bio-metabolomics-xcms-preprocessing

name	bio-metabolomics-xcms-preprocessing
description	Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabolomics/normalization-qc; for metabolite identification see metabolomics/metabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics/msdial-preprocessing; for downstream statistics see metabolomics/statistical-analysis.
origin	openai4s
category	bioskills/metabolomics
metadata	{"tool_type":"r","primary_tool":"xcms","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: xcms 4.x+ (MsExperiment/XcmsExperiment containers), Spectra 1.12+, CAMERA 1.58+

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

• R: packageVersion('xcms') then ?CentWaveParam to verify parameter names and defaults

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

A feature table is only meaningful alongside its full processing specification: which xcms version, every *Param value, and the fill/filter ordering. The table is a parameterized hypothesis about which molecules exist, not the data.

XCMS Untargeted LC-MS Preprocessing

"Turn my raw LC-MS files into a feature table" -> Detect chromatographic peaks per file, align retention times across runs, group corresponding peaks into features, fill gaps, then collapse adduct/isotope redundancy.

• R: readMsExperiment() -> findChromPeaks() -> adjustRtime() -> groupChromPeaks() -> fillChromPeaks() (xcms)

The Single Most Important Insight -- The Feature Table Is a Model-Dependent Artifact, Not Ground Truth

Every cell in the table is the output of a detection + grouping + filling model with chosen parameters. Two analysts with different centWave/grouping settings produce materially different tables from identical raw files, so "not detected" is a statement about the parameters, not the sample. Three consequences reorganize the whole workflow: (1) preprocessing parameters silently set the detection floor - a compound absent from results may be present in the raw data but excluded by noise/prefilter/peakwidth/snthresh; (2) fillChromPeaks integrates whatever signal sits in a feature window even when no peak exists, fabricating a positive number where the honest answer is "below detection"; (3) one compound yields 5-15 features (adducts, isotopologues, in-source fragments, multimers), so a 10,000-feature table is plausibly ~1,000 compounds (Mahieu 2017). Report parameters as part of the result, inspect EICs and alignment of every hit, and collapse redundancy before annotation.

API Generations -- Use Modern, Not Legacy

Path	Containers	Verbs	Status
Modern (xcms 4.x)	MsExperiment (raw, Spectra backend) / XcmsExperiment (result)	findChromPeaks / adjustRtime / groupChromPeaks / fillChromPeaks driven by *Param objects	Preferred
Legacy (xcms <3)	xcmsSet / xcmsRaw	findPeaks / group / retcor / fillPeaks; readMSData(mode='onDisk')	Deprecated - do not use in new code

Parameters are objects, not loose args: findChromPeaks(data, param = CentWaveParam(...)), never findChromPeaks(data, ppm=..., peakwidth=...).

Decision Tree by Scenario

Situation	Do	Why
High-res centroid (Orbitrap, Q-Exactive, qTOF)	CentWaveParam	Wavelet on real mass traces, no fixed binning
Low-res / quadrupole / profile-only	MatchedFilterParam	Model-peak on binned EICs tolerates poor resolution
Profile data of any kind	Centroid first (msconvert vendor peakPicking, or Spectra::pickPeaks)	centWave requires centroids; profile input yields garbage mass traces
Many shared, well-behaved peaks across samples	PeakGroupsParam (after an initial groupChromPeaks)	Loess on universal anchor peaks; gentle and fast
Few shared peaks / sparse / strong nonlinear drift	ObiwarpParam	Full-profile warping needs no prior peaks
Cohort with large case/control compositional differences	ObiwarpParam, or PeakGroupsParam with subset = QC indices	Few universal anchors mis-register the condition-specific metabolome
New instrument, no parameter priors	AutoTuner / IPO for a starting neighborhood, then verify against EIC FWHM	Optimizers maximize a surrogate, not biology (McLean 2020)
GC-EI data	Deconvolution tools, not xcms peak picking -> metabolomics/msdial-preprocessing	Co-elution + universal fragmentation require component separation first

Peak Detection

Goal: Detect chromatographic peaks in each centroided file.

Approach: Build a CentWaveParam with ppm and peakwidth set from the actual instrument and chromatography (see Quantitative Thresholds), then call findChromPeaks.

library(xcms)
# spectraFiles: centroided mzML paths; pd: data.frame with one row per file
raw <- readMsExperiment(spectraFiles = mzml_files, sampleData = pd)

# ppm is across-scan centroid scatter (~2-3x measured error), NOT the spec mass accuracy.
# peakwidth is c(min, max) in SECONDS, measured from EIC base-widths of known peaks.
cwp <- CentWaveParam(ppm = 10, peakwidth = c(2, 20), snthresh = 10,
                     prefilter = c(3, 1000), noise = 1000, mzdiff = -0.001,
                     integrate = 1L, mzCenterFun = 'wMean')
xdata <- findChromPeaks(raw, param = cwp)
nrowchromPeaksxdata

Retention-Time Alignment

Goal: Remove cross-run RT drift so the same compound lands at the same RT in every sample.

Approach: Choose obiwarp (no prior peaks) or peakGroups (anchor-based); align to a pooled QC, never to file #1. Regroup afterward because RTs changed.

# obiwarp: full-profile warping. binSize here is the m/z profile bin (default 1),
# distinct from PeakDensityParam$binSize and MatchedFilterParam$binSize.
xdata <- adjustRtime(xdata, param = ObiwarpParam(binSize = 0.6))

# peakGroups alternative needs an initial correspondence and good universal anchors:
# xdata <- groupChromPeaks(xdata, param = pdp_anchor)
# xdata <- adjustRtime(xdata, param = PeakGroupsParam(minFraction = 0.85, span = 0.4,
#     subset = which(sampleData(xdata)$sample_type == 'QC'), subsetAdjust = 'average'))
plotAdjustedRtime(xdata)

Correspondence (Grouping)

Goal: Match peaks across samples into consensus features.

Approach: Peak-density grouping in m/z slices; bw is the dominant knob and must reflect residual post-alignment RT scatter, not raw peak width.

pdp <- PeakDensityParam(sampleGroups = sampleData(xdata)$sample_group,
                        bw = 5, minFraction = 0.5, minSamples = 1, binSize = 0.025)
xdata <- groupChromPeaks(xdata, param = pdp)
nrow(featureDefinitions(xdata))

Gap-Filling

Goal: Integrate signal for features missing a detected peak in some samples.

Approach: fillChromPeaks with ChromPeakAreaParam; treat filled values as imputations, not measurements.

xdata <- fillChromPeaks(xdata, param = ChromPeakAreaParam())
filled <- chromPeakData(xdata)$is_filled   # logical flag; lives in chromPeakData, not chromPeaks
feat <- featureValues(xdata, value = 'into')        # features x samples matrix
defs <- featureDefinitions(xdata)                   # mzmed / rtmed / npeaks per feature

Redundancy Collapse

Goal: Group the same compound's adducts/isotopes/fragments back toward compound spectra before annotation.

Approach: CAMERA in order groupFWHM -> groupCorr -> findIsotopes -> findAdducts (isotopes before adducts). Correlation grouping needs enough samples to be meaningful and can over- or under-merge - verify against the table size.

library(CAMERA)
xsa <- xsAnnotate(as(xdata, 'xcmsSet'))
xsa <- groupFWHM(xsa, perfwhm = 0.6)
xsa <- groupCorr(xsa)
xsa <- findIsotopes(xsa, mzabs = 0.01, ppm = 10)
xsa <- findAdducts(xsa, polarity = 'positive')
peaklist <- getPeaklist(xsa)

QC Feature Filtering (Preprocessing/QC Bridge)

Goal: Drop features that fail conventional QC, operationalizing Broadhurst 2018 inside the xcms object.

Approach: filterFeatures with RsdFilter (CV in QCs), DratioFilter (sd_QC/sd_sample), PercentMissingFilter, BlankFlag. Drift correction and the full QC pipeline live in metabolomics/normalization-qc.

qc <- sampleData(xdata)$sample_group == 'QC'
study <- sampleData(xdata)$sample_group %in% c('Control', 'Treatment')
xdata <- filterFeatures(xdata, filter = RsdFilter(threshold = 0.3, qcIndex = qc))
xdata <- filterFeatures(xdata, filter = DratioFilter(threshold = 0.5, qcIndex = qc, studyIndex = study))

Per-Method Failure Modes

ppm set to the spec sheet

• Trigger: Setting ppm = 3 because the Orbitrap datasheet says 3 ppm.
• Mechanism: centWave ppm is across-scan centroid scatter, which exceeds time-averaged mass accuracy; too tight fragments one ion into short ROIs that each fail prefilter.
• Symptom: Features vanish entirely (not degrade); the better the instrument spec, the worse it looks.
• Fix: Set ppm to ~2-3x the empirical per-scan centroid scatter, not the datasheet number.

peakwidth mismatch

• Trigger: Copying the default c(20, 50) onto modern UHPLC.
• Mechanism: Lower bound too high discards sharp 2-5 s peaks; upper bound too low clips broad HILIC/tailing peaks. No warning is emitted.
• Symptom: Real peaks silently absent from the table.
• Fix: Measure base-width FWHM from 5-10 known EICs; set peakwidth ~ c(0.5x min, 2x max).

prefilter/noise/snthresh as the trace guillotine

• Trigger: Tuning snthresh while prefilter[I] already kills the trace.
• Mechanism: These are three serial gates on the same low-intensity signal; the lowest wins. On high-baseline instruments the default I=100 may both under-filter noise and kill trace metabolites.
• Symptom: Trace metabolites never appear regardless of snthresh.
• Fix: Lower prefilter[I] first for trace work; the lowest gate dominates.

bw too coarse / alignment-coupled

• Trigger: Copying bw = 30 onto UHPLC, or choosing bw independently of alignment quality.
• Mechanism: On UHPLC, bw=30 merges chromatographically resolved co-eluting compounds; with poor alignment a tight bw instead splits one compound across features.
• Symptom: Averaged-away differences (over-merge) or duplicate split features (under-merge).
• Fix: Set bw from residual post-alignment RT scatter (often 2-6 s on UHPLC); inspect EICs of merged/split features.

gap-filling fabricates intensities

• Trigger: Feeding a naively filled table straight into a t-test.
• Mechanism: Missingness is MNAR (below LOD); filling integrates the noise floor into a positive number, inflating the absent group's mean and shrinking the fold-change being tested.
• Symptom: "Significant" features that are mostly filled in one group.
• Fix: Track is_filled; report per-feature filled fraction; for inference use unfilled values with MNAR-aware imputation (QRILC/GSimp), reserving the fill for dense exploratory PCA.

skipping redundancy collapse

• Trigger: Treating feature count as compound count.
• Mechanism: One compound makes 5-15 features (~90% of features are degenerate, Mahieu 2017); correlated adduct "hits" multiply the multiple-testing burden.
• Symptom: Inflated dimensionality; clusters of co-significant features that are one molecule.
• Fix: Run CAMERA/RAMClustR before annotation; treat collapse as a tunable false-merge/false-split tradeoff with no ground truth.

Quantitative Thresholds

Threshold	Source	Rationale
ppm Orbitrap/Q-Exactive 5-10, qTOF 15-30	Tautenhahn 2008; instrument physics	~2-3x measured across-scan centroid scatter, not spec accuracy
peakwidth UHPLC c(2,20), HPLC c(10,40), HILIC c(10,60) (s)	Smith 2006; chromatography	Must bracket measured EIC base-widths; default c(20,50) wrong for UHPLC
Points across peak >= ~6-7	Zeng 2023 JASMS 34:1136	Below this, peak-area precision degrades non-linearly; an acquisition limit no parameter recovers
prefilter = c(3, I)	Tautenhahn 2008	Min 3 consecutive scans above intensity I; I set per instrument baseline
Grouping bw 2-6 s (UHPLC)	xcms vignette	Default 30 s merges resolved co-eluting compounds on fast chromatography
QC CV (RSD) < 0.20-0.30	Broadhurst 2018 Metabolomics 14:72	Features with high QC variance are unreliable
D-ratio < 0.5	Broadhurst 2018	Technical variance must sit well below biological
Blank flag k ~ 3-5	Broadhurst 2018	Test-sample mean must exceed k x blank mean
~1 compound per 5-15 features	Mahieu 2017 Anal Chem 89:10397	~90% of detected features are adduct/isotope/fragment degeneracy

Common Errors

Error / symptom	Cause	Solution
could not find function "readMSData" or legacy verbs missing	Using deprecated xcmsSet/readMSData API on xcms 4.x	Use readMsExperiment() + the findChromPeaks/groupChromPeaks verbs
unused argument (ppm = ...) in findChromPeaks	Passing loose args instead of a *Param object	Wrap in CentWaveParam(...) and pass via param =
Features defined on uncorrected RT	Skipped the regroup after adjustRtime	Call groupChromPeaks again after alignment
Garbage mass traces, almost no peaks	Profile (non-centroid) data fed to centWave	Centroid first (msconvert vendor peakPicking or Spectra::pickPeaks)
sampleGroups length/semantics error	Vector misaligned with sample order or missing	Pass sampleData(xdata)$group matching file order; it is mandatory
Three different binSize defaults confused	obiwarp (m/z, default 1) vs PeakDensity (m/z, 0.25) vs matchedFilter (m/z, 0.1)	Set each in its own *Param; they are not the same knob
as(xdata, 'xcmsSet') fails or warns	CAMERA expects the legacy container	Coerce the XcmsExperiment to xcmsSet only for CAMERA; keep modern objects upstream

References

• Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G. 2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779-787.
• Tautenhahn R, Bottcher C, Neumann S. 2008. Highly sensitive feature detection for high resolution LC/MS (centWave). BMC Bioinformatics 9:504.
• Prince JT, Marcotte EM. 2006. Chromatographic alignment of ESI-LC-MS proteomic data sets by ordered bijective interpolated warping. Anal Chem 78:6140-6152.
• Lange E, Tautenhahn R, Neumann S, Gropl C. 2008. Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics 9:375.
• Kuhl C, Tautenhahn R, Bottcher C, Larson TR, Neumann S. 2012. CAMERA: an integrated strategy for compound spectra extraction and annotation of LC/MS data sets. Anal Chem 84:283-289.
• Myers OD, Sumner SJ, Li S, Barnes S, Du X. 2017. Detailed investigation and comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods. Anal Chem 89:8689-8695.
• Mahieu NG, Patti GJ. 2017. Systems-level annotation of a metabolomics data set reduces 25,000 features to fewer than 1,000 unique metabolites. Anal Chem 89:10397-10406.
• McLean CM, Kujawinski EB. 2020. AutoTuner: high fidelity and robust parameter selection for metabolomics data processing. Anal Chem 92:5724-5732.
• Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB. 2018. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 14:72.
• Zeng W, Bateman KP. 2023. Quantitative LC-MS/MS. 1. Impact of points across a peak on the accuracy and precision of peak area measurements. J Am Soc Mass Spectrom 34(6):1136-1144.
• Louail P, Brunius C, Garcia-Aloy M, et al. 2025. xcms in peak form: now anchoring a complete metabolomics data preprocessing and analysis software ecosystem. Anal Chem 97:27639-27645.

Related Skills

• metabolomics/normalization-qc - Drift correction, CV/D-ratio filtering, and feature-table normalization
• metabolomics/metabolite-annotation - Identification of features into named metabolites
• metabolomics/msdial-preprocessing - GUI/MS-DIAL alternative with MS2Dec deconvolution and GC-EI support
• metabolomics/statistical-analysis - Differential and multivariate statistics on the feature table