Kegg Candidate Network Integration

KEGG Candidate Network Integration

Summary

Integrate KEGG metabolite candidates into a metabolic network graph and apply diffusion-based scoring to prioritize annotations by propagating confidence scores through network topology. This skill transforms a flat list of candidate compounds into a ranked, network-contextualized annotation table.

When to use

Apply this skill after cluster-based filtering has produced a set of candidate KEGG compounds for each feature cluster in untargeted LC-MS data, and you need to rank these candidates by their metabolic plausibility using network context rather than mass accuracy alone. Use when you have both filtered KEGG candidates and access to a metabolite interaction network (e.g., FELLA graph).

When NOT to use

• Input is a pre-ranked or manually curated annotation table — network integration will not add value.
• No metabolite network graph is available or applicable to your compound class (e.g., synthetic small molecules not in KEGG).
• Candidate set is extremely sparse or fragmented across disconnected network components; diffusion will not propagate meaningful scores.

Inputs

• Filtered candidate assignments (cluster → KEGG compound mappings)
• Feature intensity matrix or feature clustering output
• Metabolite network graph (igraph object, typically FELLA sample.graph)
• Adduct/fragment annotation table (from prior matching stage)

Outputs

• Ranked annotation table (Ranked.Tab) with columns: peak identifier, KEGG compound ID, diffusion score, z-score normalized confidence, network-integrated rank
• Recovered peaks list (compounds flagged as completely removed by filtering but recovered via network)

How to apply

Compute diffusion input scores from filtered candidate assignments using the diffusion.input function with input.type set to either 'probability' (weighted by match quality) or 'binary' (presence/absence). Load or construct a metabolite network graph (typically FELLA's KEGG-derived graph via igraph). Execute set.diffusion on the network to propagate candidate scores through the graph topology, accounting for metabolic relationships. Apply z-score normalization to the diffusion output to account for graph structure (nodes in highly connected regions receive different baseline scores). Recover any peaks completely removed by prior filtering using recoveringPeaks, then merge recovered peaks with diffusion results by compound identifier. Finally, compile and rank results using the finalResults function to produce a Ranked.Tab table sorted by network-integrated confidence.

Related tools

• FELLA (Provides pre-built KEGG metabolite network graph (sample.graph) and set.diffusion function for network propagation of scores)
• igraph (Underlying graph representation and manipulation; converts network to undirected format for diffusion propagation)
• mWISE (R package containing diffusion.input, recoveringPeaks, finalResults functions; orchestrates candidate-to-network integration workflow) — https://dev.b2s.club/b2slab/mWISE
• R (Execution environment for network graph operations and diffusion calculations)

Examples

diffusion_scores <- set.diffusion(sample.graph, diffusion.input(filtered_candidates, input.type='probability')); recovered <- recoveringPeaks(filtered_candidates, prior_removed); final_ranks <- finalResults(diffusion_scores, recovered, scores='z')

Evaluation signals

• Ranked.Tab contains no NaN or infinite values in diffusion or z-score columns; all rows correspond to input feature clusters or recovered peaks.
• Diffusion scores show expected topology-dependent variation: compounds in highly connected network regions have different baseline scores than peripheral compounds, even when input confidence is identical.
• Z-score normalization produces mean ≈ 0 and standard deviation ≈ 1 across the full result set, confirming statistical scaling.
• Recovered peaks are a non-empty subset of candidates completely filtered out in the prior stage; their re-appearance can be validated against the filtering log.
• Final rank order shows network-plausible compounds (e.g., cofactors, substrates) ranked higher than chemically similar but metabolically isolated candidates when both have similar input scores.

Limitations

• Diffusion results depend critically on network completeness and accuracy; KEGG coverage gaps or outdated network versions will propagate through rankings.
• Z-score normalization may over-penalize or over-reward candidates in sparsely connected regions; results may not be intuitive for rare or orphan metabolites.
• Binary vs. probability input.type choice significantly affects output; no automated selection is provided — user must justify choice based on upstream confidence model.
• Network diffusion assumes metabolic plausibility correlates with graph proximity; this assumption breaks for isomers, xenobiotics, and non-KEGG compounds.

Evidence

• [intro] The diffusion prioritization stage operates by: (1) computing diffusion input from filtered candidates using diffusion.input; (2) applying set.diffusion with z-score normalization on the FELLA graph to obtain diffusion scores; (3) recovering completely removed peaks with recoveringPeaks; (4) merging the recovered peaks with diffusion results by compound identifier; and (5) building a final ranked table using finalResults function with z-score normalization.: "The diffusion prioritization stage operates by: (1) computing diffusion input from filtered candidates using diffusion.input; (2) applying set.diffusion with z-score normalization on the FELLA"
• [intro] The different diffusion inputs can be computed using the diffusion.input function. The input.type argument can be set to probability or binary.: "The different diffusion inputs can be computed using the diffusion.input function. The input.type argument can be set to probability or binary."
• [intro] The z score normalizes the diffusion scores by taking into account the topology of the graph. On the other hand, when scores = raw, no normalization is applied.: "The z score normalizes the diffusion scores by taking into account the topology of the graph. On the other hand, when scores = raw, no normalization is applied."
• [intro] we will now use the sample graph provided by FELLA R package: "we will now use the sample graph provided by FELLA R package"
• [intro] mWISE integrates several strategies to provide a fast annotation of peak-intensity tables. It consists of three main steps aimed at i) matching mass-to-charge ratio values to KEGG database, ii) clustering and filtering the potential KEGG candidates, iii) building a final prioritized list using diffusion in networks.: "building a final prioritized list using diffusion in networks"