Fig. 2

New inference algorithms in MDSINE outperform our previously published method on simulated data. Data were simulated to capture key features of real microbiome surveys, including noise and compositionality. Simulations assumed an underlying dynamical systems model with ten species observed over 30 days and an invading species at day 10. The number of time points sampled was varied between 8 and 27 to mimic common experimental designs and sequencing depths of 1000 or 25,000 reads were evaluated. Performance of the four MDSINE inference algorithms, maximum likelihood ridge regression (MLRR), maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS), were compared. Algorithm performance was assessed using four different metrics: root mean square error (RMSE) for microbial growth rates (a); RMSE for microbial interaction parameters (b); RMSE for prediction of microbe trajectories on held-out subjects given only initial microbe concentrations for the held-out subject (c); and area under the receiver operator curve (AUC ROC) for the underlying microbial interaction network (d). Lower RMSE values indicate superior performance, whereas higher AUC ROC values indicate superior performance