Zhenwei Dai1,2, Sunny H Wong1,2, Jun Yu1,2, Yingying Wei3. 1. Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, Hong Kong. 2. Gastrointestinal Cancer Biology & Therapeutics Laboratory, CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong. 3. Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: An R package" BDMMA" for Windows and Linux is available at https://github.com/DAIZHENWEI/BDMMA/BDMMA, and a version for MacOS is provided at https://github.com/DAIZHENWEI/BDMMA/BDMMA_MacOS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: An R package" BDMMA" for Windows and Linux is available at https://github.com/DAIZHENWEI/BDMMA/BDMMA, and a version for MacOS is provided at https://github.com/DAIZHENWEI/BDMMA/BDMMA_MacOS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Zhigang Li; Lu Tian; A James O'Malley; Margaret R Karagas; Anne G Hoen; Brock C Christensen; Juliette C Madan; Quran Wu; Raad Z Gharaibeh; Christian Jobin; Hongzhe Li Journal: J Am Stat Assoc Date: 2021-01-27 Impact factor: 5.033
Authors: Wodan Ling; Jiuyao Lu; Ni Zhao; Anju Lulla; Anna M Plantinga; Weijia Fu; Angela Zhang; Hongjiao Liu; Hoseung Song; Zhigang Li; Jun Chen; Timothy W Randolph; Wei Li A Koay; James R White; Lenore J Launer; Anthony A Fodor; Katie A Meyer; Michael C Wu Journal: Nat Commun Date: 2022-09-15 Impact factor: 17.694