Network analysis of multimodal COVID-19 patient datasets

Over recent years there has been an enormous number of developments in the ways we obtain molecular information from patients with disease. This has resulted in the ability to generate multiple measurements per patient for large cohorts, including levels of various proteins in their blood stream, gene activity across multiple different cell types at the resolution of single cell as well as precise methods of counting the numbers of different cells present in the system of interest. At the same time, there is an acute need to integrate this information and enable using all the data at once and not one at a time. Multilayer networks which at each layer summarize information available from a single experiment and then connect these layers allow us to integrate molecular information across various molecular measurements. We aim to develop statistically robust network methods and apply them to a dataset of more than 100 hospitalized COVID-19 patients of different severities. We also aim to compare them with sepsis, hospitalized flu patients and COVID-19 non-hospitalized patients and healthy volunteers to discover better ways to stratify patients and understand the underlying biological mechanisms driving their disease.