Sparse Bayesian Group Factor Model for Feature Interactions in Multiple Count Tables Data
多计数表特征交互的稀疏贝叶斯组因子模型
Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develop a sparse Bayesian group factor model (Sp-BGFM) for multiple count table data that captures the interaction between microorganisms in different domains. Sp-BGFM uses a rounded kernel mixture model using a Dirichlet process (DP) prior with…