SPIEC-EASI

SPIEC-EASI infers sparse microbial association networks using sparse inverse covariance estimation and latent graphical models to detect community-wide statistical relationships while accounting for compositional constraints and latent confounders.

Key Features:

• Latent Graphical Model Inference: Employs sparse inverse covariance estimation within a latent graphical model framework to identify parsimonious statistical relationships among microbial taxa.
• Handling Unobserved Factors: Identifies latent variables correlated with technical covariates and accounts for compositional biases to improve accuracy of inferred associations.
• Theoretical Performance Guarantees: Provides theoretical guarantees regarding computational tractability and robustness of the inference procedure.
• Validation on Simulated and Amplicon-based Gut Microbiome Data: Demonstrated performance on simulated datasets and real-world amplicon-based gut microbiome datasets.

Scientific Applications:

• Microbial Community Analysis: Investigating structure and dynamics of microbial ecosystems, including human gut microbiome studies.
• Environmental Microbiology: Exploring how environmental factors influence microbial interactions and community composition.
• Technical Covariate Adjustment: Correcting for biases introduced by technical variations and latent confounders in sequencing-based profiling.

Methodology:

Accepts targeted amplicon-based or metagenomic profiling data; applies a latent graphical model using sparse inverse covariance estimation to infer statistical relationships among microbes while simultaneously identifying unobserved factors; outputs include identified compositional biases, latent factors, and robust microbial associations.