Bayesian statistical data integration of single-cell and bulk OMICS datasets for accurate prediction of clinical outcomes in rheumatoid arthritis

Supervisors:

Prof Stefan Siebert

Dr Thomas D. Otto 

PhD project summary: 

Precision medicine has the promise to help the prediction and understanding of diseases. One challenge is generating computational methods to combine and analyse biological (including genomics, Transcriptomics and proteomics) with clinical data. In this project the student will develop new statistical methods to combine biological and clinical data (such as age, smoking status, phenotype of disease and drug treatment) of immune mediated inflammatory diseases, focusing on rheumatoid arthritis as exemplar. The aim is to combine different datasets and cross reference them through Bayesian statistical framework in the latent space. This project will be supervised by an academic clinician (Stefan Siebert), a statistician (Mayetri Gupta) and a bioinformatician (Thomas Otto), reflecting the interdisciplinary nature of this proposal.