Regression: Predicting Continuous Outcomes

Regression: Predicting Continuous Outcomes#

This section demonstrates how to use log-contrast regression to predict continuous outcomes (like temperature, pH, or other environmental variables) from microbial community composition.

Overview#

Log-contrast regression applies regularized regression models to log-transformed compositional data, identifying which microbial taxa or taxonomic groups are most predictive of continuous environmental or clinical variables.

Workflows#

We present two approaches:

Log-Contrast Regression#

  • Uses CLR transformation and basic covariates

  • Faster computation

  • Good for exploratory analysis

  • Best when taxonomic relationships are not critical

TRAC: Regression with Taxonomic Information#

  • Incorporates taxonomic hierarchy via adaptive weights

  • More interpretable results with taxonomic context

  • Better feature selection through phylogenetic grouping

  • Recommended for publication-quality analyses

Advanced Options#

After mastering the basic workflows, explore:

Example Use Case#

In this tutorial, we predict average soil temperature from the Atacama desert microbiome dataset. The models identify which taxa show strong associations with temperature gradients.

Prerequisites#

Complete Data Preparation before starting these tutorials.

Next Steps#

  1. Start with Log-Contrast Regression

  2. Compare with TRAC approach

  3. Interpret your results using the Interpretation guide

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