Project

Dissecting tissue spatial organization using machine learning and spatial transcriptomics

Code

01D16720

Duration

01 November 2020 → 31 October 2021

Funding

Regional and community funding: Special Research Fund

Promotor

Yvan Saeys

Fellow

Lotte Pollaris

Research disciplines

Natural sciences
- Machine learning and decision making
- Bio-informatics
- Computational biomodelling and machine learning
- Single-cell data analysis

Keywords

Machine learning bioinformatics single-cell omics

Project description

In this project, we aim to better functionally characterize different spatial contexts within tissues. To this end we will develop novel bioinformatics pipelines to process and integrate several “omics” and imaging data types. Novel machine learning methods will be explored that aim to combine the high spatial resolution of imaging techniques with the deep phenotyping capabilities of current scRNAseq methods.