A unified framework for unconstrained and constrained ordination of microbiome read count data

Area of Science:

• Microbiome research
• Bioinformatics
• Statistical modeling

Background:

• Explorative visualization summarizes microbiome data using dimension reduction.
• Existing methods often focus on sample ordination, neglecting bacterial species roles.
• Many methods fail to address overdispersion and varying sequencing depths in microbiome data.

Purpose of the Study:

• To develop a novel framework for unconstrained and constrained ordination of microbiome data.
• To address limitations of existing dimension reduction techniques in handling data characteristics like overdispersion and sequencing depth variations.
• To improve the elucidation of bacterial species roles and biological patterns in microbiome datasets.

Main Methods:

• Combined log-linear models with a dispersion estimation algorithm and flexible response function modeling.
• Developed a framework for both unconstrained and constrained ordination.
• Implemented algorithms in the R-package RCM for fitting and plotting.

Main Results:

• The new method effectively handles dispersion differences between taxa and varying sequencing depths.
• It yields meaningful biological patterns and corrects for technical confounders.
• Demonstrated advantages over existing methods using simulated and real microbiome datasets.

Conclusions:

• The proposed framework offers a robust approach to microbiome data exploration and visualization.
• It explicitly states assumptions, allowing for verification through diagnostics, unlike distance-based methods.
• The integrated unconstrained and constrained ordination facilitates a comprehensive understanding of microbiome data.