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Use when when you have binned mass spectrometry imaging peaks and want to understand which detected mass-to-charge ratios represent the same metabolite in different ionization states (parent vs. adduct form).
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Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | adduct-ion-parent-ion-pairing-analysis |
| description | Use when when you have binned mass spectrometry imaging peaks and want to understand which detected mass-to-charge ratios represent the same metabolite in different ionization states (parent vs. adduct form). |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3632","edam_topics":["http://edamontology.org/topic_0121","http://edamontology.org/topic_3520"],"tools":["mass2adduct","R","Cardinal","SCiLS","MSiReader"],"techniques":["CE-MS","MS-imaging"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/acs.analchem.0c04720","title":"mass2adduct"}] |
| evidence_spans | ["This package presents tools for counting and identifying possible adducts in MS data","library(mass2adduct)"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_mass2adduct","doi":"10.1021/acs.analchem.0c04720","title":"mass2adduct"}],"dedup_kept_from":"coll_mass2adduct"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.analchem.0c04720","all_source_dois":["10.1021/acs.analchem.0c04720"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Identify and validate parent–adduct ion pairs in MALDI-MSI data by matching mass differences to known chemical adducts (e.g., sodium, potassium, matrix ions) and confirming spatial correlation between paired peaks across imaging pixels. This skill distinguishes true molecular adducts from spurious mass differences and illuminates metabolites that form abundant salt or matrix adducts.
When you have binned mass spectrometry imaging peaks and want to understand which detected mass-to-charge ratios represent the same metabolite in different ionization states (parent vs. adduct form). Apply this skill when adduct formation is suspected to obscure metabolite identity—particularly in MALDI-MSI where matrix ions (e.g., DHB, CHCA) and sample salts (Na⁺, K⁺) readily form adducts with metabolites. Use it to recover 'dark metabolome' peaks that would otherwise be missed because they appear as distinct m/z values rather than as a single peak.
First, compute all pairwise mass differences from detected peaks using massdiff(), then bin them into a histogram with instrument-appropriate precision (typically 0.01 Da bin width). Next, match mass difference bins to a reference library of known adducts (built-in adducts or adducts2 datasets) using adductMatch() to identify candidate parent–adduct pairs. For each candidate pair, extract the actual m/z values using diffGetPeaks() with a user-specified mass window (e.g., ±0.5 mDa or ppm tolerance). Finally, test spatial correlation between the intensity profiles of paired peaks across all imaging pixels using corrPairsMSI() with default two-tailed Pearson correlation and Bonferroni-corrected significance threshold (p < 0.05). Pairs showing both mass-difference match AND significant spatial correlation (high Pearson r, low corrected p-value) are validated parent–adduct relationships; reject pairs with poor spatial correlation, as they indicate coincidental mass differences rather than chemical associations.
d <- msimat("msi.csv", sep=";"); d.diff <- massdiff(d); d.diff.hist <- hist(d.diff); d.diff.annot <- adductMatch(d.diff.hist); d.diff.annot.cor <- corrPairsMSI(d, d.diff.annot); pointsAdducts(d, subset(d.diff.annot.cor, matches=='Na adduct'), which='adduct', signif=TRUE, pch=20, col='red')
adducts and adducts2 datasets) includes only common biological adducts and matrix ions; rare or unexpected adducts will not be matched and may be missed unless a custom adduct table is supplied.msimat function). Test for spatial correlations between mass peaks in MS imaging data (imported with the msimat function).: "Test for spatial correlations between mass peaks in MS imaging data (imported with the msimat function)."diffGetPeaks function (above).: "If they are truly related by molecular adduct formation, then their abundances should be correlated."corrPairsMSIchunks instead of corrPairsMSI.: "For large data sets, where the tables would not fit into memory, it is possible to break up the problem into "chunks" processed serially."