Batch effect removal metabolomic data

Skill HolobiomicsLab/asb-skill-collections/collections/metabolomics/v2/skills/batch-effect-removal-metabolomic-data

Use when after integrating feature matrices from multiple analytical experiments or batches (e.g., n=3 or more batches). Use it when batch-related systematic variations are present in the integrated dataset before proceeding to sample separation, marker identification, or annotation steps.From its SKILL.md

Install

npx -y skills add HolobiomicsLab/asb-skill-collections --skill batch-effect-removal-metabolomic-data

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Batch-Effect Removal in Metabolomic Data

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Remove unwanted batch variations from integrated multi-batch metabolomic datasets using algorithm-specific correction methods. This skill is essential after data integration to harmonize feature intensities across analytical experiments and enable unbiased downstream analysis.

When to use

Apply this skill after integrating feature matrices from multiple analytical experiments or batches (e.g., n=3 or more batches). Use it when batch-related systematic variations are present in the integrated dataset before proceeding to sample separation, marker identification, or annotation steps.

When NOT to use

Input is a single-batch dataset with no cross-batch contamination
Feature matrix has not yet been integrated across multiple analytical experiments
Batch information or batch labeling is unavailable or ambiguous

Inputs

Integrated multi-batch feature-by-sample matrix (MutileAlign format)
Number of batches as integer (n parameter)
Algorithm selection string (BMC/PAMR, ComBat/EB, or GlobalNorm)

Outputs

Batch-corrected feature matrix (same dimensions as input)
Harmonized intensities across all batches

How to apply

Load the integrated feature-by-sample matrix (e.g., MutileAlign output containing mass, retention time, intensity, isotope, and adduct information) into R. Call the Removal_Batch() function with three key parameters: the integrated dataset, the number of batches (n parameter, e.g., n=3), and the algorithm parameter specifying the correction method (BMC/PAMR for batch mean-centering, ComBat/EB for empirical Bayes, or GlobalNorm for global normalization). The function returns a batch-corrected feature matrix where unwanted variations among different batches have been removed. Extract and validate the corrected output before downstream analysis.

Related tools

LargeMetabo (R package housing Removal_Batch() function and batch correction algorithms (BMC/PAMR, ComBat/EB, GlobalNorm)) — https://github.com/LargeMetabo/LargeMetabo
R (Runtime environment (≥ 3.5.0) required to execute Removal_Batch() and dependent packages) — https://www.r-project.org
mixOmics (Background package used for batch effect removal algorithms)

Examples

DataAfterBatch <- Removal_Batch(MutileAlign, n = 3, algorithm = "BMC/PAMR")
DataAfterBatch[1:5,1:5]

Evaluation signals

Output feature matrix has same dimensions (rows, columns) as input integrated matrix
Corrected intensities fall within expected physiological or analytical ranges (no NAs or infinities)
Visualization of corrected data (e.g., PCA, hierarchical clustering) shows reduced batch-dependent clustering compared to pre-correction data
Cross-batch coefficient of variation (CV) for reference or QC metabolites decreases after correction
No systematic bias remains when comparing batch-specific feature distributions post-correction

Limitations

Algorithm choice (BMC/PAMR vs. ComBat/EB vs. GlobalNorm) must be specified a priori; no automated selection guidance is provided in the README
Method assumes batch structure is known and correctly specified via the n parameter; misspecified batch count may produce artifacts
Removal_Batch() assumes integrated data has been properly aligned (via Integrate_Data() step); garbage input produces garbage output
Global normalization (GlobalNorm) may mask true biological differences if batch and treatment effects are confounded

Evidence

[readme] After data integration, it was essential to remove the unwanted variations among different batches: "After data integration, it was essential to remove the unwanted variations among different batches"
[other] Removal_Batch() function accepts integrated dataset, number of batches, and algorithm parameter to produce batch-corrected feature matrix: "The Removal_Batch() function accepts a comprehensive integrated dataset (MutileAlign), the number of batches (n=3), and an algorithm parameter (BMC/PAMR) to produce a batch-corrected feature matrix"
[readme] Multiple batch effect removal methods are provided: "Various methods are provided in the LargeMetabo package for removing batch effects in different analytical experiments, including batch mean-centering (BMC/PAMR), the empirical Bayes method"
[readme] Example workflow for batch effect removal: "DataAfterBatch <- Removal_Batch(MutileAlign, n = 3, algorithm = "BMC/PAMR")"

What ships with it

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