Table 2.

Summary of statistical tools for inference of directed microbial interaction networks from longitudinal metagenomic data.

	LSA	LIMITS	MetaMIS	MC-TIMME	MDSINE	TIME
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
Pros	Can be used on cross-sectional data	Controls for error due to experimental uncertainty Bootstrapping reduces compositional effects	User-friendly interface and visualization Performs well on rare species	Allows design of longitudinal experiments OTU-binning improves estimations	Multiple algorithms implemented	Provides user-friendly visualization
Cons	Analysis result is affected by time scale	Biased towards highly-abundant OTUs	Biased towards highly-abundant OTUs	Assumes instantaneous transitions between dynamics	Requires concentrations, rather than relative abundances	Does not adequately account for sparsity Granger causality is prone to high FPR
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.