bio-multi-omics-mofa-integration

What problem does it solve?

MOFA2 integration solves the challenge of unifying heterogeneous omics data to uncover latent factors that drive biological variation across modalities, enabling researchers to interpret complex multi-omics experiments.

Core Features & Use Cases

• Load and align multiple omics matrices (RNA, proteomics, methylation, etc.) into a common samples-by-features structure.
• Configure and train MOFA2 models to identify shared and view-specific latent factors, with options for grouped data and single-cell workflows.
• Extract factor scores and feature weights, visualize variance explained, and interpret factors with downstream enrichment and metadata integration.
• Use case: integrate transcriptomics and proteomics to discover coordinated programs driving a biological condition.

Quick Start

Run MOFA2 on your multi-omics data to identify latent factors that explain cross-modality variation.