Bayesian models for understanding the effects of mutations on health and disease

Recently developed sequencing technologies, like scATAC-seq and scRNA-seq, can obtain the epigenome and the transcriptome at the resolution of a single cell. Computational methods that leverage the omics big data regime can help study the mutations in cells, and hence lead to a better understanding of cell dysfunction in both healthy and diseased organisms.

The broad aim of this project is to build Bayesian models to study the effects of the presence of mutation in health and disease.. We seek to build Bayesian models that incorporate biological domain knowledge to study if the variation of mutations in a cell has an impact on the cell's gene expression profile, while handling lineage confounders.

This project falls within the EPSRC Mathematical sciences, and Physical sciences areas.