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

We developed a set of three multivariate and non-parametric batch effect correction methods for microbiome data to estimate and remove batch variation while preserving treatment variation.

The methods were specifically designed to handle unbalanced batch x treatment designs (weighted PLSDA-batch) and to avoid overfitting in components estimation with variable selection (sparse PLSDA-batch).

The application of our methods to both simulated and real case studies showed competitive performance to existing methods, especially for unbalanced batch x treatment designs.

Various visual and numerical assessments for batch effect detection and removal are available.