Dissecting tissue spatial organization using machine learning and spatial transcriptomics

01 November 2020 → Ongoing
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Machine learning and decision making
    • Bio-informatics
    • Computational biomodelling and machine learning
    • Single-cell data analysis
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.