Log-Contrast Classification

Log-Contrast Classification#

This tutorial demonstrates how to use log-contrast classification to predict vegetation presence from microbial community data without incorporating taxonomic information.

Overview#

Log-contrast classification is useful for predicting categorical outcomes (like vegetation presence/absence) from compositional data. This approach uses regularized logistic regression with log-contrast penalties to handle the compositional nature of microbiome data.

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:

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 classification analysis:

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

Step 4: Train the Classification Model#

Train a log-contrast classifier using cross-validation to predict vegetation presence:

qiime classo classify \
    --i-features data/classify-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 vegetation \
    --p-huber False \
    --p-stabsel \
    --p-cv \
    --p-path \
    --p-lamfixed \
    --p-stabsel-threshold 0.5 \
    --p-cv-seed 42 \
    --p-no-cv-one-se \
    --o-result data/classifytaxa_lc.qza

Parameters explained:

  • --i-features: Training feature table

  • --i-c: C matrix for log-contrast constraints

  • --i-weights: Feature weights

  • --m-y-column vegetation: Target variable (vegetation presence/absence)

  • --p-huber False: Use standard logistic loss

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

  • --p-cv: Perform cross-validation

  • --p-stabsel-threshold 0.5: Stability selection threshold

Step 5: Make Predictions#

Apply the trained model to test data:

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

Step 6: Generate Summary Visualization#

Create a comprehensive summary of the classification results:

qiime classo summarize \
    --i-problem data/classifytaxa_lc.qza \
    --i-predictions data/classify-predictions_lc.qza \
    --o-visualization data/classifytaxa_C1_lc.qzv

Output visualization: The .qzv file contains:

  • Selected taxa and their classification coefficients

  • Model performance metrics (accuracy, precision, recall, F1-score)

  • Confusion matrix for test predictions

  • Cross-validation curves

  • Feature importance rankings

View the results at QIIME 2 View.

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