Metabolomic Data Format Standardization

---
name: metabolomic-data-format-standardization
description: Use when you have raw peak table data from liquid chromatography–mass
  spectrometry (LC-MS) or related metabolomic instruments, generated by one of 12
  supported software tools (or already in NOREVA's standardized format), and need
  to prepare it for preprocessing method evaluation or biomarker.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3763
  edam_topics:
  - http://edamontology.org/topic_3172
  - http://edamontology.org/topic_0091
  tools:
  - R
  - devtools
  - Biobase
  - R (≥3.5)
  - readr
  techniques:
  - LC-MS
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1038/s41596-021-00636-9
  title: NOREVA
evidence_spans:
- '[![R >3.5](https://img.shields.io/badge/R-%3E3.5-success.svg)](https://www.r-project.org/)'
- '![installed with devtools](https://img.shields.io/badge/installed%20with-devtools-blueviolet.svg)'
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_noreva_cq
    doi: 10.1038/s41596-021-00636-9
    title: NOREVA
  dedup_kept_from: coll_noreva_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1038/s41596-021-00636-9
  all_source_dois:
  - 10.1038/s41596-021-00636-9
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# metabolomic-data-format-standardization

## Summary

Convert raw metabolomic peak tables from instrument-specific formats into a standardized ExpressionSet object that downstream NOREVA assessment functions can consume. This skill enables unified preprocessing of multi-class and time-series metabolomic datasets regardless of their originating software tool.

## When to use

You have raw peak table data from liquid chromatography–mass spectrometry (LC-MS) or related metabolomic instruments, generated by one of 12 supported software tools (or already in NOREVA's standardized format), and need to prepare it for preprocessing method evaluation or biomarker discovery workflows. The input peak table must include sample identifiers and feature intensity columns, and you have an accompanying label file defining sample classes or time-course points.

## When NOT to use

- Peak table has already been converted to an ExpressionSet or equivalent annotated feature matrix object.
- Sample labels or class assignments are missing or cannot be matched to peak table columns by row count.
- Input peak table format is not one of the 12 supported software tools and does not conform to NOREVA's standardized schema (format code 1).

## Inputs

- Peak table (CSV): feature rows × sample columns with intensity values
- Label file (CSV or table): sample identifiers with class or time-course annotations
- dataformat code: integer 1 (standardized) or 2 (software-generated)

## Outputs

- ExpressionSet object: Biobase S4 class containing assayData (intensity matrix), phenoData (sample annotations), and featureData (feature metadata)
- Prepared input object for NOREVA preprocessing and assessment functions

## How to apply

Call PrepareInputFiles() with three arguments: (1) dataformat code ('1' for standardized format, '2' for software-generated formats), (2) the filename of your raw peak table CSV, and (3) the filename of your label file specifying class/time assignments. The function loads both files using R base or readr functions, validates table structure by checking for required columns (sample identifiers, feature intensities) and verifying row count consistency between peak table and labels, then constructs a Biobase ExpressionSet object where intensities populate the expression matrix, sample metadata (class/time labels) populate phenoData, and feature metadata populate featureData. The returned ExpressionSet serves as the input object for all downstream NOREVA assessment functions (normulticlassqcall, nortimecourseqcall, etc.).

## Related tools

- **Biobase** (Constructs and manages ExpressionSet objects containing expression matrices, sample phenotypes, and feature annotations) — https://bioconductor.org/packages/Biobase
- **R (≥3.5)** (Runtime environment for loading CSV files and executing PrepareInputFiles function) — https://www.r-project.org/
- **readr** (Efficient CSV loading and parsing of peak table and label files (alternative to R base functions))
- **devtools** (Installation of NOREVA package from GitHub repository) — https://github.com/idrblab/NOREVA

## Examples

```
library(NOREVA); eset <- PrepareInuputFiles(dataformat=2, rawdata="peak_table.csv", label="sample_labels.csv")
```

## Evaluation signals

- Returned object is a valid Biobase ExpressionSet with non-empty assayData, phenoData, and featureData slots.
- ExpressionSet row count (features) matches input peak table row count; column count (samples) matches both peak table and label file row counts.
- Sample phenotype labels in phenoData align 1:1 with ExpressionSet column identifiers (sample order preserved).
- No missing or mismatched values when sample identifiers from peak table are cross-referenced against label file.
- ExpressionSet is accepted without error as input to downstream NOREVA functions (normulticlassqcall, nortimecourseqcall, etc.).

## Limitations

- PrepareInputFiles supports only 12 pre-defined instrument software formats (format code 2) plus NOREVA's standardized format (code 1); custom or unlisted formats require manual conversion to standardized format first.
- Label file must have exactly matching row count and aligned sample order with peak table; mismatches are caught only by row count validation, not by name matching.
- Peak table structure must include explicit sample identifier columns and intensity columns; ambiguous or unnamed columns may cause validation or assignment failures.
- No handling of missing intensity values, outliers, or quality control flags during standardization; such issues are addressed in downstream preprocessing functions.

## Evidence

- [other] PrepareInputFiles accepts a peak table in either standardized format (format code 1) or in formats generated by 12 available software tools (format code 2), along with a label file for time-course/multi-class studies.: "PrepareInuputFiles accepts a peak table in either standardized format (format code 1) or in formats generated by 12 available software tools (format code 2), along with a label file for"
- [other] Construct an ExpressionSet object using Biobase, assigning intensity data to the expression matrix, sample metadata (including class/time-series labels) to phenoData, and feature metadata to featureData.: "Construct an ExpressionSet object using Biobase, assigning intensity data to the expression matrix, sample metadata (including class/time-series labels) to phenoData, and feature metadata to"
- [readme] This function enables the preparation and input of peak table which facilitate the subsequent application of other NOREVA functions.: "This function enables the preparation and input of peak table which facilitate the subsequent application of other NOREVA functions."
- [other] Validate table structure, checking for required columns (sample identifiers, feature intensities) and matching row counts between peak table and labels.: "Validate table structure, checking for required columns (sample identifiers, feature intensities) and matching row counts between peak table and labels"
- [intro] NOREVA package not only enables the pre-processing and assessment of multi-class/time-series metabolomic data: "NOREVA package not only enables the pre-processing and assessment of multi-class/time-series metabolomic data"