Deconvolution of multilayered networks in single-cell-multiomics
Single-cell-omics is transforming biomedical research and poses new analytical challenges to extract meaningful and experimentally actionable knowledge from the vast amount of information produced in any single experiment. The aim of this PhD project is to exploit network-related tools to create multilayer networks and structures from SC multi-omics data. The scientific community is moving fast to shape tools for visualizing and connecting features extracted from individual cells with analytical algorithms. In the context of this rapidly evolving field, the PhD student will test, validate and benchmark those tools to create an integrated dataset and shape it in a network that permits to extract the most relevant features of a biological model. After the network is created the student will infer the effects of interfering into the system through experimental data. The endpoint of the project will be a well characterized evolution network enabling the streamlined extraction of meaningful biological insight for the disease models under study.