Null-model-based network comparison reveals core associations.

Area of Science:

• Microbial Ecology
• Bioinformatics
• Network Analysis

Background:

• Microbial network construction is crucial in microbial ecology but often relies on inferred associations, leading to inaccuracies.
• Existing methods for microbial association networks are error-prone and may not accurately represent true ecological interactions or community structure.

Purpose of the Study:

• To introduce 'anuran,' a novel toolbox for investigating noisy microbial networks using null models.
• To enable the identification of conserved subsets (core association networks, CANs) and shared network properties across multiple microbial networks.

Main Methods:

• Development of the 'anuran' toolbox, incorporating null models to generate data under a random association hypothesis.
• Application of null models to identify non-random patterns within groups of association networks.
• Comparison of multiple networks to detect conserved core association networks (CANs) and shared properties.

Main Results:

• Demonstrated the existence of CANs in a time series of fecal samples from 20 women, highlighting conserved microbial patterns in individuals.
• Utilized data from the Global Sponge Project to show that sponge orders exhibit larger-than-random CANs, indicating conserved microbial associations.
• Validated the effectiveness of 'anuran' in identifying conserved microbial network structures.

Conclusions:

• The 'anuran' toolbox provides a robust method for comparing microbial networks across diverse conditions, time series, gradients, or hosts.
• Null model-based analysis significantly improves the reliability of microbial network interpretation, moving beyond simple statistical associations.
• The identification of core association networks (CANs) offers valuable insights into stable microbial community structures.