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.

The primary CDT impact will be training 75 PhD graduates as the next generation of leaders in statistics and statistical machine learning. These graduates will lead in industry, government, health care, and academic research. They will bridge the gap between academia and industry, resulting in significant knowledge transfer to both established and start-up companies. Because this cohort will also learn to mentor other researchers, the CDT will ultimately address a UK-wide skills gap. The students will also be crucial in keeping the UK at the forefront of methodological research in statistics and machine learning.
After graduating, students will act as multipliers, educating others in advanced methodology throughout their career. There are a range of further impacts:
- The CDT has a large number of high calibre external partners in government, health care, industry and science. These partnerships will catalyse immediate knowledge transfer, bringing cutting edge methodology to a large number of areas. Knowledge transfer will also be achieved through internships/placements of our students with users of statistics and machine learning.
- Our Women in Mathematics and Statistics summer programme is aimed at students who could go on to apply for a PhD. This programme will inspire the next generation of statisticians and also provide excellent leadership training for the CDT students.
- The students will develop new methodology and theory in the domains of statistics and statistical machine learning. It will be relevant research, addressing the key questions behind real world problems. The research will be published in the best possible statistics journals and machine learning conferences and will be made available online. To maximize reproducibility and replicability, source code and replication files will be made available as open source software or, when relevant to an industrial collaboration, held as a patent or software copyright.