PhD Studentship: Statistical Modelling for Single-Cell RNA Sequencing Data for Crucial Health Applications

Statistical modelling for single-cell RNA sequencing data for crucial health applications

Supervisors: Magdalena Strauss (Mathematics and Statistics),

Akshay Bhinge (Clinical and Biomedical Sciences),

Marc Goodfellow (Mathematics and Statistics)

Single-cell RNA sequencing can quantify the activity of each human gene in individual cells. This has allowed much better insight into heterogeneity across different cells, and helped identify potential drug targets in precision medicine. This fully funded PhD project applies principled statistical modelling to help identify causes of disease and potential drug targets, and to understand mechanisms behind resistance to drugs. 

Application 1: identifying potential therapeutic targets for ALS

ALS is a fatal neurodegenerative condition characterised by a loss of motor neurons that leads to progressive paralysis and death usually within 3-5 years post-diagnosis. The analysis performed as part of this PhD project will aim to identify key genes as potential therapeutic targets, i.e. genes that will lead to a desired therapeutic outcome if targeted by a drug.

Application 2:  identifying mechanisms of drug resistance in cancer

When cells divide, mistakes lead to DNA mutations. DNA mutations are associated with many diseases. Precision genome editing allows the re-creation of mutations at large scale in a lab. The student will investigate the mechanism with which mutations cause drug resistance in cancer, in collaboration with the Coelho lab at the Wellcome Sanger Institute, who will be performing the experimental work.

Application 3:  identifying causes of congenital heart defects

Congenital heart defects (CHDs) are the most common birth defect, affecting ~1% of live births. Despite advances in medical and surgical interventions, they are a leading cause of foetal death and infant mortality. Despite this over half of all CHD cases have no definitive cause. In collaboration with the Tyser lab (University of Cambridge) the student will explore the relationship between phenotype and genotype using CHD models to define mechanisms which could underpin disease.

Statistical challenges

The applications described above have a complicated multi-level structure. Data include several patients or replicates, several types of cells, several different genetic backgrounds, or groups of cells edited using different reagents. Accurate modelling of structures and dependencies in the data is essential to control the rate of false positives concerning the identification of potential therapeutic targets. Further statistical areas relevant to this project include high-dimensional statistics and uncertainty quantification. 

Candidate suitability

This PhD studentship is a great opportunity for a student interested in applying statistics in a collaborative and inter-disciplinary setting, with impactful applications in medical research. The project will suit students with a strong background in an appropriate quantitative subject such as mathematics, statistics, machine learning, computer science, bioinformatics, physics or econometrics, and an enthusiasm for medical applications. Applicants for this studentship must have obtained a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. Preferably, applicants should have or be about to obtain, an MSc degree, or the equivalent qualifications gained outside the UK. Experience in coding in Python or R is essential.

For eligible students the studentship will cover Home fees plus an annual tax-free stipend of at least £ 20,776 for 3.5 years full-time.

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