isotopic-peak-removal
Summary
Remove isotopic peaks (primarily ¹³C) from FT-ICR MS peak-abundance matrices to eliminate redundant molecular signals and reduce noise before downstream analysis. This filtering step prevents overrepresentation of naturally occurring isotopic variants of the same compound.
When to use
Apply this skill after loading raw FT-ICR MS peak lists with assigned molecular formulas when you have detected peaks across multiple m/z values that correspond to isotopic variants of the same parent compound. This is especially critical when the downstream analysis goal is compound-level (rather than isotope-level) interpretation, such as chemodiversity assessment, biochemical transformation networks, or statistical comparison of molecular compositions across samples.
When NOT to use
- The goal is isotope-ratio analysis or tracing (e.g., ¹³C labeling experiments); isotope peaks are the primary signal of interest.
- Input data are already de-isotoped or derived from tandem MS with neutral loss annotation; redundant isotope removal may have already occurred upstream.
- Raw m/z values are provided without assigned molecular formulas; isotopic peak identification requires formula assignment.
Inputs
- CSV file containing detected peaks with columns: m/z values, molecular formulas, peak intensities, isotopic annotations or isotopic carbon labels
Outputs
- Filtered peak-abundance matrix (CSV) with isotopic peaks removed, retaining only monoisotopic peaks for each unique molecular formula
How to apply
Iterate through the detected peaks and identify isotopic variants by checking the peak annotations for isotopic carbon labeling (¹³C) or equivalent isotopic markers in the assigned molecular formula field. Remove all peaks flagged as isotopic variants, retaining only the monoisotopic (M) peak for each compound. The rationale is that ¹³C peaks are naturally occurring and predictable from elemental composition; including them inflates apparent molecular diversity and can bias subsequent normalization and statistical analyses. This filtering must occur before m/z-based and error-threshold filtering to avoid propagating redundant signals through the pipeline.
Related tools
- pandas (Load, filter, and manipulate the peak CSV file to identify and remove rows with isotopic peak annotations)
- Formularity (Assign and annotate molecular formulas to m/z peaks, providing the isotopic carbon and isotopic labeling information needed to flag isotopic variants)
Evaluation signals
- Peak count decreases by a predictable fraction (typically 1–5% for natural samples, reflecting the expected ¹³C abundance); large deletions suggest over-filtering.
- Remaining peaks are all monoisotopic forms; verify no peaks with ¹³C, ²H, ¹⁸O, or other isotopic labels remain in the output matrix.
- Elemental composition (e.g., number of carbons) of retained peaks is stable across filtering; no systematic bias toward lighter or heavier formulas.
- Downstream diversity metrics (e.g., NOSC, DBE distributions) and Van Krevelen diagrams show expected molecular composition without artificial clustering at +1.003 m/z intervals.
- Comparison with external reference datasets (e.g., known metabolite databases) confirms that retained peaks match expected monoisotopic masses within measurement tolerance (0.5 ppm).
Limitations
- Isotopic peak detection depends on accurate molecular formula assignment; if formula errors are high (>0.5 ppm), isotopic variants may be misidentified or missed.
- Natural samples with high carbon content will have higher natural ¹³C abundance; filtering may remove a larger proportion of peaks than in low-carbon samples, potentially introducing composition bias.
- The skill assumes that isotopic annotations are unambiguous in the input CSV; if multiple isotopic labels are present or mixed with adducts/fragments, manual curation or post-filtering validation is required.
- Does not address isotopic interference from multiply charged ions or unresolved doublets; requires sufficient mass resolution from the FT-ICR instrument.
Evidence
- [other] Filter peaks by m/z range (user-defined) and remove isotopic peaks (13C).: "Filter peaks by m/z range (user-defined) and remove isotopic peaks (13C)."
- [methods] detected peaks are filtered by their m/z values (based on the user's input): "detected peaks are filtered by their m/z values (based on the user's input)"
- [methods] isotopic presence (13C peaks): "isotopic presence (13C peaks)"
- [other] MetaboDirect applies multiple sequential filters (isotopic carbon removal, m/z-based filtering, formula assignment error thresholds): "applies multiple sequential filters (isotopic carbon removal, m/z-based filtering, formula assignment error thresholds)"
1---2name: isotopic-peak-removal3description: Use when after loading raw FT-ICR MS peak lists with assigned molecular formulas when you have detected peaks across multiple m/z values that correspond to isotopic variants of the same parent compound.4license: CC-BY-4.05---67# isotopic-peak-removal89## Summary1011Remove isotopic peaks (primarily ¹³C) from FT-ICR MS peak-abundance matrices to eliminate redundant molecular signals and reduce noise before downstream analysis. This filtering step prevents overrepresentation of naturally occurring isotopic variants of the same compound.1213## When to use1415Apply this skill after loading raw FT-ICR MS peak lists with assigned molecular formulas when you have detected peaks across multiple m/z values that correspond to isotopic variants of the same parent compound. This is especially critical when the downstream analysis goal is compound-level (rather than isotope-level) interpretation, such as chemodiversity assessment, biochemical transformation networks, or statistical comparison of molecular compositions across samples.1617## When NOT to use1819- The goal is isotope-ratio analysis or tracing (e.g., ¹³C labeling experiments); isotope peaks are the primary signal of interest.20- Input data are already de-isotoped or derived from tandem MS with neutral loss annotation; redundant isotope removal may have already occurred upstream.21- Raw m/z values are provided without assigned molecular formulas; isotopic peak identification requires formula assignment.2223## Inputs2425- CSV file containing detected peaks with columns: m/z values, molecular formulas, peak intensities, isotopic annotations or isotopic carbon labels2627## Outputs2829- Filtered peak-abundance matrix (CSV) with isotopic peaks removed, retaining only monoisotopic peaks for each unique molecular formula3031## How to apply3233Iterate through the detected peaks and identify isotopic variants by checking the peak annotations for isotopic carbon labeling (¹³C) or equivalent isotopic markers in the assigned molecular formula field. Remove all peaks flagged as isotopic variants, retaining only the monoisotopic (M) peak for each compound. The rationale is that ¹³C peaks are naturally occurring and predictable from elemental composition; including them inflates apparent molecular diversity and can bias subsequent normalization and statistical analyses. This filtering must occur before m/z-based and error-threshold filtering to avoid propagating redundant signals through the pipeline.3435## Related tools3637- **pandas** (Load, filter, and manipulate the peak CSV file to identify and remove rows with isotopic peak annotations)38- **Formularity** (Assign and annotate molecular formulas to m/z peaks, providing the isotopic carbon and isotopic labeling information needed to flag isotopic variants)3940## Evaluation signals4142- Peak count decreases by a predictable fraction (typically 1–5% for natural samples, reflecting the expected ¹³C abundance); large deletions suggest over-filtering.43- Remaining peaks are all monoisotopic forms; verify no peaks with ¹³C, ²H, ¹⁸O, or other isotopic labels remain in the output matrix.44- Elemental composition (e.g., number of carbons) of retained peaks is stable across filtering; no systematic bias toward lighter or heavier formulas.45- Downstream diversity metrics (e.g., NOSC, DBE distributions) and Van Krevelen diagrams show expected molecular composition without artificial clustering at +1.003 m/z intervals.46- Comparison with external reference datasets (e.g., known metabolite databases) confirms that retained peaks match expected monoisotopic masses within measurement tolerance (0.5 ppm).4748## Limitations4950- Isotopic peak detection depends on accurate molecular formula assignment; if formula errors are high (>0.5 ppm), isotopic variants may be misidentified or missed.51- Natural samples with high carbon content will have higher natural ¹³C abundance; filtering may remove a larger proportion of peaks than in low-carbon samples, potentially introducing composition bias.52- The skill assumes that isotopic annotations are unambiguous in the input CSV; if multiple isotopic labels are present or mixed with adducts/fragments, manual curation or post-filtering validation is required.53- Does not address isotopic interference from multiply charged ions or unresolved doublets; requires sufficient mass resolution from the FT-ICR instrument.5455## Evidence5657- [other] Filter peaks by m/z range (user-defined) and remove isotopic peaks (13C).: "Filter peaks by m/z range (user-defined) and remove isotopic peaks (13C)."58- [methods] detected peaks are filtered by their m/z values (based on the user's input): "detected peaks are filtered by their m/z values (based on the user's input)"59- [methods] isotopic presence (13C peaks): "isotopic presence (13C peaks)"60- [other] MetaboDirect applies multiple sequential filters (isotopic carbon removal, m/z-based filtering, formula assignment error thresholds): "applies multiple sequential filters (isotopic carbon removal, m/z-based filtering, formula assignment error thresholds)"
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