Acquisition Method Overlap Analysis

---
name: acquisition-method-overlap-analysis
description: Use when you have acquired the same sample(s) using multiple LC-MS, LC-IMS-MS,
  or direct infusion methods (e.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3933
  edam_topics:
  - http://edamontology.org/topic_3520
  - http://edamontology.org/topic_0092
  tools:
  - IonToolPack
  - Comparador
  - Mirador
  - PeakQC
  techniques:
  - LC-MS
  - direct-infusion-MS
  - ion-mobility-MS
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1021/jasms.4c00146
  title: PeakQC
evidence_spans:
- IonToolPack is a software suite housing tools for mass spectrometry data
- IonToolPack is a software suite housing tools for mass spectrometry data.
- 'Comparador: Tool to compare lists of features (CSV files) from different acquisition
  methods or processing software'
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_peakqc_cq
    doi: 10.1021/jasms.4c00146
    title: PeakQC
  dedup_kept_from: coll_peakqc_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1021/jasms.4c00146
  all_source_dois:
  - 10.1021/jasms.4c00146
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# acquisition-method-overlap-analysis

## Summary

Harmonize and compare feature lists (m/z, retention time, identifiers) generated by different mass spectrometry acquisition methods or processing software, then quantify feature overlap and identify method-specific features. This skill surfaces agreement and divergence across workflows to validate instrument/software choices or inform feature consensus decisions.

## When to use

You have acquired the same sample(s) using multiple LC-MS, LC-IMS-MS, or direct infusion methods (e.g., different instrument vendors, DDA vs. DIA mode, or outputs from competing peak-picking software), exported feature lists in CSV format, and need to understand which features are reproducible across methods and which are method-specific artifacts or true sample features.

## When NOT to use

- Input is already a consolidated, vendor-curated feature table or a single-method acquisition result (no methodological variance to compare).
- Feature lists lack numeric m/z or retention time fields necessary for harmonization and matching.
- Comparing metabolite identifications rather than raw feature detections — use spectral library matching (e.g., TandemMatch) or annotation tools instead.

## Inputs

- Multiple CSV feature lists, each containing m/z, retention time, feature identifier, and optional intensity or quality metrics from different MS acquisition methods or processing software
- Method/software source labels (one per input CSV)
- Harmonization parameters: m/z tolerance (ppm or Da), retention time tolerance (seconds or minutes)

## Outputs

- Harmonized feature table (CSV) mapping original feature IDs to unified identifiers across all input lists
- Feature overlap report (CSV or tabular) documenting: absolute and relative overlap counts per method pair, per-feature source method tags, and method-union and method-intersection feature sets
- Comparison statistics: total unique features, percentage overlap by method, method-specific feature counts

## How to apply

Load multiple feature-list CSV files into Comparador, each tagged with its source acquisition method or software. Harmonize feature identifiers, m/z, retention time, and metadata by matching on common numeric and textual fields and resolving naming conflicts across methods. Apply cross-list comparison logic to identify overlapping features (e.g., m/z ± tolerance and RT ± tolerance matching) and method-unique features. Generate a structured comparison report documenting feature overlap statistics (absolute counts and percentages), harmonized feature IDs, source method tags for each feature, and analysis results. Use overlap metrics and source-method annotations to prioritize high-confidence features detected in multiple methods and flag single-method detections for validation.

## Related tools

- **Comparador** (Primary tool: ingests multiple feature-list CSV files, harmonizes identifiers and metadata (m/z, RT), performs cross-list overlap comparison, and outputs harmonized feature tables and overlap statistics.) — https://github.com/pnnl/IonToolPack
- **Mirador** (Upstream tool: exports raw MS data as feature lists (CSV) and visualizations (XIC, XIM heatmaps, MS/MS mirror plots) from multiple instrument formats, which serve as inputs to Comparador.) — https://github.com/pnnl/IonToolPack
- **PeakQC** (Complementary tool: automated quality control on MS1 data by PCA analysis; can flag low-quality or outlier samples before feature list comparison to reduce noise in overlap analysis.) — https://github.com/pnnl/IonToolPack

## Evaluation signals

- Harmonized feature table has no duplicate or orphaned feature IDs; each row maps to at least one original input feature.
- Overlap statistics are internally consistent: union of method-specific counts equals total features; intersection counts are ≤ all individual method counts.
- Method-source tags correctly track the origin CSV file for each feature; spot-check 5–10 high-overlap features to verify they appear in expected source methods.
- Features with high m/z and RT match confidence (within user-specified tolerances) cluster together in overlap report; features with marginal matches (near tolerance boundaries) are flagged or annotated.
- Comparison report is deterministic: running Comparador twice on the same input CSVs and parameters produces identical output tables and statistics.

## Limitations

- Harmonization relies on exact m/z and retention time matching within user-specified tolerances; systematic calibration drift or method-specific RT shifts can produce false negatives (missed overlaps). Calibration or retention-time alignment correction may be needed before comparison.
- CSV format requires manual extraction or export from vendor software; non-standard layouts or missing required columns (m/z, RT) will fail or require preprocessing.
- No built-in handling of adduct or charge-state variants (e.g., [M+H]+ vs. [M+Na]+, z=1 vs. z=2) — features differing only in adduct will not match unless explicitly harmonized upstream.
- Overlap analysis is omics-agnostic but does not account for biological or chemical context; high overlap may reflect true reproducibility or instrumental/software drift; low overlap may reflect true biological variance or method bias.

## Evidence

- [other] Comparador ingests feature lists in CSV format from different acquisition methods or processing software, applies harmonization procedures, and performs comparative analysis on the results.: "Comparador ingests feature lists in CSV format from different acquisition methods or processing software, applies harmonization procedures, and performs comparative analysis on the results."
- [other] Harmonize feature identifiers, retention time, m/z, and other metadata across lists by matching on common fields and resolving naming conflicts.: "Harmonize feature identifiers, retention time, m/z, and other metadata across lists by matching on common fields and resolving naming conflicts."
- [other] Identify overlapping and unique features across all input lists using cross-list comparison logic.: "Identify overlapping and unique features across all input lists using cross-list comparison logic."
- [other] Generate a structured comparison report (CSV or tabular format) documenting feature overlap statistics, harmonized feature IDs, source method tags, and analysis results.: "Generate a structured comparison report (CSV or tabular format) documenting feature overlap statistics, harmonized feature IDs, source method tags, and analysis results."
- [readme] Tool to compare lists of features (CSV files) from different acquisition methods or processing software, by harmonizing and analyzing results.: "Tool to compare lists of features (CSV files) from different acquisition methods or processing software, by harmonizing and analyzing results."
- [readme] It reads data from multiple instrument formats, requires no installation and provides omics agnostic functionalities (metabolomics, lipidomics, proteomics, etc.): "It reads data from multiple instrument formats, requires no installation and provides omics agnostic functionalities (metabolomics, lipidomics, proteomics, etc.)"