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PDF, English - main document Download (20MB) | Lizenz: Modelling Cell-cell Communication using Prior Knowledge with Single-cell and Spatially-resolved Omics Data by Dimitrov, Daniel underlies the terms of Creative Commons Attribution 4.0

Abstract

Cell-cell communication (CCC) is a dynamic process which governs and coordinates diverse biological functions. The popularity of single-cell and spatially-resolved transcriptomics has recently sparked an array of computational methods that model CCC to be developed. My thesis describes the development of LIANA+ - an all-in-one framework for the inference of CCC from single-cell and spatial (multi-) omics data. In the first chapter of my thesis, I summarise the current state of the CCC field and state my motivation for the development of LIANA. In the second chapter, I describe the initial study that led to the development of the first iteration of LIANA. I also show that the choice of CCC method and resource can impact biological insights. In the third chapter, I evaluate CCC methods using alternative data modalities, and show that most CCC methods are generally coherent with those. In the final chapter, I summarise the current challenges of the CCC field and I showcase how the modularity of LIANA+ provides a comprehensive answer to those. I further see LIANA+ as a step towards modelling host-microbiome interactions, with already some pilots in place.