Batch Design Specification

batch-design-specification

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

Summary

Specification and documentation of batch assignment vectors and experimental design metadata required to correctly apply batch effect correction models (ComBat, two-stage procedure) to metabolomics datasets. Proper batch design specification ensures that technical heterogeneity is correctly attributed and removed without confounding biological signal.

When to use

Before applying any batch effect correction function in dbnorm (dbnormPcom, dbnormNPcom, dbnormBer, dbnormBagging, Visodbnorm, or hclustdbnorm), you must first prepare and validate a batch assignment vector that maps each sample to its analytical run or batch. Use this skill when metabolomics data spans multiple analytical runs, sample preparation batches, instrument sessions, or time-dependent drift periods, and you need to distinguish technical variation from biological variation.

When NOT to use

• When samples are already batch-corrected or normalized by external tools; double-correction may remove biological signal.
• When batch information is unavailable or all samples were processed in a single analytical run (no technical heterogeneity across batches).
• When batch assignment is ambiguous or samples cannot be reliably mapped to a single batch (e.g., multiplexed or partially overlapping runs without clear demarcation).

Inputs

• CSV file with samples (rows) × features (columns) matrix
• Batch assignment vector (first column of CSV, one label per sample)
• Preprocessed and log2-scaled metabolomics intensity matrix

Outputs

• Validated batch design specification (R data.frame with batch column)
• Confirmed alignment of batch vector to sample count
• Input-ready data structure for dbnorm batch correction functions

How to apply

Prepare a comma-separated CSV file with samples (independent experiments) in rows and features (metabolites) in columns. Place batch level identifiers in the first column, with each unique batch label (e.g., 'Batch_1', 'Batch_2', 'run_A', 'run_B') representing a distinct analytical run or preparation cohort. The batch vector must have one entry per sample and correctly align with the feature matrix columns. Load this structured data using read.csv() in R with row.names=1 to preserve batch labels. The batch design specification directly feeds into the statistical models: ComBat (parametric and non-parametric) uses empirical Bayes methods to estimate batch effects conditional on the batch assignment, while the two-stage procedure uses batch labels to partition data for location and scale adjustment. Validate that batch labels are consistent, non-empty, and that no samples are unassigned.

Related tools

• dbnorm (Batch normalization and visualization package that consumes batch design specification to apply ComBat and two-stage correction models) — https://github.com/NBDZ/dbnorm
• sva (Bioconductor package providing ComBat (parametric and non-parametric) empirical Bayes models used by dbnorm for batch effect correction) — https://bioconductor.org/packages/sva
• R (Statistical computing environment for loading, validating, and preparing batch design specifications and metabolomics matrices)

Examples

data <- read.csv("path/to/metabolomics.csv", sep=",", header=TRUE, row.names=1); library(dbnorm); dbnormPcom(data)

Evaluation signals

• Batch vector length equals number of samples (rows) in the feature matrix; no missing or NA values in batch column.
• All unique batch labels are non-empty strings and are consistent across the design (no typos or case mismatches).
• After loading with read.csv(..., row.names=1), table(data[,1]) or unique(data[[1]]) shows all expected batch groups with non-zero sample counts.
• Batch design passes as input to dbnorm functions without type errors or dimension mismatches; functions execute without 'batch vector length mismatch' warnings.
• Downstream batch correction outputs (adjusted R² scores, PCA plots, corrected feature matrices) show reduced batch-level clustering and improved overlap of replicate samples across batches.

Limitations

• Batch labels are categorical; temporal or continuous drift within a batch cannot be captured by discrete batch assignment alone.
• The two-stage procedure and ComBat assume that batch effects are additive or multiplicative on the log2-transformed scale; other systematic biases (e.g., instrument calibration drift, nonlinear detector response) may not be fully removed.
• Small or unbalanced batch sizes (e.g., a batch with only 1–2 samples) may lead to unstable empirical Bayes parameter estimates, reducing correction efficacy.
• The batch design specification does not account for covariates of interest (disease status, phenotype); care must be taken that batch adjustment does not remove biological signal correlated with batch.

Evidence

• [other] Load the preprocessed metabolomics matrix and batch assignment vector into R: "Load the preprocessed metabolomics matrix and batch assignment vector into R."
• [readme] Input data must be in .csv format with independent experiments in rows and features in columns, with batch levels in first column: "The input data must be in .csv format with the independent experiments in the rows and the features (variables) in the columns, with the batch levels considered in the first column."
• [readme] ComBat and two-stage procedure use batch labels to correct for technical heterogeneity: "This function performs batch effect adjustment via three statistical models implemented in the dbnorm, namely two-stage procedure as described by Giordan (2013) and/or empirical Bayes methods in"
• [readme] dbnorm includes 11 functions for batch effect correction based on statistical models: "conventional functions for batch effect correction based on statistical models, as well as functions using advanced statistical tools to generate several diagnosis plots"
• [readme] Batch design enables removal of technical heterogeneity across analytical runs: "It allows users to efficiently correct drift across batch and to adjust large metabolomics datasets for technical variation"