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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Egg and Poultry Production Safety Research Unit » Research » Publications at this Location » Publication #416035
Research Project: Reduction of Foodborne Pathogens and Antimicrobial Resistance in Poultry Production Environments

Location: Egg and Poultry Production Safety Research Unit

Title: A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data

Author
item ZHANG, MENG - University Of North Georgia
item Li, Xiang
item Oladeinde, Adelumola - Ade
item Rothrock, Michael
item Pokoo-Aikins, Anthony
item ZOCK, GREGORY - Oak Ridge Institute For Science And Education (ORISE)

Submitted to: Microorganisms
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/7/2024
Publication Date: 9/9/2024
Citation: Zhang, M., Li, X., Oladeinde, A.A., Rothrock Jr, M.J., Pokoo-Aikins, A., Zock, G. 2024. A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data . Microorganisms. https://doi.org/10.3390/microorganisms12091866.
DOI: https://doi.org/10.3390/microorganisms12091866

Interpretive Summary: Microbes like bacteria live together in communities and interact. Studying their DNA data can reveal how different microbe species are connected. But understanding this data is very challenging. This work introduces a new computational approach called SMG (Slope-Matrix-Graph). SMG can more reliably identify relationships and groupings within microbial communities, even when there is a lot of rare data represented by zeros. SMG works by finding correlated relationships between microbes of interest and calculating differences between microbe groups using slope distances. Testing showed SMG outperforms other methods at accurately grouping related microbes. SMG also aligns with other ecological measures on real data. One of the key advantages of SMG is its ability to analyze microbial data with excessive zeros without any special adjustments required. Overall, this simple yet powerful SMG algorithm shows great potential for better understanding the complex interactions in microbiomes.

Technical Abstract: Networks are widely used to represent relationships between objects, including microorganisms within ecosystems based on high-throughput sequencing data. However, issues arise around appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation challenges. This work introduces a new Slope-Matrix-Graph (SMG) algorithm to identify microbial interrelationships and clusters based on slope-based distance calculations. SMG can effectively handle zeros in relative abundance matrices and mainly involves: 1) searching for correlated relationships based on a "target of interest", and 2) quantifying matrix/graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG's ability to accurately cluster microbes into distinct positive/negative correlation groups compared to methods like Bray-Curtis and SparCC. SMG results were also consistent with other ecological metrics like Shannon diversity. A key advantage is SMG's natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications.