Version Compatibility

Reference examples tested with: xcms 4.0+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Metabolomics Normalization and 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)

# 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)

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.

# 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: divide by trend, multiply by median
        median_val <- median(qc_values, na.rm = TRUE)
        normalized[, feature] <- data[, feature] / predicted * median_val
    }

    return(normalized)
}

data_corrected <- qc_rsc_normalize(data, sample_info)