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PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data.

Yiwen Wang^1,2, Kim-Anh Lê Cao²

¹Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Rd, Shenzhen, 518000, Guangdong, China.

Briefings in Bioinformatics

|January 18, 2023

Summary

New methods using Partial Least Squares Discriminant Analysis (PLSDA) effectively correct batch effects in microbiome data. These approaches improve the accuracy of biological insights by removing unwanted variation while preserving true treatment effects.

Keywords:

batch effect correction dimension reduction microbiome data multivariate non-parametric

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Published on: May 16, 2022

Area of Science:

Microbiology
Bioinformatics
Statistical Modeling

Background:

Microbiome data exhibit high dynamism and sensitivity to environmental changes, making them prone to batch effects.
Existing batch effect correction methods, often designed for gene expression data, fail to account for microbiome data characteristics like zero inflation and overdispersion.

Purpose of the Study:

To introduce novel multivariate, non-parametric batch effect correction methods tailored for microbiome data.
To address limitations of existing methods by considering unique features of microbiome datasets.

Main Methods:

Development of Partial Least Squares Discriminant Analysis (PLSDA)-based batch effect correction methods (PLSDA-batch).
Estimation and subtraction of batch-associated variation using latent components.
Introduction of two variants to handle unbalanced designs and prevent overfitting via variable selection.

Main Results:

PLSDA-batch methods demonstrate competitive performance in removing batch variation while preserving treatment effects, particularly in unbalanced designs.
Comparison with established methods (removeBatchEffect, ComBat, Surrogate Variable Analysis) using simulations and case studies.
Downstream analyses confirm the selection of biologically relevant taxa from corrected data.

Conclusions:

The proposed PLSDA-batch methods offer significant improvements for microbiome data analysis.
Effective batch effect correction enhances the reliability and biological relevance of microbiome research findings.
The developed methods and code are publicly available for reproducible research.