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 previous 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. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity. Simulation studies and real data analysis show that BDMMA can successfully adjust batch effects and substantially reduce false discoveries in microbial meta-analyses. Our R package BDMMAcorrect is available at Bioconductor: bioconductor.org/packages/release/bioc/html/BDMMAcorrect
Dr Yingying Wei
Associate Professor, Department of Statistics, The Chinese University of Hong Kong
Dr Wei is an Associate Professor in the Department of Statistics at the Chinese University of Hong Kong. She obtained her bachelor’s degree in Mathematics from Tsinghua University in 2009 and her MSc Eng degree in Computer Science and PhD degree in Biostatistics from Johns Hopkins University in 2014. Her research focuses on developing statistical methods for
analysing noisy, complex and heterogeneous big genomic data. Her paper on meta-clustering of genomic data received the W. J. Youden Award in Interlaboratory Testing from the American Statistical Association in 2019.