Article: PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data
It’s been a long journey!
The method developed by my former PhD student Eva Yiwen Wang is out and ready to be used!
Yiwen Wang, Kim-Anh Lê Cao, PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data, Briefings in Bioinformatics, 2023;, bbac622, https://doi.org/10.1093/bib/bbac622
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.