GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data.

Area of Science:

• Microbiome research
• Bioinformatics
• Computational biology

Background:

• Microbiome studies often face challenges with small sample sizes, database bias, and batch effects.
• Predictive models trained on one microbiome dataset frequently fail to generalize to others.
• Current methods struggle to integrate data from diverse 16S rRNA sequencing studies.

Purpose of the Study:

• To address limitations in microbiome data analysis, particularly generalization issues in predictive modeling.
• To develop a method for standardizing Amplicon Sequence Variant (ASV) data from 16S rRNA sequencing.
• To create a tool that improves the interpretability and applicability of microbiome data across studies.

Main Methods:

• Applied GloVe word embedding and Principal Component Analysis (PCA) to ASV data from the American Gut Project.
• Generated translation matrices for transforming 16S rRNA V4 region data into a standardized embedding space.
• Developed the GMEmbeddings R package with pre-computed embedding transformation matrices at various dimensions.

Main Results:

• Demonstrated improved generalization of predictive models for host phenotype prediction using the embedding space.
• Showcased correlation of embedding space properties with KEGG functional pathways for biological interpretation.
• Benchmarked embedding-based classifiers on six independent gut microbiome datasets, confirming generalization capabilities.

Conclusions:

• The GMEmbeddings R package offers a robust solution for standardizing and analyzing gut microbiome data, enhancing model generalizability.
• This approach facilitates cross-study comparisons and biological interpretation of microbiome findings.
• Future work will extend these embedding transformation matrices to other biological systems.