Investigating microbial associations from sequencing survey data with co-correspondence analysis

Area of Science:

• Ecology
• Microbiology
• Bioinformatics

Background:

• Microbial communities are vital for ecosystem functions, but their structure and interactions are difficult to study.
• Existing methods often analyze single datasets, limiting the understanding of multi-community dynamics.
• Next-generation sequencing (NGS) now enables simultaneous data collection for multiple taxonomic groups within the same samples.

Purpose of the Study:

• To propose and validate an analytical framework, co-correspondence analysis (CoCA), for studying the distributions, assemblages, and interactions between two microbial communities.
• To demonstrate CoCA's capability in identifying association patterns within and between microbial assemblages.
• To show how CoCA can complement existing methods like network analysis for ecological pattern investigation.

Main Methods:

• Development of a co-correspondence analysis (CoCA) framework.
• Application of CoCA to analyze paired next-generation sequencing (NGS) data from two microbial communities.
• Comparative analysis with network analysis for ecological network reordering.

Main Results:

• CoCA successfully highlighted significant association patterns between microbial communities.
• Strong relationships were identified between autotrophic and heterotrophic microbial eukaryote assemblages.
• Significant associations were also found between microalgae and virus communities.
• CoCA demonstrated utility in reordering co-occurrence networks, aiding pattern discovery.

Conclusions:

• CoCA provides a powerful approach for analyzing relationships between two microbial communities using paired NGS data.
• This method enhances our ability to understand microbial community structure and interactions, crucial for predicting ecosystem responses.
• CoCA serves as a valuable complementary tool to network analysis for ecological research.