Recent advances in next-generation sequencing technologies allowed for various types of biological data to be considered and analysed in the context of each other. In this presentation, I plan to give an overview of available methodology for biological data integration analysis, and concentrate on Bayesian learning as a promising way to explore and combine heterogenous data in Life Sciences. I will demonstrate how Bayesian matrix factorization techniques can be successfully used for studying heterogeneity among biological cells and discovering novel cell types for biomedical applications.