7 Data Preparation
7.1 Required inputs
The primary input for featureMSEA is a feature table — a data frame where each row represents one LC-MS feature. The following columns are required:
| Column | Type | Description |
|---|---|---|
variable_id |
character | Unique identifier for each metabolic feature |
mz |
numeric | Measured mass-to-charge ratio (m/z) |
rt |
numeric | Retention time in seconds |
condition |
numeric | Phenotype-associated ranking statistic (absolute value): |SNR|, |log₂FC|, or |r| |
polarity |
character | Ion mode: "positive" or "negative"
|
mean_intensity |
numeric | Average feature intensity across all samples |
The analysis additionally requires two reference databases:
- MS1 metabolite database — for accurate-mass-based feature annotation (m/z matching). Supported: KEGG, HMDB.
- Metabolite set database — defines the sets of metabolites tested for enrichment. Supported: KEGG, SMPDB, IMETPD, Reactome, WikiPathways.
Both databases can be downloaded from the TidyMass website.
7.2 Demo data
A ready-to-use demo feature table is available for download:
- Feature table: Demo data
- MS1 database and Metabolite set databases: Download here
Load the feature table and databases into your R session before proceeding to the annotation step:
# Load feature table (if saved as .rda)
load("feature_table.rda") # loads object: feature_table
# Load MS1 metabolite database (example: KEGG compound MS1 database)
load("kegg_compound_ms1.rda") # loads object: kegg_compound_ms1
# Load metabolite set database (example: KEGG pathway database)
load("pathway_database.rda") # loads object: pathway_database