bio-metabolomics-normalization-qc

Load and Inspect Data

Goal: Load the feature table and sample metadata, separating QC and biological samples for downstream processing.

Approach: Read CSV files, partition by sample type, and assess missing value prevalence.

"Normalize my metabolomics data and correct for batch effects" → Apply QC-based signal correction, handle missing values, transform intensities, and assess normalization quality via RSD and PCA.

library(tidyverse)
library(pcaMethods)

## 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)

Goal: Remove injection-order-dependent signal drift using QC sample trends.

Approach: Fit a LOESS curve to QC sample intensities over injection order, then correct all samples by dividing by the predicted drift and rescaling to the QC median.