XCMS Metabolomics Preprocessing

Requires Bioconductor 3.18+ with xcms 4.0+ and MSnbase 2.28+.

Load Raw Data

library(xcms) library(MSnbase)

Read mzML/mzXML files

raw_files <- list.files('raw_data', pattern = '\.(mzML|mzXML)$', full.names = TRUE)

Create OnDiskMSnExp object

raw_data <- readMSData(raw_files, mode = 'onDisk')

Check data

raw_data table(msLevel(raw_data))

Define Sample Groups

Sample metadata

sample_info <- data.frame( sample_name = basename(raw_files), sample_group = c(rep('Control', 5), rep('Treatment', 5), rep('QC', 3)), injection_order = 1:length(raw_files) )

Assign to phenoData

pData(raw_data) <- sample_info

Peak Detection (Centroided)

CentWave algorithm for centroided data

cwp <- CentWaveParam( peakwidth = c(5, 30), # Peak width range in seconds ppm = 15, # m/z tolerance snthresh = 10, # Signal-to-noise threshold prefilter = c(3, 1000), # Min peaks and intensity mzdiff = 0.01, # Minimum m/z difference noise = 1000, # Noise level integrate = 1 # Integration method )

Run peak detection

xdata <- findChromPeaks(raw_data, param = cwp)

Summary

head(chromPeaks(xdata)) cat('Peaks found:', nrow(chromPeaks(xdata)), '\n')

Peak Detection (Profile Data)

MatchedFilter for profile/continuum data

mfp <- MatchedFilterParam( binSize = 0.1, fwhm = 30, snthresh = 10, step = 0.1, mzdiff = 0.8 )

xdata_profile <- findChromPeaks(raw_data, param = mfp)

Retention Time Alignment

Obiwarp alignment (recommended)

obp <- ObiwarpParam( binSize = 0.5, response = 1, distFun = 'cor_opt', gapInit = 0.3, gapExtend = 2.4 )

xdata <- adjustRtime(xdata, param = obp)

Check alignment

plotAdjustedRtime(xdata)

Peak Correspondence (Grouping)

Group peaks across samples

pdp <- PeakDensityParam( sampleGroups = pData(xdata)$sample_group, bw = 5, # RT bandwidth minFraction = 0.5, # Min fraction of samples minSamples = 1, # Min samples per group binSize = 0.025 # m/z bin size )

xdata <- groupChromPeaks(xdata, param = pdp)

Check feature definitions

featureDefinitions(xdata) cat('Features:', nrow(featureDefinitions(xdata)), '\n')

Gap Filling

Fill in missing peaks

fpp <- ChromPeakAreaParam() xdata <- fillChromPeaks(xdata, param = fpp)

Alternative: FillChromPeaksParam for more control

fpp2 <- FillChromPeaksParam( expandMz = 0, expandRt = 0, ppm = 0 )

Extract Feature Table

Get feature values (intensity matrix)

feature_values <- featureValues(xdata, method = 'maxint', value = 'into')

Feature definitions (m/z, RT)

feature_defs <- featureDefinitions(xdata) feature_defs <- as.data.frame(feature_defs) feature_defs$feature_id <- rownames(feature_defs)

Combine

feature_table <- cbind(feature_defs[, c('feature_id', 'mzmed', 'rtmed')], feature_values) rownames(feature_table) <- feature_table$feature_id

Save

write.csv(feature_table, 'feature_table.csv', row.names = FALSE)

Quality Control

TIC for each sample

tic <- chromatogram(raw_data, aggregationFun = 'sum') plot(tic)

Peak count per sample

peak_counts <- table(chromPeaks(xdata)[, 'sample']) barplot(peak_counts, main = 'Peaks per sample')

Check RT correction

par(mfrow = c(1, 2)) plotAdjustedRtime(xdata, col = pData(xdata)$sample_group)

PCA of features

library(pcaMethods) log_values <- log2(feature_values + 1) log_values[is.na(log_values)] <- 0 pca <- pca(t(log_values), nPcs = 3, method = 'ppca') plotPcs(pca, col = as.factor(pData(xdata)$sample_group))

CAMERA Annotation (Isotopes/Adducts)

library(CAMERA)

Create CAMERA object

xsa <- xsAnnotate(as(xdata, 'xcmsSet'))

Group by RT

xsa <- groupFWHM(xsa, perfwhm = 0.6)

Find isotopes

xsa <- findIsotopes(xsa, mzabs = 0.01, ppm = 10)

Find adducts

xsa <- findAdducts(xsa, polarity = 'positive')

Get annotated peak list

camera_results <- getPeaklist(xsa)

Export for MetaboAnalyst

Format for MetaboAnalyst web or R package

export_data <- t(feature_values) colnames(export_data) <- paste0('M', round(feature_defs$mzmed, 4), 'T', round(feature_defs$rtmed, 1))

Add sample info

export_df <- data.frame(Sample = rownames(export_data), Group = pData(xdata)$sample_group, export_data)

write.csv(export_df, 'metaboanalyst_input.csv', row.names = FALSE)

Related Skills

• metabolite-annotation - Identify metabolites

• normalization-qc - Normalize feature table

• statistical-analysis - Differential analysis