Compound Identifier Aggregation

Compound Identifier Aggregation

Summary

Harmonize and deduplicate metabolite compound identifiers across multiple studies by converting diverse ID formats (chemical name, InChI, InChIKey, SMILES) to a common standard (PubChem ID) and detecting duplicates. This enables meaningful meta-analysis by ensuring each metabolite is counted only once across study results.

When to use

You have metabolomics results from multiple studies with compound identifiers in heterogeneous formats (names, InChI strings, SMILES, ChEBI/KEGG/HMDB codes) and need to merge datasets for vote-counting or meta-analysis. Apply this skill before computing consensus measures (e.g., vote-counting or weighted p-value combination) to avoid artificially inflating sample size or misattributing results to distinct metabolites.

When NOT to use

• Identifiers are already standardized to a single namespace (e.g., all KEGG IDs or all PubChem IDs with no aliases) — check_names will still work but adds unnecessary latency.
• Your compound list is already deduplicated and you are only performing within-study analysis (not cross-study meta-analysis); identifier harmonization is critical only for multi-study consensus.
• Internet access to PubChem is unavailable; webchem queries will fail and check_names cannot complete.

Inputs

• Harmonized metabolite dataset (CSV, XLS, or TXT) with columns: compound identifier (any format), p-value or trend label, fold-change, study sample size (N), and bibliographic reference
• Pre-loaded amanida data frame from amanida_read() with mode='quan' or mode='qual'

Outputs

• Deduplicated amanida data frame with PubChem IDs, duplicate flags, and harmonized compound records
• Compound descriptor table (optional, if comp.inf=T) including PubChem ID, molecular formula, molecular weight, SMILES, InChIKey, KEGG, ChEBI, HMDB, and Drugbank identifiers

How to apply

Use the amanida check_names function on your harmonized dataset (loaded via amanida_read with columns: identifier, p-value or trend label, fold-change, study size N, and reference). The function queries PubChem via the webchem package to convert all identifiers to PubChem IDs, then automatically detects and flags duplicates. Set comp.inf = T in subsequent analysis to retrieve and attach molecular descriptors (formula, weight, SMILES, InChIKey, KEGG, ChEBI, HMDB, Drugbank) to each deduplicated record. This transformation ensures downstream vote-counting and meta-analysis operate on a single, standardized compound namespace.

Related tools

• amanida (R package providing check_names() function for PubChem ID conversion and duplicate detection, and amanida_read() for loading harmonized datasets) — https://github.com/mariallr/amanida
• webchem (R package queried by amanida's check_names to retrieve and convert compound identifiers to PubChem IDs)
• PubChem (Public chemical compound database serving as the standard namespace for identifier conversion and metadata retrieval)

Examples

datafile <- amanida_read(input_file, mode = "quan", coln = c("Compound Name", "P-value", "Fold-change", "N total", "References"), separator=";"); datafile <- check_names(datafile)

Evaluation signals

• All identifiers in the output are in PubChem ID format (numeric); no heterogeneous ID formats remain.
• Duplicate compound records are flagged and consolidated; the number of unique compounds in the output matches the expected deduplicated count.
• When comp.inf=T, each record includes non-null molecular formula, weight, and cross-database identifiers (KEGG, ChEBI, HMDB); missing descriptors indicate PubChem lookup failure for that compound.
• Comparison of pre- and post-harmonization record counts shows expected reduction; no data loss for non-duplicate records.
• Subsequent vote-counting or meta-analysis results are reproducible across runs (same deduplicated set produces same consensus votes or p-values).

Limitations

• PubChem database coverage is incomplete for rare or novel compounds; identifiers without PubChem matches are dropped or retained as-is, reducing harmonization completeness.
• Identical chemical structures with different stereochemistry or ionization states may map to distinct PubChem IDs, causing artificial splitting rather than merging; manual review of flagged duplicates is recommended.
• webchem queries depend on internet connectivity and PubChem API availability; high-throughput queries may be rate-limited or fail transiently.
• Only identifier formats supported by PubChem and webchem (chemical name, InChI, InChIKey, SMILES) are reliably converted; proprietary or institutional metabolite codes may not harmonize.

Evidence

• [intro] Identifier conversion and deduplication process: "The IDs in format chemical name, InChI, InChIKey, and SMILES are searched in PubChem to transform all into a common nomenclature, PubChem ID. Then duplicates are searched"
• [intro] check_names function purpose and usage: "Before the meta-analysis the IDs can be checked using public databases information. The IDs in format chemical name, InChI, InChIKey, and SMILES are searched in PubChem to transform all into a common"
• [intro] Compound descriptor retrieval with comp.inf parameter: "If you select the option comp.inf = T the package will retrieve the PubChem ID from the ID using webchem. Results will return the following information: PubChem ID, Molecular Formula, Molecular"
• [intro] Metadata output from identifier harmonization: "Results will return the following information: PubChem ID, Molecular Formula, Molecular Weight, SMILES, InChIKey, KEGG, ChEBI, HMDB, Drugbank"
• [readme] Integration with downstream meta-analysis workflow: "In this step you will obtain an S4 object with two tables: adapted meta-analysis acces by amanida_result@stat [, when] Selecting the option comp.inf = T the package need the previous use of"