Prostate cancer (PCa) is alarmingly common (12% of men are diagnosed) and often fatal (9% of male cancer deaths). The identification of molecular disease subtypes is a critical step in developing an effective personalised approach to treatment. Several molecular subtype frameworks have been proposed by applying unsupervised machine learning techniques to bulk gene expression data, including DESNT developed by ourselves.
Single-cell sequencing has emerged as a powerful tool for studying tumours at the level of individual cells. An increasing number of PCa expression datasets at the single cell level have been generated. No one has yet investigated existing subtypes in single cell data. This provides an opportunity for us to investigate the cancer biology behind existing subtypes and discover new ones.
In this PhD you will gather all PCa single cell RNAseq datasets and process them through a common analytical pipeline. For each cell, you will determine existing subtypes and analyse them to determine their biological relevance. Finally, you will apply cutting-edge mathematics approaches to discover new subtypes that could improve patient care.
This is a PhD in bioinformatics/data analysis. During the PhD, you will learn how to deal with "Big Data," high performance computing, pipeline development, and statistical analyses. We have extensive experience helping people to become experts at the forefront of cancer, biology, and data science. A training programme designed specifically for you will be created. You will be a member of the Norwich Medical School's Cancer Genetics team, which is an interdisciplinary team comprised of bioinformaticians and lab-based scientists. We will provide specialist support through our international collaborations. We have a broad interest in translational cancer molecular studies with the goal of improving patient care including urine-based biomarker development, whole genome sequencing studies, cancer-subtype detection, and bacteria in cancer studies.
A minimum of a 2:1 honour degree in Computer Science, Physics, Mathematics, Engineering, Biological Sciences, Biochemistry, or Biomedical Science.