The COMBAT (COVID-19 Multi-Omic Blood ATlas) Consortium, led by the University of Oxford, brings together over 200 researchers to determine the nature, drivers and predictors of severe COVID-19 disease through deep immune phenotyping of peripheral blood. Using multiple experimental modalities, clinical information and integrative analyses (including machine learning, mathematical techniques and data visualisation), the consortium aims to identify immune signatures and correlates of host response, and to advance the development of new drugs and personalised medicine approaches in the treatment of COVID-19.
- to identify peripheral blood immune signatures and cellular drivers of severe disease and how these evolve over time
- to determine the nature and basis of observed heterogeneity in severe disease including through a data-led approach to determine evidence for disease subclasses (subphenotypes)
- to identify predictive biomarkers of disease severity and sub phenotypes
- to determine shared and specific features in the pathogenesis of severe COVID-19, flu and all cause sepsis
- collaborative discovery-led multi-omic/immune profiling strategy applied at scale to a new disease in a pandemic
- high resolution atlas of blood immune response with cross platform correlation and validation
- data-driven, systems biology integrative analyses
Please take a look at our first paper published in Cell.
MLV (Multi Locus View) provides all the functionalities for displaying and investigating the data from various modality together with the demographical and clinical variables of each sample in a highly interactive manner.
R Shiny App enables the user-defined analysis with flexible parameters for differential gene expression and PCA (Principal Component Analysis) on the COMBAT bulk RNA-Seq data.