Robustifying genomic classifiers to batch effects via ensemble learning.

Area of Science:

• Genomics
• Bioinformatics
• Machine Learning

Background:

• Genomic data often exhibits batch effects due to production in separate batches, impacting downstream analyses.
• Traditional methods merge data and adjust for batch effects, but may not be optimal for all scenarios.

Purpose of the Study:

• To propose and evaluate an ensemble learning strategy for handling batch effects in genomic data.
• To compare the proposed ensemble method against traditional batch adjustment techniques.

Main Methods:

• Developed prediction models within individual batches.
• Integrated batch-specific models using ensemble weighting methods.
• Systematically compared ensemble learning with merging and batch adjustment using tuberculosis genomic data.

Main Results:

• Ensemble learning demonstrated more robust performance in genomic classification, particularly with high batch effect severity.
• Merging followed by batch adjustment showed better discrimination at low heterogeneity levels.
• The study provides practical guidelines for handling batch effects in genomic classifier development.

Conclusions:

• Ensemble learning offers a powerful alternative for managing batch effects in genomic data.
• The choice of method depends on the severity of batch effects and desired robustness.
• This work contributes to more reliable genomic data analysis and prediction.