bio-metabolomics-normalization-qc
Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.
npx skills add majiayu000/claude-skill-registry --skill normalization-qc --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 Normalization and QC ## Load and Inspect Data ```r library(tidyverse) library(pcaMethods) # Load feature table (samples x features) data <- read.csv('feature_table.csv', row.names = 1) sample_info <- read.csv('sample_info.csv') # Separate QC samples qc_samples <- sample_info$sample_name[sample_info$sample_type == 'QC'] bio_samples <- sample_info$sample_name[sample_info$sample_type != 'QC'] data_qc <- data[qc_samples, ] data_bio <- data[bio_samples, ] # Missing value summary missing_pct <- colMeans(is.na(data)) * 100 cat('Features with >50% missing:', sum(missing_pct > 50), '\n') ``` ## QC-Based Normalization (QC-RSC) ```r # QC-based Robust Spline Correction library(statTarget) qc_rsc_normalize <- function(data, sample_info) { # Fit LOESS to QC samples over injection order # Correct biological samples based on QC trend injection_order <- sample_info$injection_order is_qc <- sample_info$sample_type == 'QC' normalized <- data for (feature in colnames(data)) { qc_values <- data[is_qc, feature] qc_order <- injection_order[is_qc] # Fit LOESS fit <- loess(qc_values ~ qc_order, span = 0.75) # Predict for all samples predicted <- predict(fit, injection_order) # Correct: divid
- Load and Inspect Data
- QC-Based Normalization (QC-RSC)
- Total Ion Current (TIC) Normalization
- Probabilistic Quotient Normalization (PQN)
- Batch Correction (ComBat)
- Missing Value Handling
- Data Transformation
- QC Assessment
- Quality Report
- Related Skills
What does the bio-metabolomics-normalization-qc skill do?
Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill normalization-qc --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.