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

ABSTRACT: Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10025448 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature