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bio-multi-omics-similarity-network
Maintainer FreedomIntelligence · Last updated April 1, 2026
Similarity Network Fusion (SNF) for patient stratification using multi-omics data. Integrates multiple data types into a unified patient similarity network. Use when performing patient stratification or integrating multi-omics data into unified similarity networks.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-multi-omics-similarity-network
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- R: SNFtool::SNF() to fuse networks, spectralClustering() for subtyping.
- Stratify patients using multi-omics data" → Fuse omics-specific patient similarity networks into a unified network for subtype discovery and clustering. R: SNFtool::SNF() to fuse networks, spectralClustering() for subtyping.
- data1 <- as.matrix(read.csv('rnaseq.csv', row.names = 1)) data2 <- as.matrix(read.csv('methylation.csv', row.names = 1)) data3 <- as.matrix(read.csv('mirna.csv', row.names = 1)).
Source Doc
Excerpt From SKILL.md
Basic SNF Workflow
Goal: Fuse multiple omics-specific patient similarity networks into a single unified network.
Approach: Compute per-omics distance and affinity matrices, then iteratively fuse with SNF.
library(SNFtool)
## Ensure matching samples
common <- Reduce(intersect, list(rownames(data1), rownames(data2), rownames(data3)))
data1 <- data1[common, ]
data2 <- data2[common, ]
data3 <- data3[common, ]
## Compute distance matrices
dist1 <- dist2(as.matrix(data1), as.matrix(data1))
dist2 <- dist2(as.matrix(data2), as.matrix(data2))
dist3 <- dist2(as.matrix(data3), as.matrix(data3))
Use cases
- Use when performing patient stratification or integrating multi-omics data into unified similarity networks.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- mofa-integration - Factor-based integration
- mixomics-analysis - Supervised integration
- single-cell/clustering - Single-cell clustering methods
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