Understanding the uncertainty in the decomposition of cancer gene or protein expression data

About the Project

Many tools exist that will take the expression profile of a tumour sample and decompose that signal into components arising from different tissue types (e.g. tumour cells, benign cells, immune cells, fibroblasts, stromal cells etc.).

Few of these tools consider formally the uncertainty inherent in the problem, and those that provide a measure of uncertainty about the final reported decomposition tend to do so in a manner that takes into account only some sources of variability.

In this project the student will consider a general approach to the question and look to develop methods to quantify the uncertainty inherent in the solutions provided. Methods will be motivated by problems identified in the literature, as well as the analysis of primary data arising from consortia in which the supervisors of the project are involved.

The project is supervised by Michail Papathomas who has extensive experience in the evaluation of uncertainty and the analysis of genetic data, and Andy Lynch who has extensive experience in methods for and analysis of cancer sequencing data.

Funding Notes

Fully funded scholarship places (fees, plus stipend of approx. £19,775) are typically available for well-qualified students. UK, EU and other overseas students are all encouraged to apply. Further details of the application and selection procedure are at View Website (pdf - see last page) and View Website.