Bottom-up phenotyping of tissues across cell-types (Professor Christoffer Nellaker)
PROJECT TITLE
Bottom-up phenotyping of tissues across cell-types
SUPERVISORS
DESCRIPTION OF PROJECT
We have developed a bottom-up histology analysis pipeline (HAPPY) which we trained in a supervised fashion for placenta tissue phenotyping. This is a powerful approach of analysing tissue structures using graph convolutional network analysis of cell communities. However, this is not sustainable to expand to other tissues due to the difficulty of manual annotation of cell types.
A project with the group would focus on building self- and weakly-supervised modelling approached to extract low dimensional representations of cell morphologies. There are existing models that perform this task (e.g. cellViT) which will be used as benchmarks. The project will focus on improved self-supervised learning models, and utilizing published spatial transcriptomic and histological datasets as a source of weak labels for training.
We will utilize cell representations to build self-supervised graph convolutional networks to capture tissue structures. Better cell representations should lead to better tissue structure discrimination and therefore be a means to measure the performance of models.
We would then look to apply these to reproductive tissue questions, which is a main focus of the group.
The broader ambition is to build a research tool set to enable tissue analysis and quantification across all tissue types.
TRAINING OPPORTUNITIES
- Research training in machine learning, statistics, quantitative genetics and beyond
- Membership to journal clubs and research group to understand current research landscape, cutting edge methodology and interdisciplinary applications
- Skills training in presentation, scientific writing, funding applications and scientific leadership
- Training plans will be developed holistically to benefit the scientific needs and interests, as well as the career goals of the individual.
Funding Information
The position is not currently funded and therefore the candidate will need to secure funding.
HOW TO APPLY
To apply for this research degree, please click here.