We will construct networks of the 131 immune measures, where a node is an immune measure and links are formed when two immune measures are similar across mice. We will construct these networks separately for wild mice (our unique data) and lab mice (published data), and then analyse the meso-scopic structure of networks called the core-periphery structure (including community structure  as a special case), to identify densely-connected core nodes and peripheral nodes. Masuda has recently improved algorithms to do this [5, 6]. In addition, we will analyse these for correlation networks, which are a restricted class of networks . We will accommodate the algorithms to find communities and core-periphery structure to correlation networks to derive unbiased results. We will also analyse the networks’ centrality, to determine which immune measure is central (i.e. important) in the network. In particular, the betweenness centrality will identify key immune measures that are located between different communities of immune measures in the correlation network.
|Alternative title||Immune State Networks|
|Effective start/end date||1/03/18 → 8/06/18|
- Jean Golding
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