High-Dimensional Statistics with QIIME 2

High-Dimensional Statistics with QIIME 2#

We extend the QIIME 2 microbiome multi-omics data science platform [1] with advanced methods for high-dimensional statistical modeling. This suite currently features two powerful plugins:

q2-gglasso: Solves General Graphical Lasso problems via sparse inverse covariance estimation [2, 3].
q2-classo: Implements sparse log-contrast regression and classification models [4, 5].

With these plugins, you can:

Analyze high-dimensional data using regularization to control overfitting and improve interpretability.
Infer microbial association networks with graphical lasso and group graphical lasso.
Perform sparse, interpretable classification and regression with log-contrast models.
Build reproducible workflows leveraging QIIME 2 artifact system.

Both plugins are fully integrated with the QIIME 2 framework, enabling reproducible workflows and seamless interoperability. They aim to help researchers model, visualize, and interpret complex microbiome data using state-of-the-art high-dimensional statistical methods.

This documentation will guide you through setup, installation, and example analyses using q2-gglasso and q2-classo, demonstrating how high-dimensional statistics can advance your microbiome research.