Data-integration of multiple studies is key to understanding and gaining knowledge in statistical research. However, such data present artifactual sources of variation, also known as covariate effects. Covariate effects can be complex and can lead to systematic biases. If not corrected, these biases may lead to unreliable inferences. Here, we will present novel sparse latent factor regression and multi-study factor regression models to integrate heterogeneous data.