RNA sequencing of individual cells allows us to take a snapshot of the dynamic processes within a cell and explore the differences between cell types. As this technology has developed over the last few years it has been rapidly adopted by researchers in areas such as developmental biology. Along with the development of protocols for producing this data has been a simultaneous burst in the development of computational methods for analysing it. My thesis explores the computational tools and techniques for analysing single-cell RNA-sequencing data. I will present a database that charts the release of analysis software (https://scrna-tools.org), Splatter, a software package for easily simulating single-cell datasets from multiple models (http://bioconductor.org/packages/splatter/) and clustering trees, a visualisation approach for inspecting clustering results at multiple resolutions (https://CRAN.R-project.org/package=clustree). In the final part of my thesis, I use an analysis of a kidney organoid dataset to demonstrate and compare some of the current analysis methods.