics: Total ion current (TIC) normalization, probabilistic quotient normalization (PQN), or SERRF for batch correction
Methylation arrays: Beta-Mixture Quantile normalization (BMIQ) or functional normalization (funnorm)

Handling Missing Data

Multi-omics datasets frequently contain missing values. In proteomics, missing values often result from the stochastic nature of data-dependent acquisition (DDA). Common strategies include:

Filtering: Remove features with >50% missing values
Imputation: kNN imputation, random forest imputation (missForest), or minimum value imputation for left-censored proteomics data
Model-based: Methods like MOFA handle missing data natively through probabilistic frameworks

Feature Selection

With thousands of features per omics layer, dimensionality reduction is critical. Variance filtering removes low-variance features. Differential analysis (DESeq2, limma) identifies features associated with conditions of interest. Pathway-level summarization reduces thousands of genes to hundreds of pathways using tools like GSVA or ssGSEA.

Practical Tools and Software

MOFA+ (Multi-Omics Factor Analysis)

MOFA+ is one of the most popular tools for multi-omics integration. It decomposes multi-omics data into a set of latent factors that capture the major axes of variation. Each factor has associated weights for each omics layer, revealing which features drive the observed patterns.

`# R code for MOFA+ library(MOFA2) mofa

mixOmics

The mixOmics R package offers multiple integration methods including sparse PLS (sPLS), DIABLO for supervised multi-omics classification, and MINT for multi-study integration. DIABLO is particularly useful for identifying multi-omics biomarker panels.

SNF (Similarity Network Fusion)

SNF constructs patient similarity networks from each omics type and fuses them into a single network. This approach is powerful for patient stratification and subtype discovery. The resulting fused network can be clustered using spectral clustering to identify disease subtypes.

iCluster+

iCluster+ performs joint clustering of multi-omics data using a penalized latent variable model. It was developed specifically for cancer subtyping and has been widely used in TCGA pan-cancer analyses.

Deep Learning Approaches

Autoencoders

Variational autoencoders (VAEs) learn compressed latent representations of multi-omics data. The encoder maps high-dimensional omics features to a low-dimensional latent space, while the decoder reconstructs the original data. The latent space captures meaningful biological variation and can be used for clustering, classification, or visualization.

Graph Neural Networks

GNNs integrate multi-omics data with prior biological knowledge encoded as graphs. Protein-protein interaction networks, gene regulatory networks, or pathway structures serve as the graph backbone, and omics measurements provide node features. This approach naturally incorporates biological context into the integration.

Attention-Based Models

Transformer architectures with cross-attention mechanisms can model interactions between different omics layers. Multi-omics transformers learn which features from one layer are most relevant for predicting features in another, providing interpretable cross-layer associations.

A Step-by-Step Integration Workflow

Step 1: Define the Biological Question

Is the goal unsupervised (subtype discovery, pattern finding) or supervised (classification, prediction)? This determines the appropriate integration strategy and evaluation criteria.

Step 2: Quality Control Each Layer

Before integration, thoroughly QC each omics dataset. Check for batch effects using PCA plots. Identify and remove outlier samples. Verify that biological signal is present in each layer independently.

Step 3: Normalize and Transform

Apply appropriate normalization for each omics type. Transform data to approximate normality if needed. Scale features to comparable ranges — this is especially important for concatenation-based approaches.

Step 4: Integrate

Choose an integration method based on your question, data characteristics, and computational resources. For exploratory analysis, MOFA+ provides excellent interpretability. For classification, DIABLO or deep learning approaches may be more appropriate.

Step 5: Interpret Biologically

Map integrated results back to biological knowledge. Perform pathway enrichment analysis on key features. Visualize results using network tools like Cytoscape. Validate findings in independent cohorts when possible.

Common Pitfalls and How to Avoid Them

Ignoring batch effects: Multi-omics data are often generated in different labs or at different times. Always check for and correct batch effects before integration.
Over-integrating: Not all omics layers will be informative for every question. Sometimes two layers are sufficient — adding more can introduce noise.
Overfitting: With many features and few samples, overfitting is a serious risk. Use cross-validation and regularization consistently.
Ignoring missing data patterns: Missing values in omics data are often not random. Understand the mechanism of missingness before choosing an imputation strategy.

Conclusion

Multi-omics data integration is both a tremendous opportunity and a significant challenge. The right approach depends on your biological question, data characteristics, and computational expertise. Start with well-established methods like MOFA+ or mixOmics, and progress to deep learning approaches as your confidence and data volume grow. The field is evolving rapidly, with new methods appearing regularly — staying current with the literature is essential for any multi-omics researcher.

📚 참고 데이터베이스: PubMed | UniProt | KEGG | Nature