q2-classo-latest

bio-datascience

BSD License Python version

A QIIME 2 plugin for solving constrained sparse regression and classification problems with microbiome data including:

  • Sparse log-contrast regression
  • Cross-validation for hyperparameter selection
  • Stability selection for feature selection
  • Classification and regression tasks
  • Tree-aggregated predictive modeling (trac)
  • Interactive visualizations with model diagnostics

📂 Tutorial & Examples

Installation¶

First, make sure you have the required dependencies installed:

conda install -c conda-forge zarr plotly

OR

pip install zarr plotly c-lasso

Next to install the plugin:

pip install git+https://github.com/bio-datascience/q2-classo-latest.git
qiime dev refresh-cache

Usage Tutorial¶

A complete tutorial on using q2-classo for microbiome data analysis — including preprocessing, CLR transformation, constrained regression, and visualization — is available in the repository examples.

👉 Quick Start Guide

This tutorial includes:

  • Random data generation and basic workflow
  • CLR transformation and taxonomic aggregation
  • Constrained lasso regression with cross-validation
  • Stability selection for robust feature selection
  • HIV sCD14 prediction case study
  • Interactive visualization of model results

Citation¶

If you use q2-classo, please cite:

Bien, J., Yan, X., Simpson, L. and Müller, C. L. (2020). Tree-Aggregated Predictive Modeling of Microbiome Data. arXiv preprint arXiv:2002.08698.

  • c-lasso: Python solvers for constrained lasso problems
  • q2-gglasso: QIIME 2 plugin for graphical lasso problems
  • QIIME 2: Extensible microbiome analysis platform

License¶

BSD 3-Clause License. See LICENSE for details.

BSD License Python version

A QIIME 2 plugin for solving constrained sparse regression and classification problems with microbiome data including:

  • Sparse log-contrast regression
  • Cross-validation for hyperparameter selection
  • Stability selection for feature selection
  • Classification and regression tasks
  • Tree-aggregated predictive modeling (trac)
  • Interactive visualizations with model diagnostics

📂 Tutorial & Examples

Installation¶

First, make sure you have the required dependencies installed:

conda install -c conda-forge zarr plotly

OR

pip install zarr plotly c-lasso

Next to install the plugin:

pip install git+https://github.com/bio-datascience/q2-classo-latest.git
qiime dev refresh-cache

Usage Tutorial¶

A complete tutorial on using q2-classo for microbiome data analysis — including preprocessing, CLR transformation, constrained regression, and visualization — is available in the repository examples.

👉 Quick Start Guide

This tutorial includes:

  • Random data generation and basic workflow
  • CLR transformation and taxonomic aggregation
  • Constrained lasso regression with cross-validation
  • Stability selection for robust feature selection
  • HIV sCD14 prediction case study
  • Interactive visualization of model results

Citation¶

If you use q2-classo, please cite:

Bien, J., Yan, X., Simpson, L. and Müller, C. L. (2020). Tree-Aggregated Predictive Modeling of Microbiome Data. arXiv preprint arXiv:2002.08698.

  • c-lasso: Python solvers for constrained lasso problems
  • q2-gglasso: QIIME 2 plugin for graphical lasso problems
  • QIIME 2: Extensible microbiome analysis platform

License¶

BSD 3-Clause License. See LICENSE for details.

BSD License Python version

A QIIME 2 plugin for solving constrained sparse regression and classification problems with microbiome data including:

  • Sparse log-contrast regression
  • Cross-validation for hyperparameter selection
  • Stability selection for feature selection
  • Classification and regression tasks
  • Tree-aggregated predictive modeling (trac)
  • Interactive visualizations with model diagnostics

📂 Tutorial & Examples

Installation¶

First, make sure you have the required dependencies installed:

conda install -c conda-forge zarr plotly

OR

pip install zarr plotly c-lasso

Next to install the plugin:

pip install git+https://github.com/bio-datascience/q2-classo-latest.git
qiime dev refresh-cache

Usage Tutorial¶

A complete tutorial on using q2-classo for microbiome data analysis — including preprocessing, CLR transformation, constrained regression, and visualization — is available in the repository examples.

👉 Quick Start Guide

This tutorial includes:

  • Random data generation and basic workflow
  • CLR transformation and taxonomic aggregation
  • Constrained lasso regression with cross-validation
  • Stability selection for robust feature selection
  • HIV sCD14 prediction case study
  • Interactive visualization of model results

Citation¶

If you use q2-classo, please cite:

Bien, J., Yan, X., Simpson, L. and Müller, C. L. (2020). Tree-Aggregated Predictive Modeling of Microbiome Data. arXiv preprint arXiv:2002.08698.

  • c-lasso: Python solvers for constrained lasso problems
  • q2-gglasso: QIIME 2 plugin for graphical lasso problems
  • QIIME 2: Extensible microbiome analysis platform

License¶

BSD 3-Clause License. See LICENSE for details.

BSD License Python version

A QIIME 2 plugin for solving constrained sparse regression and classification problems with microbiome data including:

  • Sparse log-contrast regression
  • Cross-validation for hyperparameter selection
  • Stability selection for feature selection
  • Classification and regression tasks
  • Tree-aggregated predictive modeling (trac)
  • Interactive visualizations with model diagnostics

📂 Tutorial & Examples

Installation¶

First, make sure you have the required dependencies installed:

conda install -c conda-forge zarr plotly

OR

pip install zarr plotly c-lasso

Next to install the plugin:

pip install git+https://github.com/bio-datascience/q2-classo-latest.git
qiime dev refresh-cache

Usage Tutorial¶

A complete tutorial on using q2-classo for microbiome data analysis — including preprocessing, CLR transformation, constrained regression, and visualization — is available in the repository examples.

👉 Quick Start Guide

This tutorial includes:

  • Random data generation and basic workflow
  • CLR transformation and taxonomic aggregation
  • Constrained lasso regression with cross-validation
  • Stability selection for robust feature selection
  • HIV sCD14 prediction case study
  • Interactive visualization of model results

Citation¶

If you use q2-classo, please cite:

Bien, J., Yan, X., Simpson, L. and Müller, C. L. (2020). Tree-Aggregated Predictive Modeling of Microbiome Data. arXiv preprint arXiv:2002.08698.

  • c-lasso: Python solvers for constrained lasso problems
  • q2-gglasso: QIIME 2 plugin for graphical lasso problems
  • QIIME 2: Extensible microbiome analysis platform

License¶

BSD 3-Clause License. See LICENSE for details.