Speaker
Description
Single-cell RNA-sequencing (scRNA-seq) data provides a detailed view into the gene regulatory landscape that cells traverse during differentiation from pluripotent to mature cell types. However, analysing these datasets remains challenging due to their sparsity, high dimensionality, lack of temporal information, and high levels of technical noise, necessitating the development of numerous specialised analytical methods.
We introduce and apply Ordered Diffusion Kernels (ODKs) to model cellular differentiation as a drift-diffusion Markov process by biasing transition probabilities with an ordering function from which we can compute velocity estimates, terminal states, stationary distributions, passage times and trajectory inference. Furthermore, we propose transforming snapshot scRNA-seq data into a pseudo-trajectory dataset with ODKs for purpose of applying powerful tools from the time series analysis literature to perform equation discovery and infer gene regulatory networks.
