BI
Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipid…
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数据与复现
BI
bio-multi-omics-mofa-integration
维护者 FreedomIntelligence · 最近更新 2026年4月1日
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.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-multi-omics-mofa-integration
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- R：MOFA2：：create_mofa() → prepare_mofa() → run_mofa()。
- Python：mofapy2 ，用于 training，muon ，用于 downstream。
- Find shared variation across my omics layers" → Discover latent factors that capture shared 、 modality-specific sources of biological variation in unsupervised manner. R：MOFA2：：create_mofa() → prepare_mofa() → run_mofa() Python：mofapy2 ，用于 training，muon ，用于 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))。
原始文档
SKILL.md 摘录
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)
)
适用场景
- 适合在integrating RNA-seq，proteomics，methylation，或 other omics to discover latent factors driving biological variation across modalities时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- 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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