MOFA+ Multi-Omics Factor Analysis

MOFA+ Multi-Omics Factor Analysis

Overview

MOFA+ (Multi-Omics Factor Analysis v2) is an unsupervised statistical framework that jointly decomposes multiple omics datasets into a small set of latent factors. Each factor captures an independent source of variation (e.g., cell cycle, a disease phenotype, a technical batch) and is associated with feature weights (loadings) that reveal which genes, peaks, or proteins drive it. The Python package mofapy2 produces an HDF5 model file compatible with downstream analysis in both Python and R. MOFA+ extends the original MOFA to support multi-group settings where samples belong to distinct cohorts or conditions.

When to Use

• Integrating two or more omics layers from the same set of cells or samples (e.g., scRNA-seq + scATAC-seq, RNA + proteomics, methylation + RNA)
• Identifying shared and view-specific sources of variation across omics modalities without supervised labels
• Comparing how latent factors differ between patient groups, treatment conditions, or time points in a multi-group analysis
• Reducing multi-omics dimensionality before clustering, trajectory inference, or survival modeling
• Discovering which genomic features (genes, peaks, proteins) drive each factor via sparse loadings
• Annotating latent factors by correlating factor scores with sample metadata (age, stage, treatment response)
• Use omics-plotting SKILL after training for publication-quality factor scatter and loading heatmaps
• Use scVI / MultiVI (scverse) instead when you need deep generative batch correction across modalities with explicit latent space inference and VAE architecture
• Use LIGER instead when your primary goal is integrating datasets across technologies (e.g., snRNA-seq + snATAC-seq) with shared and dataset-specific factors via iNMF

Prerequisites

• Python packages: mofapy2>=0.7, anndata>=0.10, numpy>=1.24, pandas>=2.0, scipy>=1.11, matplotlib>=3.7, seaborn>=0.13, muon (optional, for MuData integration)
• Data requirements: One AnnData object per omics view (cells/samples x features). All views must share the same obs (cell/sample) index. Missing samples per group are supported.
• Environment: Python 3.9+; trained model is saved as HDF5 and can be analyzed in R via the MOFA2 Bioconductor package

pip install mofapy2 anndata muon matplotlib seaborn

Pre-flight Interview

Settle these with the user before writing any analysis code.

decisions:
  - id: D1
    param: views
    kind: required
    source: data
    ask: "Which omics layers go into the model, and do they share the same samples?"
    default: null