The cells of our immune system play an essential role in protecting us from infections from pathogens such as viruses or harmful bacte- ria. In the context of a disease, the different types of immune cells perform special roles and interact, resulting in a finely orchestrated immune response. However, this complex immune response can in some cases be disrupted. For instance, the cells that are supposed to fight a disease can be silenced. This phenomenon can be observed in tumors, in which cells can start proliferating abnormally without being controlled by a functional immune response. Understanding how the immune system works in the context of a disease is therefore of crucial importance if we want to find efficient therapies. The cells from the immune system can now be thoroughly studied with technologies that generate unprecedented amounts of in- formation on these cells’ shape, type, and on the molecules that they contain. This enormous amount of data represents a challenge for the doctors who need to analyse it. In this context, many computational tools are being developed, to automate the analysis of medical data. These computational tools tackle typical data analysis issues, such as preprocessing (to obtain clean, noise-free data), feature selection (to identify cell features of interest), clustering (to identify groups of similar cells), trajectory inference (to identify developmental pro- cesses), and network inference (to identify genes that can influence other genes), among others. The topic of this thesis is the application and design of computational solutions for single-cell data analysis. In the first part of this the- sis, we essentially focus on identifying structure in this type of data. We first present a new computational tool for trajectory inference, TinGa, that can identify cell developmental trajectories in a fast and flexible way. Trajectories are typically identified by compressing the information contained in thousands of genes into a low-dimensional space. We thus secondly present an exploratory study, in which we aimed at computing an optimal low-dimensional space in which the identification of a trajectory would be facilitated. Thirdly, we ap- plied trajectory inference as well as a new network inference method, BRED, to gain biological insight on the response of CD8 T cells upon an acute viral infection. We identified two sources of memory along the developmental trajectory followed by activated CD8 T cells, and we characterised these two memory precursor populations. Finally, we report our results on a multi-omics study that aimed at unravel- ing differences between patients that were tolerant to a graft trans- plantation and patients who developed graft-versus-host disease. By integrating three different types of data, we were able to uncover the crucial role between an activated state and a steady state of the im- mune system in these patients. Computational tools allow to analyse new types of large scale datasets in a fast and efficient way. By allowing to automate analyses that were previously performed manually, they present multiple advan- tages. First, they make it possible to analyse data of unprecedented size and complexity. Secondly, they significantly reduce the time typ- ically needed for the analysis of any type of data. Lastly, they lead to more robust results, since correctly set computational experiments can be repeated by different persons and will lead to identical results. Altogether, the development and application of computational tools can lead to more robust and reproducible single-cell omics research.