Bioinformatics tools for cellular communication analysis from single cell RNA sequencing data

Cell-cell interactions play a fundamental role in the coordination of all the cellular activities, and are thus pivotal for the physiopathology of the living organisms. Aberrations of molecular messages during communication can lead to the onset of diseases, thus their identification provides valuable biological insights. In the past decade, the advent of single-cell RNA sequencing pushed forward the field of transcriptomics and recently several bioinformatics methods have been developed to infer and quantify ongoing cell-cell signalling. Specifically, the coordinated expression of cognate genes encoding ligand-receptor pairs can be exploited to infer intercellular communication. A more accurate evaluation is provided by quantifying the activation of downstream signalling pathways, i.e. intracellular signalling, as a means to validate the upstream intercellular communication. Nonetheless, deciphering the inferred cell-cell communication data remains a complex and time-consuming task due to the richness and high dimensionality of data. Thus, this thesis focuses on the development of CClens, an interactive dashboard for an effective and time-efficient analysis of cell-cell communication data. The developed dashboard provides customised filtering and subsetting of the data, together with a wide variety of powerful and interactive visualisations, plotting different aspects of the uploaded dataset in terms of both intercellular and intracellular signalling.