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bio-multi-omics-mofa-integration
Maintainer FreedomIntelligence · Last updated April 1, 2026
Multi-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-multi-omics-mofa-integration
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- R: MOFA2::create_mofa() → prepare_mofa() → run_mofa().
- Python: mofapy2 for training, muon for downstream.
- Find shared variation across my omics layers" → Discover latent factors that capture shared and modality-specific sources of biological variation in an unsupervised manner. R: MOFA2::create_mofa() → prepare_mofa() → run_mofa() Python: mofapy2 for training, muon for downstream.
- rna <- as.matrix(read.csv('rnaseq_matrix.csv', row.names = 1)) protein <- as.matrix(read.csv('proteomics_matrix.csv', row.names = 1)) methylation <- as.matrix(read.csv('methylation_matrix.csv', row.names = 1)).
Source Doc
Excerpt From SKILL.md
Prepare Multi-Omics Data
Goal: Load and align multiple omics matrices into a consistent format for MOFA2 input.
Approach: Read each omics layer, intersect to common samples, transpose to features-by-samples orientation.
library(MOFA2)
library(MultiAssayExperiment)
## Ensure consistent sample names across views
common_samples <- Reduce(intersect, list(rownames(rna), rownames(protein), rownames(methylation)))
rna <- rna[common_samples, ]
protein <- protein[common_samples, ]
methylation <- methylation[common_samples, ]
## Transpose to features x samples (MOFA format)
data_list <- list(
RNA = t(rna),
Protein = t(protein),
Methylation = t(methylation)
)
Use cases
- Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- mixomics-analysis - Supervised multi-omics integration
- data-harmonization - Preprocess data before MOFA
- pathway-analysis/go-enrichment - Interpret MOFA factors
- single-cell/multimodal-integration - Single-cell multi-omics
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