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Estimating numbers of molecular ‘species’ in a tissue

Project Description

An experiment to profile e.g. proteins or messenger RNAs will capture a sample of those molecules. There will be a random element to the sample but, depending on the technology used, there will be characteristics that influence the chances of seeing a particular molecule. Most obviously - the more copies of a molecule present in a tissue, the more likely we are to detect it.
When multiple experiments have been run, the tendency in the literature has been simply to sum the number of different types of molecule seen across the experiments. This is clearly only a lower bound on the number of different types of molecule present, and could be improved upon. Better estimates may have an important role to play in experimental design and quality control. This project will look to improve upon current approaches, borrowing ideas from ecological statistics. Factors such as uncertainty in the individual experimental results, biological heterogeneity, knowledge of biological pathways and cross-platform correlations can then be incorporated.

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group (CREEM). Some are open to international students, some to EU and some UK only.

Applicants should have a good first degree in mathematics, statistics or another discipline with substantial numerical component. Applicants with degrees in other subjects (e.g., biology) should have the equivalent of A-level/Higher mathematics, and experience using statistical methods; such candidates should discuss their qualifications with the Postgraduate Officer. A masters-level degree is an advantage.

Further details of the application procedure, including contact details for the Postgraduate Officer, are available at View Website

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

Research output data provided by the Research Excellence Framework (REF)

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