The field of single-cell transcriptomics (scRNA-seq) is currently experiencing a massive boom in popularity. It enables researchers to study gene expression in individual cells, revolutionizing their view on complex biological processes as development and disease. The technology is extremely high-throughput; it measures the expression of thousands of genes in up to 10000 cells per sample, giving scientists a bird's-eye view on the gene expression profiles of all different cell types within a sample. This has the promise to unravel cell type specific gene expression signatures. More importantly, it also allows to establish how these signatures differ between healthy and diseased subjects; and how each cell type responds to stimuli. This is, however, currently hampered by the lack of data analysis tools that 1) can deal with the fact that many genes are not picked up by the technology or because their expression is high in some cells, and absent in others, 2) account for strong correlation of cells within a subject, since their expression profiles are more alike than those of cells between different subjects, and 3) can study differences in the relative usage of isoforms within the gene. Within this project, we will develop novel statistical tools that overcome each of these challenges switching scRNA-seq data analysis into the next gear.