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bio-multi-omics-mixomics-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Supervised and unsupervised multi-omics integration with mixOmics. Includes sPLS for pairwise integration and DIABLO for multi-block discriminant analysis. Use when performing supervised multi-omics integration or identifying features that discriminate between groups.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-multi-omics-mixomics-analysis
- 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：mixOmics：：block.splsda() (DIABLO)，mixOmics：：spls() ，用于 pairwise integration。
- 整合 my multi-omics data ，支持 supervised analysis" → Identify cross-omics feature signatures that discriminate between groups ，使用 sparse PLS 、 multi-block discriminant analysis. R：mixOmics：：block.splsda() (DIABLO)，mixOmics：：spls() ，用于 pairwise integration。
- X_rna <- as.matrix(read.csv('rnaseq.csv'，row.names = 1)) X_protein <- as.matrix(read.csv('proteomics.csv'，row.names = 1)) Y <- factor(read.csv('phenotype.csv')$Condition)。
原始文档
SKILL.md 摘录
Setup and Data Preparation
Goal: Load and align omics matrices with matching sample labels and phenotype information.
Approach: Read each omics layer and phenotype, then intersect to common samples.
library(mixOmics)
## Ensure matching samples
common <- Reduce(intersect, list(rownames(X_rna), rownames(X_protein)))
X_rna <- X_rna[common, ]
X_protein <- X_protein[common, ]
Y <- Y[match(common, read.csv('phenotype.csv')$Sample)]
Pairwise Integration: sPLS
Goal: Identify correlated features between two omics layers using sparse partial least squares.
Approach: Tune component count, fit sPLS with feature selection (keepX/keepY), and visualize cross-omics correlations.
适用场景
- 适合在performing supervised multi-omics integration 或 identifying features that discriminate between groups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 - Unsupervised multi-omics
- data-harmonization - Preprocess before integration
- differential-expression/de-results - Single-omics analysis
- pathway-analysis/go-enrichment - Interpret selected features
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