The Wallace group (https://chr1swallace.github.io) work on understanding mechanisms underlying immune-mediated disease through integrated analysis of omics data.

We seek a statistician interested in human genetics who wants to apply machine learning to estimate cell population frequencies from bulk gene expression data. A common approach to identify dysregulated pathways in diseases is to compare gene expression profiles from healthy vs disease samples. However, the cellular composition of many biological samples is heterogeneous, and, given that many disease processes involve cell infiltration and expansion, such comparisons are confounded by changes in cellular composition. In addition, it is generally recognised that rare populations play a critical role in the pathogenesis of complex diseases, therefore, the ability to measure and interpret phenotypic changes between specific conditions at the cell subset level is critical to obtain a detailed understanding of the role of each cell subset in disease. However, in practice, measuring the frequency of such populations in large cohorts of human samples can be very challenging. Thus, methodologies to infer population frequencies from bulk transcriptome data opens up many possibilities towards understanding disease mechanisms.

https://www.jobs.cam.ac.uk/job/39257/