Single-cell RNA sequencing (scRNAseq) is revolutionizing biology and medicine. The possibility to assess cellular heterogeneity at a previously inaccessible resolution, has profoundly impacted our understanding of development, of the immune system functioning and of many diseases. While scRNAseq is now mature, the single-cell technological development has shifted to other large-scale quantitative measurements, a.k.a. ‘omics’, and even spatial positioning. In addition, combined omics measurements profiled from the same single cell are becoming available. Each single-cell omics presents intrinsic limitations and provides a different and complementary information on the same cell. The current main challenge in computational biology is to design appropriate methods to integrate this wealth of information and translate it into actionable biological knowledge. In this talk, I will discuss two main computational directions for multi-omics integration, currently explored in my team: (i) joint dimensionality reduction to study cellular heterogeneity simultaneously from multiple omics and (ii) multilayer networks to integrate a large range of interactions between the features of various omics and isolate the regulators underlying cellular heterogeneity.