Single-cell RNA-sequencing (scRNA-seq) is a novel technology that measures transcript expression (i.e., messenger RNA abundance) at the level of single cells.
While some biological systems consist of clearly separated groups (cell types), others are  characterized by a smooth transition in cellular states, often represented as trajectories, e.g. the development of stem cells to mature cells. Based on the identified cell types or trajectories, researchers often attempt to discover marker genes, genes for which the expression level changes according to cell type or developmental lineage, through differential expression (DE) analysis.
In this project, we aim to increase the resolution of DE analysis in two complementary ways. First, we will develop methods to discover genes for which the expression profile changes (i) along lineages (that for instance capture the development of a stem cell to a mature cell type), and, (ii) between lineages (e.g. a stem cell population developing to distinct mature cell types).
Second, we will develop reliable tools to assess DE of isoforms and differences in the relative usage of isoforms within the gene. The methods will account for quantification uncertainty of the different isoforms of a gene, and will be applicable to both bulk RNA-seq and scRNA-seq data.
Finally, we will combine these approaches to provide optimal resolution in DE analysis by developing tools that allow for DE analysis at the transcript level along and between lineages.