ranked-annotation-prioritization
Summary
This skill reconstructs the diffusion prioritization stage of metabolomics annotation, which combines network-propagated diffusion scores with recovered peaks to build a final ranked compound annotation table. It is essential for converting cluster-filtered KEGG candidates into a context-aware, metabolite-network-informed ranking that prioritizes biologically plausible annotations.
When to use
Apply this skill when you have completed cluster-based filtering of KEGG candidate assignments in untargeted LC-MS metabolomics and need to rank those candidates by biological plausibility using a metabolite interaction network. Specifically, use it after clusterBased.filter has assigned quasi-molecular adducts to feature clusters, but before delivering a final annotated peak table to the user.
When NOT to use
- Input is already a fully annotated feature table or peak-intensity matrix; this skill applies only after cluster-based candidate filtering
- No KEGG metabolite network graph is available; diffusion propagation requires a connected network representation
- All peaks have been retained by cluster-based filtering and no recovery step is needed (skill is still applicable but the recovery step would be trivial)
Inputs
- Cluster-filtered candidate assignments (output from clusterBased.filter)
- Sample FELLA graph (igraph object representing KEGG metabolite network)
- Feature-to-cluster mapping with intensity information
Outputs
- Ranked.Tab: final ranked annotation table sorted by diffusion-prioritized scores
- Recovered peaks: peaks restored from complete removal by cluster-based filtering
How to apply
The diffusion prioritization workflow operates in five steps. First, compute diffusion input scores from the cluster-filtered candidates using the diffusion.input function with input.type set to either 'probability' (soft scores) or 'binary' (hard assignments). Second, execute set.diffusion on the FELLA sample.graph to propagate scores through the metabolite network, allowing metabolites connected to high-scoring candidates to inherit signal. Third, apply z-score normalization to the diffusion scores to account for network topology and degree bias (setting scores = z rather than raw). Fourth, use recoveringPeaks to restore peaks that were completely removed by cluster-based filtering, merging them back by compound identifier. Fifth, compile and rank the final results using the finalResults function with z-score normalization, producing the Ranked.Tab output sorted by diffusion score.
Related tools
- mWISE (R package providing diffusion.input, set.diffusion, recoveringPeaks, and finalResults functions for ranked prioritization) — https://dev.b2s.club/b2slab/mWISE
- FELLA (Provides the sample.graph metabolite network (igraph object) used for diffusion score propagation)
- igraph (Graph manipulation library; used to construct and traverse the undirected metabolite network)
- R (Execution environment for mWISE functions and statistical normalization (z-score calculation))
Examples
diffusion.scores <- set.diffusion(diffusion.input(candidates, input.type='probability'), sample.graph); recovered <- recoveringPeaks(filtered.candidates); final <- finalResults(merge(diffusion.scores, recovered, by='compound_id'), scores='z')
Evaluation signals
- Diffusion scores are computed for all cluster-filtered candidates and fall within a biologically plausible range (typically normalized to z-scores with mean 0, SD 1)
- Recovered peaks are successfully merged back into the final table by compound identifier with no duplicates or missing values
- The Ranked.Tab output is sorted in descending order by diffusion score, with the highest-scoring candidate per peak ranked first
- Z-score normalization has been applied; raw diffusion scores should NOT appear in the final output
- The final table schema includes columns for mass-to-charge ratio, cluster ID, compound identifier, diffusion score, and rank position
Limitations
- Diffusion prioritization depends entirely on the quality and completeness of the underlying KEGG metabolite network; missing or misannotated edges may bias propagation
- Peaks completely removed by cluster-based filtering are recovered, but their diffusion scores may be lower due to lack of supporting evidence in the filtered candidate set
- Z-score normalization assumes the distribution of diffusion scores approximates a normal distribution; severely skewed networks may produce unintuitive rankings
- The method is sensitive to the
input.type parameter (probability vs. binary); choice of input mode can substantially affect final rankings
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] The
recoveringPeaks function recovers the peaks that have been completely removed by the cluster-based filter.: "The recoveringPeaks function recovers the peaks that have been completely removed by the cluster-based filter."
- [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, and building a final prioritized list using diffusion in networks: "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)"
1---2name: ranked-annotation-prioritization3description: Use when you have completed cluster-based filtering of KEGG candidate assignments in untargeted LC-MS metabolomics and need to rank those candidates by biological plausibility using a metabolite interaction network. Specifically, use it after `clusterBased.4license: CC-BY-4.05---67# ranked-annotation-prioritization89## Summary1011This skill reconstructs the diffusion prioritization stage of metabolomics annotation, which combines network-propagated diffusion scores with recovered peaks to build a final ranked compound annotation table. It is essential for converting cluster-filtered KEGG candidates into a context-aware, metabolite-network-informed ranking that prioritizes biologically plausible annotations.1213## When to use1415Apply this skill when you have completed cluster-based filtering of KEGG candidate assignments in untargeted LC-MS metabolomics and need to rank those candidates by biological plausibility using a metabolite interaction network. Specifically, use it after `clusterBased.filter` has assigned quasi-molecular adducts to feature clusters, but before delivering a final annotated peak table to the user.1617## When NOT to use1819- Input is already a fully annotated feature table or peak-intensity matrix; this skill applies only after cluster-based candidate filtering20- No KEGG metabolite network graph is available; diffusion propagation requires a connected network representation21- All peaks have been retained by cluster-based filtering and no recovery step is needed (skill is still applicable but the recovery step would be trivial)2223## Inputs2425- Cluster-filtered candidate assignments (output from clusterBased.filter)26- Sample FELLA graph (igraph object representing KEGG metabolite network)27- Feature-to-cluster mapping with intensity information2829## Outputs3031- Ranked.Tab: final ranked annotation table sorted by diffusion-prioritized scores32- Recovered peaks: peaks restored from complete removal by cluster-based filtering3334## How to apply3536The diffusion prioritization workflow operates in five steps. First, compute diffusion input scores from the cluster-filtered candidates using the `diffusion.input` function with `input.type` set to either 'probability' (soft scores) or 'binary' (hard assignments). Second, execute `set.diffusion` on the FELLA sample.graph to propagate scores through the metabolite network, allowing metabolites connected to high-scoring candidates to inherit signal. Third, apply z-score normalization to the diffusion scores to account for network topology and degree bias (setting `scores = z` rather than `raw`). Fourth, use `recoveringPeaks` to restore peaks that were completely removed by cluster-based filtering, merging them back by compound identifier. Fifth, compile and rank the final results using the `finalResults` function with z-score normalization, producing the `Ranked.Tab` output sorted by diffusion score.3738## Related tools3940- **mWISE** (R package providing diffusion.input, set.diffusion, recoveringPeaks, and finalResults functions for ranked prioritization) — https://dev.b2s.club/b2slab/mWISE41- **FELLA** (Provides the sample.graph metabolite network (igraph object) used for diffusion score propagation)42- **igraph** (Graph manipulation library; used to construct and traverse the undirected metabolite network)43- **R** (Execution environment for mWISE functions and statistical normalization (z-score calculation))4445## Examples4647```48diffusion.scores <- set.diffusion(diffusion.input(candidates, input.type='probability'), sample.graph); recovered <- recoveringPeaks(filtered.candidates); final <- finalResults(merge(diffusion.scores, recovered, by='compound_id'), scores='z')49```5051## Evaluation signals5253- Diffusion scores are computed for all cluster-filtered candidates and fall within a biologically plausible range (typically normalized to z-scores with mean 0, SD 1)54- Recovered peaks are successfully merged back into the final table by compound identifier with no duplicates or missing values55- The Ranked.Tab output is sorted in descending order by diffusion score, with the highest-scoring candidate per peak ranked first56- Z-score normalization has been applied; raw diffusion scores should NOT appear in the final output57- The final table schema includes columns for mass-to-charge ratio, cluster ID, compound identifier, diffusion score, and rank position5859## Limitations6061- Diffusion prioritization depends entirely on the quality and completeness of the underlying KEGG metabolite network; missing or misannotated edges may bias propagation62- Peaks completely removed by cluster-based filtering are recovered, but their diffusion scores may be lower due to lack of supporting evidence in the filtered candidate set63- Z-score normalization assumes the distribution of diffusion scores approximates a normal distribution; severely skewed networks may produce unintuitive rankings64- The method is sensitive to the `input.type` parameter (probability vs. binary); choice of input mode can substantially affect final rankings6566## Evidence6768- [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"69- [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`."70- [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."71- [intro] The `recoveringPeaks` function recovers the peaks that have been completely removed by the cluster-based filter.: "The `recoveringPeaks` function recovers the peaks that have been completely removed by the cluster-based filter."72- [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, and building a final prioritized list using diffusion in networks: "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)"
HolobiomicsLab