Detecting interaction networks in the human microbiome with conditional Granger causality
Fig 7
Flow chart of the methods and models we employed.
In one of the first applications of causal models in microbiome interaction network analysis, we used conditional/multivariate model of Granger causality which eliminates spurious causations but retains the true ones (this is in contrast to the traditional more famous pair-wise model of Granger causality which detects spurious causations). LASSO shrinkage and rolling cross validation of the model makes it simpler and more robust. This overall method is novel in microbiome network analysis. The novelty in the findings of our study is that we show conclusively as the first report that correlation does not inform causality at all in human microbiome. For a richer ecological inference, we decomposed the taxa interactions into (1) interspecific vs intraspecific, (2) positive vs negative, (3) cause vs effect, and (4) short vs long timescales.