Gene_coexpression_scripts

Once you obtain your gene expression matrix

1. Calculate random background gene co-expression

2. Calculate pathway EC and random pathway ECs

   EC=expression coherence=(# of gene pairs with PCC > PCC95)/total # of gene pairs

   For a pathway with n genes, total number of gene pairs is taken as: [n*(n-1)]/2, without the self-pairs

3. Clustering

   kmeans, hclust (ward, complete and average linkages), cmeans, akkmeans, WGCNA

4. Visualize clusters

   1. Normalize expression matrix: all values are normalized from 0 to 1 per gene

           python normalization.py <expression matrix> <row or col>
      

   2. Combine the expression matrix with each cluster

           python combine_exressionmatrix.py <cluster file> <normalized expression file>
      

   3. Get visualized expression cluster

      input is the output from step 2. This script is meant to run locally on your computer.

           coexpression_profile_from_cluster_plot_loop.R