network-component-identification-and-filtering
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Extract and filter distinct network components (connected components, cliques, and high-similarity node pairs) from MS/MS molecular networks, applying cosine similarity and minimum size thresholds to isolate spectra groups suitable for pattern explanation analysis.
When to use
Apply this skill when you have a GNPS GraphML molecular network and need to isolate cohesive subsets of spectra (components) before analyzing which fragmentation patterns best explain them. Use it when your research requires ranking patterns by their ability to explain specific network-defined groups of spectra with high recall and precision.
When NOT to use
- Network has not yet been generated; construct molecular networks first using cosine similarity on raw MS/MS spectra.
- You are analyzing individual spectra without a network context; use this only on networked, similarity-linked spectrum collections.
- The precursor ion m/z values or spectral metadata are missing; component extraction requires node metadata for interpretation.
Inputs
- GNPS molecular network as GraphML file
- igraph object representation of the network
- minimum component size threshold (integer)
- cosine similarity threshold (0–1 range, typically ≥0.9 for high-similarity pairs)
Outputs
- list of connected components with member node IDs
- list of maximal cliques with member node IDs
- list of high-similarity node pairs meeting cosine threshold
- component membership table (node → component ID mapping)
- optionally, updated igraph object with component annotations
How to apply
Load the molecular network as an igraph object using the igraph package. Extract three types of components: connected components of the full graph, maximal cliques with minimum size ≥3, and high-similarity pairs of nodes with cosine score threshold ≥0.9. For each component type, apply size filtering (minSize parameter) to retain only components of biological relevance. Filter edges by cosine similarity to ensure component membership reflects spectral relatedness. Export the filtered component assignments (node lists and their membership labels) for downstream pattern-explanation ranking.
Related tools
- igraph (Parse GraphML network, extract connected components, cliques, and apply graph filters for component identification)
- GNPS (Generate the molecular network by cosine similarity clustering of MS/MS spectra; provides the input GraphML network structure)
- mineMS2 (Accept extracted network components and rank fragmentation patterns by recall, precision, and size metrics to identify best-explaining patterns per component) — https://github.com/odisce/mineMS2
Examples
findGNPSComponents(molnet.igraph, minSize=3, pairThreshold=0.9)
Evaluation signals
- Component membership is non-overlapping within a component type (connected components are mutually exclusive; cliques may overlap but are correctly identified as maximal).
- All nodes in a component meet the cosine similarity threshold; verify by spot-checking edge scores in the original network.
- Component size distribution reflects biological structure: connected components typically larger, high-similarity pairs small (n=2) but highly specific.
- Component assignments are stable across repeated extractions (deterministic given fixed thresholds and network version).
- Downstream pattern-explanation F1-scores correlate with component homogeneity: well-separated components allow high-precision pattern explanations.
Limitations
- Component extraction is sensitive to cosine similarity threshold (default 0.9 for pairs); lowering increases pair counts but may introduce noise.
- Minsize filtering discards small but biologically relevant components; balance between statistical power and preservation of rare metabolite clusters.
- Network topology is fixed at extraction time; dynamic updates (new spectra added) require re-extraction of all components.
- Clique-based components may miss hierarchical or fuzzy cluster structures present in the spectrum space but not captured by strict maximal cliques.
Evidence
- [intro] We consider 3 type of components of the network: the connected components of the graph, the cliques, the high similarity pairs of nodes: "We consider 3 type of components of the network: the connected components of the graph, the cliques, the high similarity pairs of nodes"
- [intro] The molecular network is read using the igraph package: "The molecular network is read using the igraph package"
- [intro] findGNPSComponents with minSize threshold and cosine similarity filtering: "Extract network components using findGNPSComponents with minSize threshold and cosine similarity filtering"
- [intro] the high similarity pairs of nodes (pairs of spectra with a cosine score superior to a threshold): "the high similarity pairs of nodes (pairs of spectra with a cosine score superior to a threshold)"
- [intro] Select cliques with minimum size threshold: "findGNPSComponents(molnet.igraph, minSize = 3, pairThreshold = 0.9, ...)"
1---2name: network-component-identification-and-filtering-23description: Use when you have a GNPS GraphML molecular network and need to isolate cohesive subsets of spectra (components) before analyzing which fragmentation patterns explain them.4license: CC-BY-4.05---67# network-component-identification-and-filtering89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112Extract and filter distinct network components (connected components, cliques, and high-similarity node pairs) from MS/MS molecular networks, applying cosine similarity and minimum size thresholds to isolate spectra groups suitable for pattern explanation analysis.1314## When to use1516Apply this skill when you have a GNPS GraphML molecular network and need to isolate cohesive subsets of spectra (components) before analyzing which fragmentation patterns best explain them. Use it when your research requires ranking patterns by their ability to explain specific network-defined groups of spectra with high recall and precision.1718## When NOT to use1920- Network has not yet been generated; construct molecular networks first using cosine similarity on raw MS/MS spectra.21- You are analyzing individual spectra without a network context; use this only on networked, similarity-linked spectrum collections.22- The precursor ion m/z values or spectral metadata are missing; component extraction requires node metadata for interpretation.2324## Inputs2526- GNPS molecular network as GraphML file27- igraph object representation of the network28- minimum component size threshold (integer)29- cosine similarity threshold (0–1 range, typically ≥0.9 for high-similarity pairs)3031## Outputs3233- list of connected components with member node IDs34- list of maximal cliques with member node IDs35- list of high-similarity node pairs meeting cosine threshold36- component membership table (node → component ID mapping)37- optionally, updated igraph object with component annotations3839## How to apply4041Load the molecular network as an igraph object using the igraph package. Extract three types of components: connected components of the full graph, maximal cliques with minimum size ≥3, and high-similarity pairs of nodes with cosine score threshold ≥0.9. For each component type, apply size filtering (minSize parameter) to retain only components of biological relevance. Filter edges by cosine similarity to ensure component membership reflects spectral relatedness. Export the filtered component assignments (node lists and their membership labels) for downstream pattern-explanation ranking.4243## Related tools4445- **igraph** (Parse GraphML network, extract connected components, cliques, and apply graph filters for component identification)46- **GNPS** (Generate the molecular network by cosine similarity clustering of MS/MS spectra; provides the input GraphML network structure)47- **mineMS2** (Accept extracted network components and rank fragmentation patterns by recall, precision, and size metrics to identify best-explaining patterns per component) — https://github.com/odisce/mineMS24849## Examples5051```52findGNPSComponents(molnet.igraph, minSize=3, pairThreshold=0.9)53```5455## Evaluation signals5657- Component membership is non-overlapping within a component type (connected components are mutually exclusive; cliques may overlap but are correctly identified as maximal).58- All nodes in a component meet the cosine similarity threshold; verify by spot-checking edge scores in the original network.59- Component size distribution reflects biological structure: connected components typically larger, high-similarity pairs small (n=2) but highly specific.60- Component assignments are stable across repeated extractions (deterministic given fixed thresholds and network version).61- Downstream pattern-explanation F1-scores correlate with component homogeneity: well-separated components allow high-precision pattern explanations.6263## Limitations6465- Component extraction is sensitive to cosine similarity threshold (default 0.9 for pairs); lowering increases pair counts but may introduce noise.66- Minsize filtering discards small but biologically relevant components; balance between statistical power and preservation of rare metabolite clusters.67- Network topology is fixed at extraction time; dynamic updates (new spectra added) require re-extraction of all components.68- Clique-based components may miss hierarchical or fuzzy cluster structures present in the spectrum space but not captured by strict maximal cliques.6970## Evidence7172- [intro] We consider 3 type of components of the network: the connected components of the graph, the cliques, the high similarity pairs of nodes: "We consider 3 type of components of the network: the connected components of the graph, the cliques, the high similarity pairs of nodes"73- [intro] The molecular network is read using the igraph package: "The **molecular network** is read using the *igraph* package"74- [intro] findGNPSComponents with minSize threshold and cosine similarity filtering: "Extract network components using findGNPSComponents with minSize threshold and cosine similarity filtering"75- [intro] the high similarity pairs of nodes (pairs of spectra with a cosine score superior to a threshold): "the **high similarity pairs of nodes** (pairs of spectra with a cosine score superior to a threshold)"76- [intro] Select cliques with minimum size threshold: "findGNPSComponents(molnet.igraph, minSize = 3, pairThreshold = 0.9, ...)"
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