Elastic Net Regression

This tool fits a linear regressor with elastic-net regularization. This tool will then subsequently select the highest weighted OTUs or taxonomic groups using recursive feature elimination.

Used For

• Selecting OTUs or taxonomic groups ("OTU signature") that are important (more heavily weighted) when predicting a quantitative metadata variable

Feature Selection Parameters

Taxonomic Level

The taxonomic level to aggregate the OTUs at. The OTUs will be grouped together (by summing the OTU values) at the selected taxonomic level before the analysis is applied.

Experimental Variable

Create comparative sample groups based on categorical variables uploaded in the metadata file.

Optionally create a categorical variable from a quantitative variable by using the Quantile Range feature on the Projects home page.

Number of Features to Keep

Specify how many features to display in the output. Leave blank to not do any filtering.

Loss Function

The type of loss to use when training the model.

Fix Training Set Between Changes

Indicate whether the training set should remain the same every time a parameter is changed and the model is retrained.

Dataset Training Proportion

Define the proportion of the data that should be randomly picked to form a training dataset.

You can set this value to be 1.0 if you don't plan on evaluating with a test dataset.

L1 Regularization Ratio

L1 (LASSO) regularization helps encourage sparsity within the selected features, which means that fewer features will be used to predict the experimental variable.

0.5 is recommended.

Max Iterations

The maximum number of passes through the data during training.

Interactive Elements

• Link back to boxplots

Additional Features

• Save Snapshot: Save the results to the experiment notebook

• Download: Downloads the results as a CSV file

• Share: Creates a shareable link that allows you to share the results with others