We will discuss method for the analysis of integrated datasets containing microbial abundance, metagenomics and host genomics (with multiple genomic data-types). We will start by discussing sparse methods for modeling interactions between host genomics measurements and metagenomic measurements. We will discuss alternate model selection methods. We will compare multiple methods for normalization and batch correction in the context of inferring host-microbiome interactions. Lastly, we will discuss the inference of host-microbiome interactions in the context of large efforts to learn immune cell sub-type regulatory networks and efforts to learn human associated microbial ecological networks.