Metabolite Feature Standardization

---
name: metabolite-feature-standardization
description: Use when after loading raw metabolomics measurement data (samples × metabolites
  matrix) into R but before computing covariance matrices or Jacobian analysis.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3435
  edam_topics:
  - http://edamontology.org/topic_0769
  - http://edamontology.org/topic_3172
  tools:
  - MInfer
  - R
  - MetaboAnalyst
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1016/j.cmpb.2025.108672
  title: MInfer
evidence_spans:
- MInfer is an R package designed for analyzing metabolomics data
- MInfer is an R package
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_minfer_cq
    doi: 10.1016/j.cmpb.2025.108672
    title: MInfer
  dedup_kept_from: coll_minfer_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1016/j.cmpb.2025.108672
  all_source_dois:
  - 10.1016/j.cmpb.2025.108672
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# metabolite-feature-standardization

## Summary

Normalize and standardize metabolite measurements from a samples × metabolites matrix to prepare them for downstream covariance and Jacobian matrix computation. This preprocessing step ensures metabolite features are on comparable scales and removes technical bias before network inference.

## When to use

Apply this skill after loading raw metabolomics measurement data (samples × metabolites matrix) into R but before computing covariance matrices or Jacobian analysis. Use it when metabolite measurements are in heterogeneous units or have unequal variance across features, which would bias interaction network inference.

## When NOT to use

- Input data has already been normalized and standardized by an upstream tool (e.g., MetaboAnalyst preprocessing); applying a second standardization may introduce artifacts.
- Metabolite measurements are already in a dimensionless or log-transformed space where rescaling is not required.
- The analysis goal does not involve covariance or correlation-based methods (e.g., only univariate metabolite abundance comparison).

## Inputs

- samples × metabolites matrix (raw metabolomics measurement data)
- loaded into R environment

## Outputs

- standardized samples × metabolites matrix
- prepared metabolomics data ready for covariance matrix generation

## How to apply

Load the raw metabolomics measurement data (samples × metabolites matrix) into R. Apply MInfer's data preparation module, which normalizes and standardizes the metabolite measurements to place all features on comparable scales. The rationale is that covariance and Jacobian matrix methods assume homogeneous variance; standardization prevents high-variance metabolites from dominating the computed interaction networks. Pass the standardized output directly to the covariance matrix generation function. Verify standardization by confirming all metabolite features have mean ≈ 0 and standard deviation ≈ 1.

## Related tools

- **MInfer** (Provides the data preparation module that implements normalization and standardization of metabolite measurements) — https://github.com/cellbiomaths/MInfer
- **R** (Runtime environment in which MInfer's data preparation functions are executed)
- **MetaboAnalyst** (Alternative upstream platform from which standardized data may be imported; MInfer facilitates transition from MetaboAnalyst to Jacobian analysis)

## Examples

```
data_6C <- prepare_data(met_input, 6); cov_6C <- generate_covariance(data_6C, num_tp = 1)
```

## Evaluation signals

- All metabolite features have mean ≈ 0 after standardization (verify with colMeans on standardized matrix)
- All metabolite features have standard deviation ≈ 1 after standardization (verify with apply(..., sd) on standardized matrix)
- Subsequent covariance matrix (generated from standardized data) has diagonal elements ≈ 1 (since covariance of a standardized variable with itself equals variance = 1)
- No NaN or Inf values appear in the standardized matrix
- Rank and dimensionality of the standardized matrix match the input (samples × metabolites)

## Limitations

- Standardization assumes metabolite measurements are approximately normally distributed; heavily skewed or multimodal distributions may require log-transformation or other preprocessing before standardization.
- Missing values (NaN, NA) in the raw data must be handled (imputation or removal) before standardization, but MInfer's README does not specify the missing-value strategy.
- Standardization can inflate noise in low-abundance metabolites; users should consider filtering low-intensity or low-variance metabolites prior to standardization if domain knowledge supports it.
- No changelog is available in the repository, making it unclear whether standardization parameters (e.g., centering method, outlier handling) have changed across MInfer versions.

## Evidence

- [other] data preparation and standardization step: "Apply MInfer's data preparation module to normalize and standardize the metabolite measurements."
- [intro] rationale for standardization in workflow: "MInfer provides tools for data preparation, covariance matrix generation, Jacobian matrix computation, and visualization of metabolite interaction networks"
- [readme] downstream use in covariance computation: "Prepare the data by selecting specific conditions (e.g., 6C and 16C): data_6C <- prepare_data(met_input, 6); Generate covariance matrices for the prepared data"
- [other] input data structure and format: "Load prepared metabolomics measurement data (samples × metabolites matrix) into R."