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A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings.

Author: Yiwen (Eva) Wang [aut, cre] , Kim-Anh Le Cao [aut]

Maintainer: Yiwen (Eva) Wang <anjiwangyiwen at>

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biocViews BatchEffect, Classification, DimensionReduction, Microbiome, Normalization, PrincipalComponent, Software, StatisticalMethod, Visualization
Version 1.0.0
In Bioconductor since BioC 3.19 (R-4.4) (< 6 months)
License GPL-3
Depends R (>= 4.3.0)
Imports mixOmics, scales, Rdpack, ggplot2, gridExtra, ggpubr, lmerTest, performance, grid, stats, pheatmap, vegan, Biobase, BiocStyle, TreeSummarizedExperiment
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Source Package PLSDAbatch_1.0.0.tar.gz
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