bio-multi-omics-similarity-network
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.
npx skills add majiayu000/claude-skill-registry --skill similarity-network-gptomics-bioskills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Similarity Network Fusion ## Basic SNF Workflow ```r library(SNFtool) # Load omics data (samples x features) 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)) # 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)) # Construct affinity matrices # K = number of neighbors, alpha = hyperparameter K <- 20 alpha <- 0.5 aff1 <- affinityMatrix(dist1, K, alpha) aff2 <- affinityMatrix(dist2, K, alpha) aff3 <- affinityMatrix(dist3, K, alpha) # Fuse networks # T = number of iterations fused <- SNF(list(aff1, aff2, aff3), K = K, t = 20) ``` ## Cluster Patients ```r # Determine optimal number of clusters estimateNumberOfClustersGivenGraph(fused, NUMC = 2:10) # Spectral clustering num_clusters <- 3 clusters <- spectralClustering(fused, num_clusters) # Add to samp
- Basic SNF Workflow
- Cluster Patients
- Visualize Network
- Normalized Mutual Information
- Feature Ranking with SNF
- Survival Analysis with Clusters
- Parameter Tuning
- Integration with Clinical Features
- Related Skills
What does the bio-multi-omics-similarity-network skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill similarity-network-gptomics-bioskills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.