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Start of funding 01.07.2022
A machine learning model to infer differential cell-cell communication from spatial transcriptomics data
Prof. Dr. Fabian Theis
Technische Universität München
Dept. of Mathematik - Institute of computational biology (ICB)
Aicha Bentaieb
Genentech
AIML department
Dr. Angela Oliveira Pisco
Chan Zuckerberg Biohub
Chan Zuckerberg Biohub
scRNA-seq methods allow the study of cells in unprecedented resolution, facilitating pivotal biological discoveries. Spatial transcriptomics (ST) methods expand on scRNA-seq methods by adding positional context, which influences key biological processes such as cell-cell communication and gene regulation. scRNA-seq and ST methods have strongly increased biological data volumes and complexity, so that machine learning methods are needed to achieve scalable and robust analyses. The goal of the project is to understand the role differential communication plays in the formation of kidney cysts, a phenomenon that characterizes acquired cystic kidney disease. To this end, we will build an interpretable machine learning model and train it on ST data to infer cell-cell communication changes that critically affect cyst formation.