The paper introduces SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for inferring microbial ecological networks from amplicon sequencing datasets. The method addresses two key challenges: the compositional nature of microbial abundance data and the underpowered nature of microbial sequencing studies. SPIEC-EASI combines data transformations for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. The method uses algorithms for sparse neighborhood and inverse covariance selection to reconstruct the network. To benchmark SPIEC-EASI, the authors develop computational tools to generate synthetic OTU count data from diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods in recovering edges and network properties on synthetic data under various scenarios. The method also successfully predicts previously unknown microbial associations using data from the American Gut project. The paper discusses the theoretical and practical advantages of SPIEC-EASI, including its scalability and efficiency in handling high-dimensional data.The paper introduces SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for inferring microbial ecological networks from amplicon sequencing datasets. The method addresses two key challenges: the compositional nature of microbial abundance data and the underpowered nature of microbial sequencing studies. SPIEC-EASI combines data transformations for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. The method uses algorithms for sparse neighborhood and inverse covariance selection to reconstruct the network. To benchmark SPIEC-EASI, the authors develop computational tools to generate synthetic OTU count data from diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods in recovering edges and network properties on synthetic data under various scenarios. The method also successfully predicts previously unknown microbial associations using data from the American Gut project. The paper discusses the theoretical and practical advantages of SPIEC-EASI, including its scalability and efficiency in handling high-dimensional data.