GMEmbeddings

GMEmbeddings applies GloVe and principal component analysis (PCA) to amplicon sequence variant (ASV) 16S rRNA V4 data to generate lower-dimensional embeddings and translation matrices that enable functional interpretation with KEGG pathways and improve cross-study predictive modeling of stool-associated gut microbiota.

Key Features:

• Embedding Techniques: Uses the GloVe (Global Vectors for Word Representation) algorithm and PCA to transform microbial count and ASV data into lower-dimensional embedding spaces.
• Translation Matrices: Provides precomputed translation matrices at 50, 100, and 250 dimensions derived from approximately 15,000 samples from the American Gut Project.
• Data Alignment and Transformation: Supports alignment, matching, and matrix multiplication to transform V4 16S rRNA sequencing data into the provided embedding spaces.
• Biological Interpretation: Enables correlation of embedding properties with KEGG (Kyoto Encyclopedia of Genes and Genomes) functional pathways for functional inference.
• Generalization and Predictive Modeling: Contextualizes microbial occurrences within a larger dataset to enhance generalizability of models predicting host phenotypes from stool-associated gut microbiota, with benchmarking reported on six gut microbiome datasets describing three phenotypes.

Scientific Applications:

• Cross-study integration: Mitigates dataset-specific biases and batch effects when combining gut microbiome studies by mapping V4 16S rRNA data into a common embedding space.
• Predictive modeling of host phenotypes: Improves model generalizability for predicting host phenotypes from stool-associated gut microbiota across independent gut microbiome datasets.
• Functional pathway inference: Facilitates linking microbial community structure to KEGG functional pathways for studies of gastrointestinal disorders, metabolic conditions, and immune-related diseases.

Methodology:

Apply GloVe to ASV counts, apply PCA for further dimensionality reduction, generate embedding spaces and precomputed translation matrices (50/100/250 dimensions) from American Gut Project samples, and transform V4 16S rRNA data via alignment, matching, and matrix multiplication into the embedding spaces.