metabolomics-xcms-preprocessing

Preprocess raw LC-MS metabolomics data using XCMS3 to automate peak detection, retention time alignment, feature grouping, and gap filling, generating a clean feature table ready for downstream statistical analysis and metabolite identification.

CentWave and MatchedFilter are both supported peak detection algorithms for metabolomics data. CentWave detects centwave-shaped peaks while MatchedFilter filters matched signals, both allowing configurable parameters for m/z tolerance, peak width, and signal-to-noise thresholds.

Yes, XCMS3 integrates CAMERA to annotate adducts and isotopes, operating on the grouped features after peak detection, retention time alignment, and gap filling to provide comprehensive feature annotation for metabolomics data.

This XCMS3-based preprocessing workflow supports both untargeted LC-MS and GC-MS metabolomics data, applying peak detection, alignment, feature grouping, and gap filling across both data types to produce a unified feature table.

Correct retention time drift across samples using the Obiwarp alignment method integrated into the XCMS3 workflow, which aligns chromatographic peaks temporally before feature grouping and gap filling to ensure accurate feature comparison.

Impute missing values in an LC-MS feature table using the gap filling step within the XCMS3 preprocessing workflow, which retrieves and fills missing peak signals after consolidating detected peaks into grouped features.