bio-metabolomics-statistical-analysis
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.
npx skills add majiayu000/claude-skill-registry --skill statistical-analysis-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.
# Metabolomics Statistical Analysis ## Univariate Analysis ```r library(tidyverse) # Load normalized data data <- read.csv('normalized_data.csv', row.names = 1) groups <- factor(read.csv('sample_info.csv')$group) # T-test for each feature ttest_results <- apply(data, 2, function(x) { test <- t.test(x ~ groups) c(pvalue = test$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') # Significant features sig_features <- ttest_results[ttest_results$fdr < 0.05, ] cat('Significant features (FDR<0.05):', nrow(sig_features), '\n') ``` ## Fold Change Calculation ```r # Calculate fold change between groups calculate_fc <- function(data, groups) { group_means <- aggregate(data, by = list(groups), FUN = mean, na.rm = TRUE) rownames(group_means) <- group_means$Group.1 group_means <- group_means[, -1] fc <- as.numeric(group_means[2, ]) / as.numeric(group_means[1, ]) log2fc <- log2(fc) return(data.frame(feature = colnames(data), fold_change = fc, log2fc = log2fc)) } fc_results <- calculate_fc(data, groups) ``` ## Volcano Plot ```r library(ggpl
- Univariate Analysis
- Fold Change Calculation
- Volcano Plot
- PCA
- PLS-DA
- sPLS-DA (Sparse)
- OPLS-DA (Orthogonal PLS-DA)
- Random Forest
- ROC Analysis
- Heatmap
- Related Skills
What does the bio-metabolomics-statistical-analysis skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statistical-analysis-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.