My research is concerned with the development of statistical methodology for the analysis of genomic data in stratified medicine. The identification of relevant patient subgroups (e.g. patients that might be expected to respond similarly to treatments, or to have similar disease progression/outcomes) on the basis of genomics datasets presents a number of challenges. In particular, because genomics datasets typically comprise measurements taken on a very large number of variables (e.g. whole-genome expression data), it is usually the case that we can identify many different patient subgroups, depending on which variables we include in our analysis. In my PhD, I am developing and implementing methods that integrate genomic datasets with data on specific patient outcomes, to ensure that we identify truly relevant patient subgroups.
Permutation tests for the equality of covariance operators of functional data with applications to evolutionary biology