What methods can we use to compare networks (PPI, gene regulatory etc) created from single-cell gene expression (or proteomics) data?
There are methods that construct networks by comparing two conditions (e.g. from (gene) co-expression, or differential gene correlation (DGCA, Differential Gene Correlation Analysis)), and infer properties based on these networks.
What I'm interested here is the comparison of networks constructed for each single cell. The networks can be constructed solely from the data, or using known interactions (edges) with the help of a database. These comparisons could be used for understanding or classifying cells.
This will probably require a network construction method (which may be hard because we do not have replicates for single cells), some form of network similarity measure / network alignment method. This is a very hard task in general, but here we have 1-to-1 correspondence for each node between the cells (networks), which should help.
Similar discussions at
Researchgate: How can I measure similarity between two networks?
One related paper for single-cells is Chan et al., 2017, which uses groups of cells (created by other methods, e.g. clustering) to construct networks, and manually compares them (common edges / Venn diagram).