Table 2.
LSA | LIMITS | MetaMIS | MC-TIMME | MDSINE | TIME | |
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Full name | Local similarity analysis | Learning Interactions from microbial time series | Metagenomic microbial interaction simulator | Microbial counts and trajectories in infinite mixture model engine | Microbial dynamical systems inference engine | Temporal insights into microbial ecology |
Implementation | R | Mathematica | Desktop app | Matlab | Matlab | Web app |
Description | Detects complex, non-linear dependence associations between species and environmental factors without data reduction | Combines sparse linear regression with bootstrapping aggregation to infer a discrete-time Lotka–Volterra model | Uses an abundance-ranking strategy paired with partial least square regression to infer a discrete-time Lotka–Volterra model | Models time-varying counts of microbial taxa using an exponential relation process, coupled with adaptive Bayesian techniques | Provides a comprehensive toolbox for dynamical systems analysis of microbiota time-series to fit generalized Lotka–Volterra differential equations | Provides a workflows for time-series metagenomic data, including network inference using Granger LASSO causality |
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References | Ruan and Sun43 | Fisher and Mehta51 | Shaw and Wang52 | Gerber and Bry53 | Bucci and Gerber54 | Baksi and Mande55 |
Note: The table contains a brief description of the tool, how the statistical methods underlying it, and some of the strengths and weaknesses of each approach.