I have been using the
WGCNA package (https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/faq.html) for correlation networks.
I understand what a hard threshold is: absolute value of correlation matrix, choose a cutoff (e.g. 0.85), anything above is considered connected in the network. But then there is soft thresholding which is when you exponentiate the correlation matrix and that accentuates larger connections.
How do you then decide which ones are connected or not? Do you do a hard threshold after the soft thresholding?
Basically, going from `|correlation|` => `exponentiated(correlation)` => `adjacency_matrix`
`One potential drawback of soft thresholding is that it is not clear how to define the directly linked neighbors of a node. A soft adjacency matrix only allows one to rank all the nodes of the network according to how strong their connection strength is with respect to the node under consideration. If a list of neighbors is requested, one needs to threshold the connection strengths, i.e. the values in the adjacency matrix. When dealing with an unweighted network, this is equivalent to the standard approach of hard thresholding the co-expression similarities since the adjacency function is monotonically increasing by definition.`
Can you change the hard threshold for the soft threshold or is that hard-coded in there? If so, what is that threshold?