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. 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.
Package details |
|
---|---|
Author | ZHENWEI DAI <daizwhao@gmail.com> |
Bioconductor views | BatchEffect Bayesian ImmunoOncology Microbiome |
Maintainer | ZHENWEI DAI <daizwhao@gmail.com> |
License | GPL (>= 2) |
Version | 1.8.0 |
Package repository | View on Bioconductor |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.