Transcriptomic and proteomic data analysis: extraction of landscapes through geometric dimensionality reduction and multi-scale clustering

This project will employ a new geometric dimensionality reduction and multi-scale clustering method to integrate multiple omics datasets to achieve a more holistic understanding of the toxicology and metabolism of agrochemicals.

This studentship will employ a new method for clustering, which is able to identify groups of metabolites/proteins/genes that respond in a functionally similar way to a given biological process, and will apply it to a unique set of data (Syngenta) concerned with the toxicology and metabolism of agrochemicals. By unravelling the connectivity and interdependence of pathways associated, this platform will be able to identify groups of proteins/genes- that respond in a functionally similar way to a given biological process and will lead to the discovery of early toxicological predictors and biological markers.