13 months ago by
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