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Multi-omics Integration Pipeline Usage Guide

Overview

This workflow integrates multiple molecular data types (transcriptomics, proteomics, metabolomics, etc.) to discover shared biological signals and cross-modal biomarkers.

Prerequisites

BiocManager::install(c('MOFA2', 'mixOmics'))
install.packages(c('SNFtool', 'pheatmap'))

Quick Start

Tell your AI agent what you want to do: - "Integrate my transcriptomics and proteomics data" - "Run MOFA2 on my multi-omics dataset" - "Find shared factors across my omics modalities"

Example Prompts

Basic Integration

"I have RNA-seq and proteomics from the same samples, integrate them with MOFA2"

"Find shared biological signals across my multi-omics data"

Supervised Analysis

"Use DIABLO to find multi-omics biomarkers that predict treatment response"

"Run supervised multi-omics analysis with patient outcomes"

Patient Stratification

"Cluster patients using multi-omics data with SNF"

"Find patient subtypes from my integrated omics data"

When to Use This Pipeline

• Studies with multiple omics measurements
• Multi-platform biomarker discovery
• Patient stratification across modalities
• Understanding disease mechanisms
• Drug response prediction

Required Inputs

1. Normalized data matrices - One per modality (samples x features)
2. Sample metadata - Conditions, outcomes, covariates
3. Feature annotations - Gene names, protein IDs, metabolite names

Data Requirements

Samples

• Common samples across modalities (or at least significant overlap)
• Minimum 20-30 samples for MOFA, more for mixOmics
• Biological replicates preferred

Features

• Pre-normalized data (log2, z-score, etc.)
• Similar scales across modalities
• Feature IDs that can be mapped (genes, proteins, metabolites)

Integration Methods

MOFA2 (Recommended)

Best for: Unsupervised exploration, factor discovery

• Handles missing views/samples
• Identifies shared vs. unique variation
• Easy interpretation via factor weights
• GPU acceleration available

mixOmics DIABLO

Best for: Supervised classification, biomarker selection

• Requires outcome variable
• Feature selection built-in
• Cross-validation for variable tuning
• Sparse solutions

SNF (Similarity Network Fusion)

Best for: Patient clustering, network-based integration

• Creates fused similarity network
• Good for patient stratification
• No feature selection
• Works with any data type

Pipeline Steps

1. Data Preprocessing

• Normalize each modality separately
• Log transform if needed
• Handle missing values

2. Sample Harmonization

• Match samples across modalities
• Verify sample IDs are consistent
• Remove samples missing from all modalities

3. Feature Selection

• Select most variable features
• Optional: pre-filter by relevance
• Balance features across modalities

4. Integration

• Run MOFA/DIABLO/SNF
• Tune parameters (components, neighbors)
• Assess convergence

5. Interpretation

• Analyze factors/components
• Identify top features per factor
• Pathway enrichment on feature sets

Parameter Guidelines

MOFA2

Parameter	Recommended	Notes
num_factors	10-15	Start high, prune later
scale_views	TRUE	Equalizes modality contributions
maxiter	1000	Increase if not converged

mixOmics DIABLO

Parameter	Recommended	Notes
ncomp	2-5	Tune via CV
keepX	10-100	Tune per modality
design	0.1	Tutorial starting value, not a default; higher (~0.5-1) favors correlated blocks over prediction

SNF

Parameter	Recommended	Notes
K (neighbors)	10-30	~10-20% of samples
t (iterations)	20	Usually sufficient

Common Issues

Few common samples

• Check sample ID formatting
• Consider imputation for missing views
• MOFA handles missing better than DIABLO

Factors dominated by one view

• Increase scale_views strength
• Balance feature numbers
• Check for outliers in dominant view

No meaningful factors

• Check data quality
• Try different feature selection
• Verify biological signal exists

DIABLO overfitting

• Reduce number of features
• Use more CV folds
• Check sample size vs features

Output Files

File	Description
mofa_model.hdf5	Trained MOFA model
mofa_factor_values.csv	Sample factor scores
mofa_weights.csv	Feature weights per factor
variance_explained.png	Factor variance decomposition
factor_scatter.png	Sample visualization
top_weights_*.png	Important features

Interpretation Guide

Factor Variance Explained

• Factors explaining >5% variance are meaningful
• Shared factors: affect multiple modalities
• Unique factors: one modality only

Feature Weights

• High absolute weight = important feature
• Sign indicates direction of association
• Compare top features across modalities

Biological Interpretation

1. Extract top genes per factor
2. Run pathway enrichment
3. Connect to clinical variables
4. Validate key features

Tips

• Sample overlap: Ensure samples are matched across modalities (same patients/conditions)
• Normalization: Pre-normalize each modality separately before integration
• Missing views: MOFA2 handles missing views better than DIABLO
• Feature selection: Select top variable features per modality to reduce noise
• Interpretation: Run pathway enrichment on top-weighted features per factor; use all features in the view as background (not the full genome), and simplify() GO results to remove redundant terms

References

• MOFA2: doi:10.1186/s13059-020-02015-1
• mixOmics: doi:10.1371/journal.pcbi.1005752
• SNF: doi:10.1038/nmeth.2810