Pathway-centric analysis of spatial transcriptomics data

Gene set enrichment analysis (GSEA) methods in computational biology facilitate the interpretation of biological findings from molecular data and have become one of the cornerstones of biomedical research. Gene set variation analysis (GSVA) is a particular type of GSEA method that enables pathway-centric analyses of molecular data by performing a conceptually simple but powerful change in the functional unit of analysis, from genes to gene sets. The R/Bioconductor package GSVA (https://bioconductor.org/packages/GSVA), with more than 200,000 downloads and 5,500 citations of its corresponding paper, has become the most popular tool for performing this kind of transformation, which is why it was recently featured in Nature among some of the most popular pieces of software for biomedical research over time (see https://doi.org/10.1038/d41586-023-00053-w).

In this project, funded by the Chan-Zuckerberg Initiative (CZI) -see https://bit.ly/3TJhYHm, we propose to adapt this methodology to spatial transcriptomics data, which allows one to quantify the relative concentration of RNA molecules at distinct spatial locations of tissue samples. Spatial transcriptomics provides a better understanding of the molecular dynamics of tissues and this project aims to contribute to that understanding by enabling pathway-centric analyses of spatially resolved transcriptomics data.

Thanks to the CZI funding, we are able to offer a full-time contract for the duration of the master's project, with remuneration at the level of a PhD student, with the possibility of starting earlier, in summer 2023, and extending it over time after the academic year 2023-24, if the student performance is satisfactory.


Supervisor: Robert Castelo