Context-aware dimensionality reduction deconvolutes gut microbial community dynamics.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Human microbiome studies face challenges due to significant interindividual variability.
• This variation complicates the identification of microbial patterns linked to specific host phenotypes.
• Existing methods may struggle with the sparse and compositional nature of microbiome data.

Purpose of the Study:

• To introduce a novel dimensionality reduction tool, compositional tensor factorization (CTF).
• To leverage multi-sample host data for uncovering microbial composition patterns.
• To improve the detection and reproducibility of microbial changes associated with host phenotypes.

Main Methods:

• Developed and applied compositional tensor factorization (CTF), a dimensionality reduction technique.
• Incorporated data from multiple samples from the same host to account for interindividual variation.
• Analyzed sparse compositional microbiome datasets to identify robust patterns.

Main Results:

• CTF successfully identified robust patterns in sparse microbiome data.
• The tool revealed microbial changes associated with specific phenotypes.
• These findings were reproducible across different datasets, demonstrating the method's reliability.

Conclusions:

• Compositional tensor factorization (CTF) offers a powerful approach to overcome interindividual variation in microbiome research.
• CTF enhances the ability to detect phenotype-associated microbial signatures.
• The method promotes reproducibility and translational potential in human microbiome studies.