Predicting microbial interactions from time series data with network information

The evolution of biotechnological knowledge poses some new challenges to study microbial interactions. Vector autoregressive (VAR) model was proved to be an efficient approach to infer dynamic interactions in biological systems. However, high-throughput metagenomics or 16S-rRNA sequencing data is high dimension, which means that the number of covariates is much larger than the number of observations. Reducing the dimension of data or selecting suitable covariates became a critical component VAR modelling. In this paper, we develop a graph-regularised vector autoregressive model incorporating network information to infer causal relationships among microbial entities. The method not only considers the signs of the network connections among any two covariates, but also constructs a network weighted matrix by microbial topology information. The coordinate descent algorithm for estimating model parameters improves the accuracy of prediction. The experimental results on a time series data set of human gut microbiomes indicate that the proposed approach has better performance than other VAR-based models with penalty functions.

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