Batch effects correction for microbiome data with Dirichlet-multinomial regression

Zhenwei Dai, Sunny Hei Wong, Jun Yu, Yingying Wei

Abstract: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data.