Log-Contrast Regression

Log-Contrast Regression#

This tutorial demonstrates how to use log-contrast regression to predict average soil temperature from microbial community data without incorporating taxonomic information.

Overview#

Log-contrast regression applies CLR transformation to your count data and uses regularized regression to identify predictive taxa. This is the most straightforward approach when you want to focus on raw compositional relationships without leveraging the taxonomic hierarchy.

Step 1: Transform Features#

Apply CLR transformation to the count data:

qiime classo transform-features \
    --p-transformation clr \
    --p-coef 0.5 \
    --i-features data/atacama-counts.qza \
    --o-x data/xclr

Step 2: Add Covariates#

Include environmental metadata with custom weights for each covariate:

qiime classo add-covariates \
    --i-features data/xclr.qza \
    --m-covariates-file data/atacama-selected-covariates-veg.tsv \
    --p-to-add ph average-soil-relative-humidity elevation average-soil-temperature vegetation \
    --p-w-to-add 1. 0.1 0.1 0.1 1 \
    --o-new-features data/xcovariates_lc \
    --o-new-c data/ccovariates_lc \
    --o-new-w data/wcovariates_lc

Step 3: Split Data#

Create training and test sets for regression analysis:

qiime sample-classifier split-table \
    --i-table data/xcovariates_lc.qza \
    --m-metadata-file data/atacama-selected-covariates-veg.tsv \
    --m-metadata-column average-soil-temperature \
    --p-test-size 0.2 \
    --p-random-state 42 \
    --p-stratify False \
    --o-training-table data/regress-xtraining_lc.qza \
    --o-test-table data/regress-xtest_lc.qza \
    --o-training-targets data/regress-training-targets_lc.qza \
    --o-test-targets data/regress-test-targets_lc.qza

Step 4: Train the Regression Model#

Use log-contrast regression with stability selection to identify the most stable predictive features:

qiime classo regress \
    --i-features data/regress-xtraining_lc.qza \
    --i-c data/ccovariates_lc.qza \
    --i-weights data/wcovariates_lc.qza \
    --m-y-file data/atacama-selected-covariates-veg.tsv \
    --m-y-column average-soil-temperature \
    --p-concomitant False \
    --p-stabsel \
    --p-cv \
    --p-path \
    --p-lamfixed \
    --p-stabsel-threshold 0.5 \
    --p-cv-seed 1 \
    --p-no-cv-one-se \
    --o-result data/regresstaxa_lc.qza

Key parameters:

  • --p-stabsel: Enable stability selection for robust feature selection

  • --p-stabsel-threshold 0.5: Features selected in >50% of subsamples

  • --p-cv: Use cross-validation for model selection

  • --p-concomitant False: Use standard formulation without adaptive noise modeling

Step 5: Make Predictions#

Apply the trained model to test data:

qiime classo predict \
    --i-features data/regress-xtest_lc.qza \
    --i-problem data/regresstaxa_lc.qza \
    --o-predictions data/regress-predictions_lc.qza

Step 6: Visualize Results#

Generate a comprehensive summary of the regression results:

qiime classo summarize \
    --i-problem data/regresstaxa_lc.qza \
    --i-predictions data/regress-predictions_lc.qza \
    --o-visualization data/regresstaxa_R1_lc.qzv

Output visualization: The .qzv file contains:

  • Selected taxa and their regression coefficients

  • Model performance metrics (R², RMSE, etc.)

  • Cross-validation curves

  • Prediction accuracy on test data

View the results at QIIME 2 View.

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