Spatial transcriptomics combines gene expression data with spatial information, allowing us to see not only which genes are active in cells but also where those cells are located within a tissue. This powerful approach opens up new opportunities to understand how different cell types interact and function together in their native environment.
The
Cell2Sample project aims to advance these insights by developing computational methods that explicitly account for spatial context (
spatially aware). The project has two main objectives: first, to infer
interactions between nearby cells; and second, to link these interactions to intracellular
gene regulatory networks. To achieve this, you will develop and adapt machine learning and statistical approaches to model cell-cell communication based on cell-type-specific spatial gene expression, and reconstruct spatially informed gene regulatory networks.
The project offers a balanced focus on method development, implementation, and real-world applications in oncology and neuroscience. You will work with high-quality spatial omics datasets from our collaborators and help establish a novel computational framework for understanding tissue architecture at single-cell resolution.
Would you like to know more about the different phases within the PhD trajectory? You can read more about this on this page.Your main role in this PhD position is to develop and implement spatially-aware methods to infer interactions between cells and intra-cellular gene regulatory networks. You will critically apply and test these methods to onco- and neurological data sets in collaboration with biomedical researchers. Results will be published in both methodological and biomedical journals.
Finally, you are expected to play an active role in the
ADORE research community, in particular the
Biocomputational Focus group. You’ll work in close collaboration with another PhD student in the Cell2Sample project, who will work on multi-omics cell type deconvolution methods.