Efficiency in dealing with batch effects will be the next frontier in large-scale biological data analysis, particularly when involving the integration of different types of datasets. Large-scale omics techniques have quickly developed during the last decade and huge amounts of data are now generated, which has started to revolutionize the area of medical research. With the increase in the volume of data across the whole spectrum of biology, problems related to data analytics are continuously increasing as analysis and interpretation of these large volumes of molecular data has become a real challenge. Tremendous efforts have been made to obtain data from various molecular levels and the most recent trends show that more and more researchers now are trying to integrate data of various molecular types to inform hypotheses and biological questions. Tightly connected to this work are the batch-related biases that commonly are apparent between different datasets, but these problems are often not tackled. In present study the ComBat algorithm was applied and evaluated on two different data integration problems. Results show that the batch effects present in the integrated datasets efficiently could be removed by applying the ComBat algorithm.