Metabolite pathway association mapping

metabolite-pathway-association-mapping

Summary

Transform individual metabolite identifiers (names or masses) into a standardized metabolite-pathway association table that maps each metabolite to one or more canonical metabolic pathways. This is a prerequisite step for pathway-level analysis in personalized metabolomics studies.

When to use

You have a list of metabolite names or identifiers detected in your samples and need to assign them to known metabolic pathways before computing pathway dysregulation scores, performing pathway-level machine learning, or conducting metabolite-pathway regression analysis.

When NOT to use

• Your metabolites are already pre-mapped to pathways in a curated database and require no conversion.
• You are working with a non-standard organism or custom metabolite nomenclature not covered by Lilikoi's embedded or linked databases.
• Your analysis goal is sample-level classification or prognosis without pathway-level aggregation; metabolite-level features may suffice.

Inputs

• List or vector of metabolite names (string identifiers)
• Sample metabolite abundance data (optional, for validation context)

Outputs

• Metabolite-pathway association table (rows: metabolites; columns: pathway assignments)
• Standardized metabolite identifiers (e.g., HMDB IDs)

How to apply

Use Lilikoi's lilikoi.MetaTOpathway function to convert metabolite names to standardized identifiers (such as HMDB IDs) and retrieve their pathway assignments from a curated metabolite-pathway database. The function outputs a table with metabolite identifiers as rows and pathway annotations as columns (or a lookup structure). This association table becomes the reference used downstream by lilikoi.PDSfun (for dysregulation scoring), lilikoi.meta_path (for regression), and lilikoi.KEGGplot (for visualization). The conversion ensures consistency across the analysis pipeline and enables aggregation of metabolite-level measurements into pathway-level features.

Related tools

• Lilikoi v2.0 (R package providing lilikoi.MetaTOpathway function for metabolite name-to-pathway conversion and lilikoi.PDSfun for downstream pathway dysregulation scoring) — https://github.com/lanagarmire/lilikoi2
• R (Programming environment in which Lilikoi v2.0 executes the metabolite-pathway mapping workflow)
• HMDB (Human Metabolome Database) (Referenced database providing standardized metabolite identifiers and pathway annotations used by lilikoi.MetaTOpathway)

Examples

convertResults=lilikoi.MetaTOpathway('name'); Metabolite_pathway_table = convertResults$table; head(Metabolite_pathway_table)

Evaluation signals

• Output table has no null or unmapped metabolites; all input metabolite names resolve to standardized identifiers (e.g., HMDB IDs).
• Pathway annotations are non-empty for >95% of metabolites; the table is sparse but complete (no column of all zeros).
• Downstream lilikoi.PDSfun executes without errors, confirming that the table structure and metabolite identifiers are compatible.
• Spot-check: manually verify that 2–3 known metabolites (e.g., glucose, pyruvate, lactate) are assigned to expected pathways (e.g., Glycolysis, TCA cycle).
• Table dimensions are consistent with input metabolite count and the expected size of the organism's metabolic network (e.g., hundreds of metabolites × dozens of pathways for human data).

Limitations

• Lilikoi's metabolite name conversion is limited to metabolites in curated pathway databases (primarily human metabolism); rare, novel, or organism-specific metabolites may not be mapped.
• Metabolite name ambiguity (e.g., common chemical names vs. IUPAC nomenclature) can lead to incorrect or missed mappings; standardized nomenclature (e.g., HMDB IDs) is recommended as input.
• The association table is static at the time of analysis; updates to pathway annotations or database content do not propagate automatically.

Evidence

• [readme] Transform the metabolite names to the HMDB ids using Lilikoi MetaTOpathway function: "Transform the metabolite names to the HMDB ids using Lilikoi MetaTOpathway function"
• [other] For each pathway, extract the subset of metabolites assigned to that pathway.: "For each pathway, extract the subset of metabolites assigned to that pathway."
• [intro] Lilikoi v2 supports data preprocessing, exploratory analysis, pathway visualization and metabolite-pathway regression: "Lilikoi v2 supports data preprocessing, exploratory analysis, pathway visualization and metabolite-pathway regression."
• [other] Organize scores into a matrix with samples as rows and pathways as columns, generating the PDSmatrix output.: "Organize scores into a matrix with samples as rows and pathways as columns"