How to cluster the human genes by pathways/system-biology/metabolic properties? I would like to make a chord plot from my data. I have a list of genes that according to my experiments are divided into 64 clusters of enrichment pattern. 

I would combine my 64 clusters with a second clustering. A clustering of all the human genes into subgroups of "interacting" genes (e.g. both are pathway, co-expressed) will allow me to combine the two clustering data on a chord plot. 

In the analogy of the following graph the continents will be my 64 clusters of the genes and the edges/chords will be assigned based on the biological function  clustering. I guess that such a functional clustering can be done ore has been done using platforms such as: KEGG, genescf, or reactome.org.




Is there a biological pathway clustering of genes for lymphoblastoid or breast cell lines?
Are there any suggestions how to build the clustering between genes whose functions are strongly correlated in the cell?


Edit: the question is about getting the clustering from a system-biological perspective not how to plot chord diagrams. 

Is there such a metric to quantify how likely two genes are correlated in their enrichment, function etc. For example, using STRING we can see that PIK3CA and PTEN are more co-functioning than PIK3CA and SF3B1. How can I do something like String on genome wide scale.
To measure if two genes are functional similar I developed the BioCor package. It calculates a similarity score between genes by the measuring the amount of pathways  shared. However, it doesn't take into account the strength of the connection between two proteins. You could multiply the similarity by a value between 0 and 1 according to the number of evidences of the relation between the two proteins.

It is optimized for large amounts of data, you could even perform them in parallel, but I doubt you find information about pathways for most of the genes (In my informal test, I think that I could calculate similarities for about 1600 genes not the ~20000 of the human genome).

When I am interested in co-expression data I calculate the co-expression by correlation and add the similarity of the genes calculated with BioCor. The resulting distance matrix can be used for clustering. Maybe the section about using weighted co-expression network analysis and BioCor can be helpful.

To have a "biological pathway clustering of genes for lymphoblastoid or breast cell lines", you would need to have a specific signature/pathway of the cell lines. Currently I am not aware of accepted standard signature database. The single cell atlas could be useful to find expression of your cells, but I don't know if they provide a signature for cell line