Quasi Molecular Adduct Filtering

quasi-molecular-adduct-filtering

Summary

Filter LC-MS feature candidates to retain only quasi-molecular ion adducts (e.g., MH, MNa, MK) after feature clustering, removing in-source fragments and low-frequency adducts. This step reduces annotation candidate noise and improves specificity before diffusion-based prioritization.

When to use

After feature clustering has been applied to co-eluting LC-MS features and mass-to-charge ratio matching to KEGG has produced an annotated table with adduct assignments. Use this skill when you need to focus on biologically plausible quasi-molecular ions and eliminate spurious in-source fragments or rare adducts that are unlikely to represent the true metabolite.

When NOT to use

• Features have not yet been clustered — cluster-based filtering requires pcgroup assignments from featuresClustering.
• You need to retain all candidate adducts (including fragments) for separate analysis — this skill explicitly removes them.
• Input is already a final ranked annotation table — filtering should occur earlier in the pipeline, before diffusion prioritization.

Inputs

• annotated feature table with KEGG candidates and adduct assignments (from matching stage)
• cluster group identifiers (pcgroup column, from featuresClustering output)
• quasi-molecular adduct list (optional; defaults to MH, MNa, MK if not supplied)
• observed adduct frequency threshold (default 0.1)

Outputs

• filtered annotation table (MH.Tab) containing only quasi-molecular ion candidates
• cluster-validated feature annotations with spurious in-source fragments removed

How to apply

Apply the mWISE clusterBased.filter function to the annotated feature table containing cluster group identifiers (pcgroup) and adduct assignments. Retain only candidates with quasi-molecular adducts (MH, MNa, MK, etc.); if no explicit quasi-molecular list is provided, the function defaults to these adducts plus any adducts with observed frequency > 0.1 (this threshold can be modified). The function processes each cluster independently, filtering out in-source fragments and low-frequency adducts (observed frequency ≤ 0.1) to produce a cleaned MH.Tab output table. This filtered table then serves as input to diffusion-based prioritization, ensuring that downstream scoring focuses on metabolically relevant ion species.

Related tools

• mWISE (Provides clusterBased.filter function to filter candidates by quasi-molecular adducts and frequency thresholds; provides featuresClustering function that generates input pcgroup assignments) — https://dev.b2s.club/b2slab/mWISE
• R (Execution environment for mWISE functions)
• CAMERA (Source of default adduct and fragment information used to build adduct tables in mWISE)
• cliqueMS (Contributes to default adduct and fragment reference tables used for classification)

Examples

MH.Tab <- clusterBased.filter(annotated_table, pcgroup_col="pcgroup", quasi_molecular_list=c("MH", "MNa", "MK"), freq_threshold=0.1)

Evaluation signals

• Output table contains only candidates with quasi-molecular adduct labels (MH, MNa, MK, etc.) — no in-source fragments or other adduct types remain.
• Adduct frequency distribution in output shows all retained adducts have observed frequency > 0.1 (or user-specified threshold).
• Number of rows in filtered table is less than or equal to the input annotated table (filtering removes or consolidates candidates).
• Cluster group (pcgroup) assignments are preserved in output and align with input feature clusters.
• Downstream diffusion prioritization produces valid probability/network scores without missing-value errors, confirming input structure is correct.

Limitations

• Default observed frequency threshold (0.1) may not be optimal for all ionization modes or instrument configurations; users should validate or adjust based on their experimental design.
• Filtering removes potentially informative fragment ions; if fragment-level analysis is needed, it must be performed on unfiltered data before this step.
• Assumes accurate prior clustering and mass-to-charge matching; errors in earlier stages (e.g., wrong pcgroup assignments, mismatched masses) propagate through filtering.
• Relies on accurate adduct annotation in the input table; if input adduct labels are misclassified, filtering will not recover them.

Evidence

• [intro] The clusterBased.filter function then filters candidates by retaining only those with quasi-molecular adducts: "The clusterBased.filter function then filters candidates by retaining only those with quasi-molecular adducts"
• [intro] retaining only features assigned to quasi-molecular adducts (MH, MNa, MK, etc.) and filtering out in-source fragments and low-frequency adducts: "retaining only features assigned to quasi-molecular adducts (MH, MNa, MK, etc.) and filtering out in-source fragments and low-frequency adducts (observed frequency ≤ 0.1 threshold)"
• [intro] If not, the quasi-molecular adducts available in mWISE, together with the adducts with an observed frequency higher than 0.1 will be used for filtering: "If not, the quasi-molecular adducts available in mWISE, together with the adducts with an observed frequency higher than 0.1 will be used for filtering"
• [intro] Apply mWISE featuresClustering function to group co-eluting features based on intensity correlation and retention time proximity. Merge the resulting cluster group (pcgroup) identifiers into the annotated table: "Apply mWISE featuresClustering function to group co-eluting features based on intensity correlation and retention time proximity. Merge the resulting cluster group (pcgroup) identifiers into the"
• [intro] Output the filtered table containing only cluster-validated quasi-molecular ion candidates: "Output the filtered table containing only cluster-validated quasi-molecular ion candidates"