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  "Description": "A novel framework to correct for batch effects prior to\nany downstream analysis in microbiome data based on Projection\nto Latent Structures Discriminant Analysis. The main method is\nnamed “PLSDA-batch”. It first estimates treatment and batch\nvariation with latent components, then subtracts\nbatch-associated components from the data whilst preserving\nbiological variation of interest. PLSDA-batch is highly\nsuitable for microbiome data as it is non-parametric,\nmultivariate and allows for ordination and data visualisation.\nCombined with centered log-ratio transformation for addressing\nuneven library sizes and compositional structure, PLSDA-batch\naddresses all characteristics of microbiome data that existing\ncorrection methods have ignored so far. Two other variants are\nproposed for 1/ unbalanced batch x treatment designs that are\ncommonly encountered in studies with small sample sizes, and\nfor 2/ selection of discriminative variables amongst treatment\ngroups to avoid overfitting in classification problems. These\ntwo variants have widened the scope of applicability of\nPLSDA-batch to different data settings.",
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