spectral-peak-parent-product-pairing
Summary
Identify and retrieve pairs of mass spectral peaks that represent parent ions and their derivative adduct ions by matching observed mass differences to a reference table of known chemical adducts. This skill bridges annotation (matching mass differences to known species) with spatial validation, enabling confirmation that putative parent–adduct pairs co-occur in imaging data.
When to use
Apply this skill when you have mass spectrometry imaging data with a histogram of pairwise mass differences that have already been matched to known adducts (via adductMatch), and you need to retrieve the actual mass peak pairs corresponding to a specific adduct of interest—particularly when you want to test whether those pairs show spatial correlation, which would support the hypothesis that they are true molecular adducts rather than chance matches.
When NOT to use
- You have not yet matched mass differences to a reference adduct table; use adductMatch first to annotate mass differences with known adduct names.
- You are working with a single list of mass peaks without pairwise differences; use massdiff to compute all pairwise mass differences before retrieving specific pairs.
- Your input is already a curated set of known parent–adduct assignments; there is no need to search for pairs by mass difference tolerance.
Inputs
- massdiff object (output from massdiff() containing parent mass A, adduct mass B, and mass difference)
- target adduct mass (numeric, e.g. 136.01600 for DHB-H2O)
- tolerance window (mDa or ppm units)
Outputs
- filtered massdiff object (subset of input massdiff containing only pairs matching the target adduct mass within tolerance)
- candidate parent–product ion pair list
How to apply
Start with a massdiff object (containing all pairwise mass differences from your MSI data) and the mass value and tolerance window for the adduct you wish to investigate. Use the diffGetPeaks function to extract all parent and derivative ion peak pairs whose mass difference falls within the specified tolerance (specified in mDa or ppm) of your target adduct mass. The function returns a filtered massdiff object containing only the pairs matching that criterion. This subset can then be passed to corrPairsMSI to test whether the peaks in those pairs are spatially correlated across the imaging dataset—a strong signal that they represent true adduct formation rather than spurious mass matches. The tolerance window should be set based on your instrument's mass accuracy; the README example uses ±0.5 mDa (mDa=1 for a 1 mDa window).
Related tools
- mass2adduct (R package containing diffGetPeaks function for retrieving parent–adduct ion pairs by mass difference; also provides massdiff object structure and corrPairsMSI for validation) — https://github.com/kbseah/mass2adduct
- R (Language and runtime environment for executing diffGetPeaks and corrPairsMSI functions)
- corrPairsMSI (Function (within mass2adduct) to test spatial correlation between retrieved parent–adduct pairs in MSI data, validating adduct hypothesis) — https://github.com/kbseah/mass2adduct
Examples
d.diff.DHBH2O <- diffGetPeaks(d.diff, mass=136.01600, mDa=1); d.diff.DHBH2O.corr <- corrPairsMSI(d, d.diff.DHBH2O)
Evaluation signals
- The returned filtered massdiff object contains only ion pairs whose mass difference falls within the specified tolerance (mDa or ppm) of the target adduct mass; verify by checking min/max mass difference in output.
- The number of retrieved pairs is non-zero and reasonable given the size of the original dataset and the selectivity of the tolerance window.
- When passed to corrPairsMSI, the retrieved pairs show significant spatial correlation (p < 0.05 with Bonferroni correction by default) across the imaging pixels, supporting the true adduct hypothesis.
- Spot-checks: manually verify that at least a sample of the retrieved pairs have mass differences visually close to the target adduct mass when plotted on the histogram.
- The output massdiff object retains the original structure (parent mass A, adduct mass B, difference) with the same column names, confirming successful filtering rather than object corruption.
Limitations
- The function depends on accurate specification of the tolerance window (mDa or ppm); a window that is too narrow may fail to retrieve true adducts (especially if the instrument has lower mass resolution than assumed), while a window that is too wide will retrieve spurious matches.
- Mass differences are inherently imprecise due to measurement error and uncertainty; the diffGetPeaks function does not account for instrumental mass calibration drift or systematic bias, so results may vary if the mass calibration changes between regions of the imaging dataset.
- The skill assumes that adduct masses are invariant across the dataset. In MALDI-MSI, adducts can form inconsistently depending on pixel-level chemistry (matrix concentration, salt content, local pH), so some true parent–adduct pairs may not be retrieved if they only occur in a subset of pixels.
- For very large datasets, even the filtered massdiff object may be large; subsequent correlation testing with corrPairsMSI may require the corrPairsMSIchunks variant to avoid memory exhaustion.
Evidence
- [readme] Find mass peaks associated with specific mass difference: "Find parent and derivative mass peaks associated with a specific molecular adduct, e.g. DHB-H2O (mass 136.01600), within a window of 1 mDa, i.e. +/- 0.5 mDa."
- [readme] diffGetPeaks function retrieves subset of massdiff: "d.diff.DHBH2O <- diffGetPeaks(d.diff, mass=136.01600, mDa=1)"
- [readme] Correlation testing validates parent–adduct pairs: "Test for spatial correlations between mass peaks in MS imaging data (imported with the
msimat function). For example, we wish to see if what we believe to be pairs of parent- and derivative-ion"
- [readme] Spatial correlation indicates true adduct formation: "If they are truly related by molecular adduct formation, then their abundances should be correlated."
- [readme] Output of diffGetPeaks is massdiff subset: "Output is a subset of the original
d.diff object."
1---2name: spectral-peak-parent-product-pairing3description: Use when you have mass spectrometry imaging data with a histogram of pairwise mass differences that have already been matched to known adducts (via adductMatch), and you need to retrieve the actual mass peak pairs corresponding to a specific adduct of interest—particularly when you want to test.4license: CC-BY-4.05---67# spectral-peak-parent-product-pairing89## Summary1011Identify and retrieve pairs of mass spectral peaks that represent parent ions and their derivative adduct ions by matching observed mass differences to a reference table of known chemical adducts. This skill bridges annotation (matching mass differences to known species) with spatial validation, enabling confirmation that putative parent–adduct pairs co-occur in imaging data.1213## When to use1415Apply this skill when you have mass spectrometry imaging data with a histogram of pairwise mass differences that have already been matched to known adducts (via adductMatch), and you need to retrieve the actual mass peak pairs corresponding to a specific adduct of interest—particularly when you want to test whether those pairs show spatial correlation, which would support the hypothesis that they are true molecular adducts rather than chance matches.1617## When NOT to use1819- You have not yet matched mass differences to a reference adduct table; use adductMatch first to annotate mass differences with known adduct names.20- You are working with a single list of mass peaks without pairwise differences; use massdiff to compute all pairwise mass differences before retrieving specific pairs.21- Your input is already a curated set of known parent–adduct assignments; there is no need to search for pairs by mass difference tolerance.2223## Inputs2425- massdiff object (output from massdiff() containing parent mass A, adduct mass B, and mass difference)26- target adduct mass (numeric, e.g. 136.01600 for DHB-H2O)27- tolerance window (mDa or ppm units)2829## Outputs3031- filtered massdiff object (subset of input massdiff containing only pairs matching the target adduct mass within tolerance)32- candidate parent–product ion pair list3334## How to apply3536Start with a massdiff object (containing all pairwise mass differences from your MSI data) and the mass value and tolerance window for the adduct you wish to investigate. Use the diffGetPeaks function to extract all parent and derivative ion peak pairs whose mass difference falls within the specified tolerance (specified in mDa or ppm) of your target adduct mass. The function returns a filtered massdiff object containing only the pairs matching that criterion. This subset can then be passed to corrPairsMSI to test whether the peaks in those pairs are spatially correlated across the imaging dataset—a strong signal that they represent true adduct formation rather than spurious mass matches. The tolerance window should be set based on your instrument's mass accuracy; the README example uses ±0.5 mDa (mDa=1 for a 1 mDa window).3738## Related tools3940- **mass2adduct** (R package containing diffGetPeaks function for retrieving parent–adduct ion pairs by mass difference; also provides massdiff object structure and corrPairsMSI for validation) — https://github.com/kbseah/mass2adduct41- **R** (Language and runtime environment for executing diffGetPeaks and corrPairsMSI functions)42- **corrPairsMSI** (Function (within mass2adduct) to test spatial correlation between retrieved parent–adduct pairs in MSI data, validating adduct hypothesis) — https://github.com/kbseah/mass2adduct4344## Examples4546```47d.diff.DHBH2O <- diffGetPeaks(d.diff, mass=136.01600, mDa=1); d.diff.DHBH2O.corr <- corrPairsMSI(d, d.diff.DHBH2O)48```4950## Evaluation signals5152- The returned filtered massdiff object contains only ion pairs whose mass difference falls within the specified tolerance (mDa or ppm) of the target adduct mass; verify by checking min/max mass difference in output.53- The number of retrieved pairs is non-zero and reasonable given the size of the original dataset and the selectivity of the tolerance window.54- When passed to corrPairsMSI, the retrieved pairs show significant spatial correlation (p < 0.05 with Bonferroni correction by default) across the imaging pixels, supporting the true adduct hypothesis.55- Spot-checks: manually verify that at least a sample of the retrieved pairs have mass differences visually close to the target adduct mass when plotted on the histogram.56- The output massdiff object retains the original structure (parent mass A, adduct mass B, difference) with the same column names, confirming successful filtering rather than object corruption.5758## Limitations5960- The function depends on accurate specification of the tolerance window (mDa or ppm); a window that is too narrow may fail to retrieve true adducts (especially if the instrument has lower mass resolution than assumed), while a window that is too wide will retrieve spurious matches.61- Mass differences are inherently imprecise due to measurement error and uncertainty; the diffGetPeaks function does not account for instrumental mass calibration drift or systematic bias, so results may vary if the mass calibration changes between regions of the imaging dataset.62- The skill assumes that adduct masses are invariant across the dataset. In MALDI-MSI, adducts can form inconsistently depending on pixel-level chemistry (matrix concentration, salt content, local pH), so some true parent–adduct pairs may not be retrieved if they only occur in a subset of pixels.63- For very large datasets, even the filtered massdiff object may be large; subsequent correlation testing with corrPairsMSI may require the corrPairsMSIchunks variant to avoid memory exhaustion.6465## Evidence6667- [readme] Find mass peaks associated with specific mass difference: "Find parent and derivative mass peaks associated with a specific molecular adduct, e.g. DHB-H2O (mass 136.01600), within a window of 1 mDa, i.e. +/- 0.5 mDa."68- [readme] diffGetPeaks function retrieves subset of massdiff: "d.diff.DHBH2O <- diffGetPeaks(d.diff, mass=136.01600, mDa=1)"69- [readme] Correlation testing validates parent–adduct pairs: "Test for spatial correlations between mass peaks in MS imaging data (imported with the `msimat` function). For example, we wish to see if what we believe to be pairs of parent- and derivative-ion"70- [readme] Spatial correlation indicates true adduct formation: "If they are truly related by molecular adduct formation, then their abundances should be correlated."71- [readme] Output of diffGetPeaks is massdiff subset: "Output is a subset of the original `d.diff` object."
HolobiomicsLab 