I am creating a gene expression network and this post is mainly to have someone check my methodology but also I have a question regarding the edge weights that are created using a soft threshold.
My plan is to use gene expression data to create a similarity matrix using Pearson's Correlation. Then to create a weighted adjacency matrix, and finally a weighted topology overlap matrix (note: this is the TOM for similarity). I believe the typical thing to do now is to create a TOM for dissimilarity and use that as an input for clustering, but I am going to skip this step. As I understand it, now I should have a weighted TOM with values between 0-1 which can be used to create a network using
graph_from_adjacency_matrix from the
igraph package. I know the TOM isn't technically an adjacency matrix but more like a 'polished' version which might still be used to create the network.
My question is that if this signed, weighted TOM network is valid, do I need to normalize the edge weights? is there anything I need to do or this should be ready to go "out of the box"? I've read some papers that discuss transforming or normalizing the weights using an inverse CDF, I'm not sure if I need to do that to the WGCNA edge weights or are they good as is.