How to select the most representative pathways from a gene enrichment analysis? I have perform an enrichment analysis to a cluster of genes. The output is a list of pathways and their p-value (the pathways are selected because p-value &lt; 0.05). The list is still quite long, so I want to reduce it. For that purpose I have a calculated the Dice coefficient of the pathways in a matrix $p$x$p$ where $p$ is the number of pathways in the list. I want both the ones that are more different (they overlap less, their Dice coefficient is lower) and the pathways more representative of the most similar pathways (So if a there is a group of 5 pathways that overlap over 0.8 take just one).

How can I select the most representatives pathways? 

There is a similar tool for GO but it relays on discarding not significant GO, while here all the initial pathways are already significant. 

If I do a clustering of the pathways using the Dice coefficient matrix I don't know where (or how) to cut.



I tried using the height to select the pathways. But I am unsure of the interpretation of height. 

Some other tools I have seen use a multidimensional scaling plot, but I am not sure if performing it and cutting at certain point of the first dimension would help.
This sounds like something that might be amenable to a clustered heatmap plot, or a correlation matrix plot, or something similar. Have you looked at a correlation matrix of the dice coefficient matrix (or maybe just a heatmap plot of that matrix without the correlation matrix)?

The corrplot package looks like it might be useful, in particular the hclust / drawing rectangles presentation.

I can't vouch for this package though; it's just something I found by a search for "R plot correlation matrix".