Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality.

Area of Science:

• Microbial Ecology
• Time Series Analysis
• Network Inference

Background:

• Large-scale time series data enable microbial community dynamics inference.
• Correlation-based association networks lack causal and time-dependent relationship information.

Purpose of the Study:

• Introduce Granger causality for microbial time series analysis.
• Identify causal relationships among microbial and environmental factors.

Main Methods:

• Applied Granger causality model to marine microbial time series data.
• Constructed a directed acyclic network of causal relationships.
• Optimized the network using conditional Granger causality to remove false associations.

Main Results:

• Developed a Granger graph to visualize causal microbial interactions.
• Identified Gammaproteobacteria as a key functional operator.
• Found Gammaproteobacteria Granger caused by total organic nitrogen and primary production (p < 0.05, Q < 0.05).

Conclusions:

• Granger causality offers a powerful approach for inferring causal microbial dynamics.
• This method enhances understanding of microbial community structure and function.
• Reveals specific environmental drivers influencing key microbial taxa.