I'm interested in extrapolating what transcripts dictates differences between sample in a PCA from RNA-seq data. 
[This][1] is the PCA of RNA-seq on different samples (**A**,B,C,**D**,E,**F**,G):

Samples are in duplicate and are circled for better understanding. **Bold** samples are controls, samples connected with arrows are treated samples.

What I want to understand is what are the genes that "moves" **A** to B (Red Arrow) or **A** to C (Blue arrow).
Given the partially similar trajectory of **A** to C and **D** to E the second question would be to check whether some genes in  **A** to C are also responsible of the movement of **D** to E. 
This same comparison can be made from  comparing **A** to B and **F** to G.

Do you happen to know a tool that can allow me to better address this questions? 


  [1]: https://ibb.co/7Rd81D7
First of all, there are no trajectories in PCA. It's not a graph/network.

You can get the 'n' list of genes that are most variable between any two groups by rowVars function that is part of several R packages. These highly variable genes are the drivers of PCA. Usually top 500 genes are considered for PCA.