Preprocessed feature tables, annotated metabolites, differential results, and pathway enrichment scores.

Each stage returns a structured result — a preprocessed feature table summary, putative metabolite annotations, differential test results, or KEGG pathway enrichment scores. These let you carry a feature table into annotation, identify which metabolites change, and see which metabolic pathways they implicate.

Interpreting metabolomics outputs

Metabolomics Preprocess output

Field	Type	Description
`features_detected`	number	Number of features in the input table
`samples_processed`	number	Number of samples processed
`normalization`	string	Normalization applied (`"TIC"` for a peak table, `"none"` for raw mzML)
`transformation`	string	Transformation applied (`"log2"` or `"none"`)
`ion_mode`	string	Ionization polarity used
`tic_median`	number	Median total ion current across samples
`tic_range`	number[]	`[min, max]` total ion current across samples
`missing_value_rate`	number	Fraction of zero/missing values in the table
`mean_cv`	number	Mean coefficient of variation across features
`top_features_by_intensity`	array	The 10 highest-intensity features, each `{feature_id, mean_intensity}`

A run against a raw mzml_key (rather than a preprocessed peak table) returns features_detected: 0 with a note that full mzML processing requires uploading a peak table.

Metabolomics Annotate output

Field	Type	Description
`annotations`	array	Per-feature annotation results (capped at the first 50 features)
`total_features`	number	Number of input features
`total_queried`	number	Number of features queried against the database
`total_annotated`	number	Features with a putative match
`mass_accuracy_ppm`	number	ppm tolerance used for matching
`ion_mode`	string	Ionization polarity used
`database`	string	Database queried (currently `"HMDB"`)

Annotation fields

Field	Type	Description
`feature_id`	string	Input feature identifier
`annotation`	string	`"putative_match"` or `"unknown"`
`confidence`	number	`0.5` for a putative match, `0` otherwise — there is no multi-level confidence scale
`hmdb_id`	string	Matched HMDB identifier, when available
`formula`	string	Molecular formula, when available
`adduct`	string	Matched adduct, when available
`query_mass`	number	The m/z value queried, present only for matched features

Metabolomics Differential output

Field	Type	Description
`significant_metabolites`	array	Features reaching FDR < 0.05, sorted by p-value (capped at 50)
`total_tested`	number	Features tested
`total_significant`	number	Features reaching significance
`method`	string	Statistical method used (e.g., `"Welch_t-test"`)
`correction`	string	Multiple testing correction method (`"Benjamini-Hochberg"`)
`group1`	object	`{name, n_samples}` for the first group
`group2`	object	`{name, n_samples}` for the second group

Result fields

Field	Type	Description
`feature_id`	string	Feature identifier
`t_statistic`	number	Test statistic
`p_value`	number	Raw p-value
`p_adjusted`	number	Benjamini-Hochberg-adjusted p-value
`log2_fold_change`	number	Difference in mean log-intensity between group 2 and group 1
`mean_group1`	number	Mean intensity in group 1
`mean_group2`	number	Mean intensity in group 2

Metabolomics Pathway output

Field	Type	Description
`enriched_pathways`	array	Pathway enrichment results, sorted by p-value
`significant_pathways`	number	Pathways reaching FDR < 0.05
`total_pathways_tested`	number	Number of KEGG pathways tested against the curated reference set
`total_features_with_mass`	number	Input metabolites whose mass could be parsed
`mass_accuracy_ppm`	number	ppm tolerance used for matching against reference metabolite masses
`database`	string	`"KEGG"`
`correction`	string	Multiple testing correction method (`"Benjamini-Hochberg"`)

Pathway fields

Field	Type	Description
`pathway`	string	Pathway name
`pathway_id`	string	KEGG pathway identifier
`p_value`	number	Raw enrichment p-value (Fisher's exact test)
`p_adjusted`	number	FDR-adjusted p-value
`matched_metabolites`	number	Reference metabolites in this pathway with a mass match in your data
`pathway_size`	number	Number of reference metabolites in this pathway
`coverage`	number	`matched_metabolites / pathway_size`
`impact_score`	number	`coverage * (1 - p_value)`
`metabolite_names`	string[]	Names of the matched reference metabolites

Interpreting metabolomics outputs