In recent years, data production in biology has experienced unprecedented growth, driven by the development of high-throughput sequencing techniques, whose scope of application continues to expand. New sequencing technologies specifically targeting a single cell (``single-cell'') are one example. In oncology, this new data is crucial for improving our understanding of tumor development and heterogeneity by identifying the different cell types (or states) that make up a tumor. At the patient level, comprehensible mapping of this heterogeneity paves the way for new personalized medicine therapies. Characterizing this cellular heterogeneity requires the use of automatic or manual methods to annotate an individual cell (or group of similar cells) based on its gene expression. In this context, the aim of this thesis project is to develop new methods for annotating single-cell data at the intersection of several disciplines, such as bioinformatics and computer science (knowledge representation and visualization, in particular).