Scalable Bayes for complex single-cell genomics data
Dr Catalina Vallejos
Single-cell RNA-sequencing (scRNA-seq) is a cutting-edge experimental tool that allows genome-wide quantification of gene expression on a cell-by-cell basis. This information is critical to study complex biological processes, such as early embryo development and cancer. However, the insights offered by scRNA-seq come at the cost of higher data complexity and can be masked by strong technical noise. This project focuses on the development of scalable Bayesian statistical methodology that is tailored to scRNA-seq data, extracting data-driven biological insights that are robust to technical variation.
This project would suit students with statistics, data science, machine learning, bioinformatics or other related backgrounds.