Cross-study analyses of microbial abundance using generalized common factor methods.

Area of Science:

• Microbial Ecology
• Bioinformatics
• Genomics

Background:

• Microbial communities modulate environments and host metabolism through biochemical pathways.
• High-throughput sequencing advances microbial ecology but faces challenges with high-dimensional, sparse, and noisy genomic data.
• Noise in sequencing data limits validation and consensus across studies.

Purpose of the Study:

• To develop a robust method for cross-study exploratory analysis of microbial abundance data.
• To identify generalizable biological signals from noisy, high-dimensional microbiome datasets.
• To overcome limitations in validating microbiome study results due to data variability.

Main Methods:

• An ensemble approach is proposed for analyzing microbial abundance data across multiple studies.
• Estimates the variance-covariance matrix of log-scale abundances assuming Poisson sampling for each dataset.
• Jointly models covariances to find a shared low-dimensional subspace, reducing noise and highlighting common biological signals.

Main Results:

• The method effectively reduces variation to shared biological signals across datasets.
• Demonstrated signal retention and interpretability on simulated and real metagenomic data.
• Identified a common structure in latent true abundances, enhancing generalizability.

Conclusions:

• The proposed ensemble approach enhances the inference of generalizable biological signals from microbiome data.
• This method improves the reliability and interpretability of cross-study microbiome analyses.
• Recommends a specific implementation for practical application in environmental and biomedical sciences.