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数据与复现
BI
bio-multi-omics-similarity-network
维护者 FreedomIntelligence · 最近更新 2026年4月1日
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.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-multi-omics-similarity-network
- 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：SNFtool：：SNF() to fuse networks，spectral聚类() ，用于 subtyping。
- Stratify patients ，使用 multi-omics data" → Fuse omics-specific patient similarity networks into unified network ，用于 subtype discovery 、 聚类. R：SNFtool：：SNF() to fuse networks，spectral聚类() ，用于 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))。
原始文档
SKILL.md 摘录
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))
适用场景
- 适合在performing patient stratification 或 integrating multi-omics data into unified similarity networks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
- mofa-integration - Factor-based integration
- mixomics-analysis - Supervised integration
- single-cell/clustering - Single-cell clustering methods
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