Generative and Explainable Modelling of Single-Cell and Spatial Omics Data
This project will develop models that can generate realistic cellular trajectories by training on single-cell time course data. We are also investigating the most suitable explainability frameworks for spatial omics datasets
Project details
Description:
One aspect of our project is the development of models that can generate realistic cellular trajectories by training on single-cell time course data. These models learn from snapshots, model time evolution, and capture both continuous variation and discrete variation into alternative cellular lineages. By capturing the dynamical and branching structure of the data, we hope to combine model interpretability with high generative accuracy. Another aspect of our project is to incorporate the spatial tissue context that is available from spatial omics assays, to better characterise diseased tissues. Here, one challenge is how best to deal effectively with multiple spatial modalities, while another challenge is to deal with large differences in spatial scales for cell and tissue interactions. We are also investigating the most suitable explainability frameworks for spatial omics datasets.
Who’s involved?
Magnus Rattray, University of Manchester
David Barber, University College London
Yuhan Wang, University of Manchester
Expertise sought
This project is seeking collaborators
We would be interested in collaborating with scientists and clinicians who are applying single-cell and spatial omics assays. We are also interested in collaborations on methodology development.
Get in touch:
If you can offer the expertise needed for this project and would like to collaborate, please email us
Contact us
