Robustifying genomic classifiers to batch effects via ensemble learning

Genomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across processing batches. Such \"batch effects\" often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods. In contrast to the typical approach of removing batch effects from the merged data, our method integrates predictions rather than data. We provide a systematic comparison between these two strategies, using studies targeting diverse populations infected with tuberculosis. In one study, we simulated increasing levels of heterogeneity across random subsets of the study, which we treat as simulated batches. We then use the two methods to develop a genomic classifier for the binary indicator of disease status. We evaluate the accuracy of prediction in another independent study targeting a different population cohort. We observed a turning point in the level of heterogeneity, after which our strategy of integrating predictions yields better discrimination in independent validation than the traditional method of integrating the data. These observations provide practical guidelines for handling batch effects in the development and evaluation of genomic classifiers.