Facing difficulty in Waters HDMS preproceesing in metabolomics pipeline
I am performing untargeted metabolomics analysis on a public dataset generated using a Waters SYNAPT-G2 HDMS (Q-TOF with ion mobility) coupled with ACQUITY UPLC. The raw data is in .raw format, and I need to convert it to .mzML for downstream processing in r/XCMS.
Because the raw files are very large and contain ion mobility data, I am using msconvert. However, I am facing issues deciding the correct conversion strategy.
The dataset details mention:
- Waters SYNAPT-G2 HDMS
- Ion mobility enabled acquisition
- Untargeted metabolomics workflow
I tested 3 conversion combinations:
- Only centroiding → mzML generated successfully, but downstream peak detection gives almost no usable peaks.
- Only
combineIonMobilitySpectra→ mzML looks usable and peaks are detected, but spectra are still largely profile-mode / insufficiently centroided. - Both centroiding +
combineIonMobilitySpectra→ mzML files become problematic/corrupted for downstream processing (e.g., m/z ordering / MSnbase errors).
At this point, using combineIonMobilitySpectra seems to be the only workable option, but I am doubtful whether collapsing ion mobility spectra at conversion is the correct approach biologically and computationally.
Has anyone processed Waters SYNAPT HDMS metabolomics data successfully for XCMS/MSnbase workflows?
- Is
combineIonMobilitySpectragenerally recommended here? - Should centroiding instead be done later inside R?
- Are there better msconvert filters/settings for Waters HDMS ion mobility data?
- How do people usually handle IM dimensions when the downstream tools do not fully support them?
Any guidance from people experienced with Waters HDMS preprocessing would help a lot.