AR
Data & Repro
arxiv-database
This skill provides Python tools for searching and retrieving preprints from arXiv.org via its public Atom API. It supports keyword search,…
K-Dense-AI/claude-scientific-skillsView
Data & Repro
BI
bio-metabolomics-statistical-analysis
Maintainer FreedomIntelligence · Last updated April 1, 2026
Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-metabolomics-statistical-analysis
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- 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').
Source Doc
Excerpt From SKILL.md
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.
Use cases
- Use when identifying differentially abundant metabolites or building classification models.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- normalization-qc - Data preparation
- pathway-mapping - Functional interpretation
- multi-omics-integration/mixomics-analysis - Advanced multivariate methods
Related skills
Related skills
BA
Data & Repro
bayesian-optimizer
Bayesian optimization for experimental design and hyperparameter tuning in biomedical research.
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-alignment-files-bam-statistics
Compute alignment statistics: flagstat, idxstats, coverage depth.
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-alignment-msa-statistics
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when com…
FreedomIntelligence/OpenClaw-Medical-SkillsView