Abstract

Recent technological advances in single-cell transcriptomics have the potential to have a huge impact in the field of drug discovery. However, pipelines that we could use for target identification and validation are only in their stages of infancy. The data produced from single cell RNA -seq experiments is huge, complex, and highly stochastic, so computational methods and mathematical expertise are critical.

In this project, the student will help us to develop a comprehensive and robust pipeline(s) to analyse complex datasets generated through multiple readouts for single cells. As a starting point, the student will use already established methods on single-cell RNA -seq data (and bulk RNA -seq data) to generate gene expression values. Using this the student will produce an analysis (via clear visualisations) on single cell trajectories using appropriate methods of normalisation, dimensionality reduction (eg PCA , SNE), cluster identification etc.

An ambitious student will then incorporate additional phenotypic datasets (eg the results of machine learning analysis on images of the cells) to this pipeline(s), requiring the development of novel methods to analyse multiple data types, ultimately resulting in a master pipeline.