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bio-metabolomics-statistical-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
bio-metabolomics-statistical-analysis：统计分析 ，用于 metabolomics data。 Covers univariate testing，multivariate methods (PCA，PLS-DA)，、 biomarker discovery。 适合在identifying differentially abundant metabolites 或 building 分类 models时使用。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-metabolomics-statistical-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
- data <- read.csv('normalized_data.csv'，row.names = 1) groups <- factor(read.csv('sample_info.csv')$group)。
- ttest_results <- apply(data，2，function(x) { test <- t.test(x ~ groups) c(pvalue = test$p.value，fc = mean(x[groups == levels(groups)[2]]) - mean(x[groups == levels(groups)[1]])) }) ttest_results <- as.data.frame(t(ttest_results)) ttest_results$fdr <- p.adjust(ttest_results$pvalue，method = 'BH')。
原始文档
SKILL.md 摘录
Univariate Analysis
Goal: Identify differentially abundant metabolites between experimental groups using feature-wise statistical tests.
Approach: Apply t-tests to each feature independently, then correct for multiple testing with Benjamini-Hochberg FDR.
"Find the differentially abundant metabolites between my groups" → Apply univariate and multivariate statistical methods to identify metabolites with significant abundance differences.
library(tidyverse)
## Significant features
sig_features <- ttest_results[ttest_results$fdr < 0.05, ]
cat('Significant features (FDR<0.05):', nrow(sig_features), '\n')
Fold Change Calculation
Goal: Quantify the magnitude and direction of abundance changes between groups.
Approach: Compute group means for each feature and calculate log2 fold change as the ratio of group means.
适用场景
- 适合在identifying differentially abundant metabolites 或 building 分类 models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
- normalization-qc - Data preparation
- pathway-mapping - Functional interpretation
- multi-omics-integration/mixomics-analysis - Advanced multivariate methods
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