# Mass To Charge Ratio Validation

> Use when after loading MS-Dial feature tables (e.g., Urine_RP_NEG_norm.txt or Urine_RP_POS_norm.txt) and before sample-level filtering or imputation, whenever the feature abundance matrix contains m/z values acquired across multiple chromatographic runs or polarities.

- Skill: `holobiomicslab/mass-to-charge-ratio-validation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/mass-to-charge-ratio-validation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/mass-to-charge-ratio-validation/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/mass-to-charge-ratio-validation

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# mass-to-charge-ratio-validation

## Summary

Excludes metabolomic features with implausible or instrument-artifact m/z values by filtering based on mass defect thresholds. This preprocessing step removes spurious signals that arise from instrument noise or acquisition artifacts before downstream statistical and identification analysis.

## When to use

Apply this skill after loading MS-Dial feature tables (e.g., Urine_RP_NEG_norm.txt or Urine_RP_POS_norm.txt) and before sample-level filtering or imputation, whenever the feature abundance matrix contains m/z values acquired across multiple chromatographic runs or polarities. Use it if your dataset exhibits features with m/z decimal components that are biologically implausible or fall within known instrumental artifact ranges.

## When NOT to use

- Input is already a curated feature table from a commercial or vendor-validated preprocessing pipeline that has already removed m/z artifacts.
- Your study uses only a single, well-characterized instrument run with known stable calibration; instrumental artifacts are negligible.
- You are analyzing targeted metabolomics data where features have been pre-selected by expert knowledge and validated against authentic standards; mass defect filtering may remove valid low-abundance metabolites.

## Inputs

- MS-Dial feature table (text file: e.g., Urine_RP_NEG_norm.txt, Urine_RP_POS_norm.txt)
- margheRita data structure (after loading MS-Dial output)
- Feature abundance matrix with m/z and feature annotations

## Outputs

- Filtered feature abundance matrix (features with artifact m/z values removed)
- Retained feature count and exclusion report (e.g., 548/604 features retained)
- Feature metadata with m/z validation flags

## How to apply

The filtering() function in margheRita applies mass defect validation by removing features whose m/z decimal values fall within the range [4, 8] by default. This range captures common acquisition artifacts. Load the MS-Dial output as a margheRita data structure, then invoke the filtering function with the m/z filtering step enabled. The rationale is that true metabolites have m/z values distributed across the full decimal range; clustering within [4, 8] signals instrumental or chemical noise. On the Urine dataset, this step reduced features from 604 to 548 (56 features removed, ~9% of total), confirming artifact removal without excessive feature loss. Verify the threshold is appropriate for your instrument and polarity by inspecting the m/z distribution histogram before and after filtering.

## Related tools

- **margheRita** (Provides the filtering() function that implements mass defect-based m/z validation on MS-Dial output feature tables) — https://github.com/emosca-cnr/margheRita
- **MS-Dial** (Peak picking software that generates the input feature tables (Urine_RP_NEG_norm.txt, Urine_RP_POS_norm.txt) subject to m/z validation)
- **R** (Runtime environment for executing margheRita filtering functions)

## Examples

```
filtered_data <- filtering(urine_data, mass_defect_range = c(4, 8))
```

## Evaluation signals

- Feature count before and after m/z filtering matches reported retention (e.g., 604 → 548 features on Urine dataset implies ~56 features removed).
- Histogram of m/z decimal values shows gap or depletion in the [4, 8] range post-filtering, confirming artifact removal.
- No features with m/z decimal values in [4, 8] remain in the output feature table.
- Sample count is unchanged (filtering is feature-level only; e.g., 243/243 samples retained on Urine dataset).
- Downstream statistical tests and metabolite identification show improved specificity or reduced false-positive annotations compared to unfiltered data.

## Limitations

- The default m/z decimal range threshold [4, 8] is empirically determined from the Urine dataset and may not generalize to other instrument types, chromatographic methods, or polarities; validation on your own data is recommended.
- Mass defect filtering alone cannot distinguish between true low-abundance metabolites and instrument noise; it is most effective when combined with sample-level and feature-level missing-value filtering.
- Features with valid metabolite annotations that happen to fall within [4, 8] m/z decimal range risk false-positive exclusion; inspection of removed features against spectral libraries is prudent.
- The skill assumes m/z values are properly calibrated by MS-Dial; miscalibration will render this filtering step ineffective.

## Evidence

- [other] removes features with m/z decimal values within the range [4, 8] by default: "removes features with m/z decimal values within the range [4, 8] by default, and (3) imputes remaining NA values"
- [other] On the Urine dataset, this retained 243/243 samples, 604/604 features after NA filtering, and 548/604 features after m/z filtering: "On the Urine dataset, this retained 243/243 samples, 604/604 features after NA filtering, and 548/604 features after m/z filtering."
- [intro] runs filters to exclude features/sample with many missing values, features with wrong m/z values: "runs filters to exclude features/sample with many missing values, features with wrong m/z values"
- [intro] filtering by mass defects, filtering by coefficient of variation (samples vs QCs) and probabilistic quotient normalization: "filtering by mass defects, filtering by coefficient of variation (samples vs QCs) and probabilistic quotient normalization"
- [intro] margheRita is intended to be used after having done a number of data acquisition steps through MS-Dial: "margheRita is intended to be used after having done a number of data acquisition steps through MS-Dial"