# Threshold Based Match Filtering

> Use when after calculating similarity metrics (e.g. cosine similarity or spectral correlation) between query peak networks and a simulated INADEQUATE database, when you need to distinguish true metabolite matches from spurious or low-confidence assignments and generate a curated list of identified.

- Skill: `holobiomicslab/threshold-based-match-filtering` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/threshold-based-match-filtering`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/threshold-based-match-filtering/raw
- Safety review: pending
- 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/threshold-based-match-filtering

---


# threshold-based-match-filtering

## Summary

Filter metabolite matches from spectral database searches by applying a similarity threshold to retain only high-confidence assignments. This skill ensures that only metabolites with sufficiently strong spectral correlation to query peak networks are reported, reducing false positives in metabolite identification.

## When to use

After calculating similarity metrics (e.g. cosine similarity or spectral correlation) between query peak networks and a simulated INADEQUATE database, when you need to distinguish true metabolite matches from spurious or low-confidence assignments and generate a curated list of identified metabolites with reliable match scores.

## When NOT to use

- When no similarity metric has yet been computed between query and database spectra—filtering requires pre-calculated scores.
- When the goal is exploratory discovery of all possible matches regardless of confidence; thresholding discards lower-scoring candidates that may be biologically relevant in some contexts.
- When the query spectral data have not been pre-processed (shifted, picked, and clustered into peak networks); filtering assumes clean, validated input peak networks.

## Inputs

- Peak network clusters (from upstream Clustering module)
- Simulated INADEQUATE metabolite database with reference spectral signatures
- Calculated similarity scores (cosine similarity or spectral correlation values) between each query peak network and database metabolites

## Outputs

- Filtered metabolite match table
- Linked peak network identifiers to assigned metabolite names
- High-confidence match scores for retained assignments

## How to apply

Calculate similarity metrics between each query peak network cluster and all reference metabolite signatures in the simulated INADEQUATE database. Define a similarity threshold appropriate to your confidence requirements and spectral data quality (the article and README do not specify a concrete threshold value). Filter the full match set to retain only those peak network–metabolite pairs exceeding this threshold. This filtering step reduces false positive identifications and produces a high-confidence metabolite assignment table. The filtered output links peak network identifiers, assigned metabolite names, and match scores, which can then be used for downstream interpretation or statistical validation.

## Related tools

- **PyINETA** (Python package that implements the complete matching and filtering workflow, including similarity calculation and threshold-based filtering of peak network–metabolite pairs) — https://github.com/edisonomics/PyINETA
- **Python** (Language in which PyINETA and similarity metric computations (cosine similarity, spectral correlation) are implemented)

## Examples

```
python <path_to_pyineta_repo>/run_pyineta.py -c config.ini -s match -o output_dir
```

## Evaluation signals

- Verify that all retained matches have similarity scores ≥ the specified threshold and all discarded matches are below it (threshold consistency check).
- Check that the output table contains only peak networks from the input cluster set and only metabolite names present in the reference database (referential integrity).
- Confirm that match scores in the filtered table are numeric, non-null, and span a reasonable range (e.g. 0–1 for normalized metrics) and are monotonically ordered if sorting is applied.
- Validate that the number of retained matches per peak network is reasonable (e.g. typically 0–5 metabolites, or domain-specific expectations) and that no peak network appears with duplicate metabolite assignments after filtering.
- Cross-check a sample of filtered assignments by manual inspection or independent spectral comparison to ensure retained matches correspond to visually plausible peak network–metabolite alignments.

## Limitations

- The article and README do not specify a recommended similarity threshold value; practitioners must determine this empirically based on their spectral quality, database composition, and acceptable false-positive rate.
- Threshold-based filtering is sensitive to the quality of the input similarity metric; poor peak picking, inadequate peak clustering, or weak database coverage will degrade match quality before filtering is applied.
- The choice of similarity metric (cosine similarity vs. spectral correlation vs. other measures) is not elaborated in the article; different metrics may produce different threshold-dependent results.
- No changelog or version-specific performance notes are provided, limiting guidance on how filtering behavior may vary across PyINETA versions.

## Evidence

- [other] Calculate similarity metrics (e.g., cosine similarity or spectral correlation) between each query peak network and database metabolite signatures.: "Calculate similarity metrics (e.g., cosine similarity or spectral correlation) between each query peak network and database metabolite signatures."
- [other] Filter matches using a similarity threshold to retain high-confidence metabolite assignments.: "Filter matches using a similarity threshold to retain high-confidence metabolite assignments."
- [other] Generate a matched metabolite output table linking peak network identifiers, assigned metabolite names, and match scores using the pyINETA Matching module.: "Generate a matched metabolite output table linking peak network identifiers, assigned metabolite names, and match scores using the pyINETA Matching module."
- [readme] pyINETA matches identified peak networks to a simulated INADEQUATE database of metabolites to identify metabolites present in the query INADEQUATE spectra: "matches to a simulated INADEQUATE database of metabolites to identify metabolites present in the query INADEQUATE spectra"
- [methods] Matching
--------
.. automodule:: pyineta.matching: "Matching
--------
.. automodule:: pyineta.matching"