Enhancing the understanding of environmental microbiomes through topic modeling: a quantitative and qualitative...

Area of Science:

• Environmental microbiology
• Machine learning applications
• Ecosystem dynamics

Background:

• Ecosystem health assessment is challenged by complex interactions.
• Microbial communities are key indicators of environmental change.
• High-dimensional microbiome data requires advanced analytical methods.

Purpose of the Study:

• To explore topic modeling (TM) for analyzing estuarine microbiome data.
• To compare TM performance against traditional dimensionality reduction techniques.
• To assess TM's utility in understanding microbial community patterns.

Main Methods:

• Application of topic modeling (TM) to estuarine microbiome data.
• Comparison of TM with Principal Component Analysis (PCA) and Principal Coordinate Analysis (PCoA).
• Utilizing Non-Negative Matrix Factorization (NNMF), a TM method, for pattern identification.

Main Results:

• TM demonstrates comparable, and in some aspects superior, performance to conventional methods.
• NNMF identified five distinct, seasonally succeeding sub-communities in the Warnow Estuary.
• These sub-communities correlated with salinity, temperature, and distinct taxonomic profiles.

Conclusions:

• Topic modeling is a valuable tool for exploring complex environmental microbiome datasets.
• TM offers complementary ecological insights and highlights coherent microbial community patterns.
• TM shows promise for environmental monitoring and ecosystem management in dynamic habitats.