There are various methods and they are all fundamentally based on the construction of correlation matrices.
For one, you can follow my tutorial here, which utilises the igraph package in R:
Network plot from expression data in R using igraph (start from Step 2).
WGCNA has been and still is quite popular, but there are very robust tutorials that you can follow:
WGCNA: an R package for weighted correlation network analysis
STRINGdb is increasingly popular and it can build your network using known protein-to-protein interactions, I believe:
STRING: functional protein association networks
Finally, if you want to follow an entire 'pipeline', then you could follow recent work that I completed with a colleague, which has just been placed on bioRxiv:
New insights in Tibial muscular dystrophy revealed by protein-protein interaction networks - it's a small study but the network methods employed, utilising STRINGdb and Cytoscape + Cytoscape plugins mainly, are standardised network parameters that the biological community are only now beginning to use more and more, such as:
- Hub score
- Betweenness centrality
- Closeness centrality
- Vertex degree
*these can also be calculated via igraph in R