Acquisition method overlap analysis

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.)"