Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing has increased microbial and metagenomic studies.
• Noise, missing data, and the compositional nature of microbiome data challenge statistical analysis.
• Obtaining biological replicates for microbiome studies is difficult, complicating uncertainty assessment.

Purpose of the Study:

• To address challenges in human microbiome data analysis, including noise, missing data, and lack of replicates.
• To introduce a novel probabilistic approach for modeling microbiome data with temporal correlations.
• To improve the statistical validity and ecological interpretation of microbiome sequencing data.

Main Methods:

• Developed a Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA).
• The model explicitly accounts for overdispersion and sampling zeros using Gaussian Processes.
• Incorporated temporal correlation between nearby time points in the analysis.

Main Results:

• TGP-CODA demonstrated superior modeling performance compared to Dirichlet-multinomial, multinomial, and non-parametric regression models.
• The method showed effectiveness on both real and synthetic human microbiome data.
• Demonstrated that dense temporal sampling can help overcome the non-replicative nature of studies.

Conclusions:

• The proposed TGP-CODA method provides a robust approach for analyzing complex microbiome data.
• Accurate modeling significantly impacts ecological interpretations, including stationarity and persistence.
• The TGP-CODA model offers a valuable tool for advancing human microbiome research.