PCA: High explained variance in just one principal component I am new to PCA and I'm trying to interpret some results I have regarding some biological data and having some difficulties fully understanding. Basically, this data is composed of solutions (set of 1,2,3,etc... biological reactions) that correspond to columns (reactions) that contain fluxes (Values). It is something like this :
           Reaction A   Reaction B Reaction C
sol_1          10            20         30
sol_2           5            3          2    
sol_3           10          40          40

Since I have a lot of solutions and I would like to further analyse only 2 or 3, my objective is to do a PCA (alongside some HCA) to find patterns and group solutions together to reduce my solution pool number.
In a model I have done, I have these results:

My main questions are:


*

*What can be interpreted regarding such a high explained variance in just one dimension (comparing to previously results I have)?

*Regarding the score plot (individuals plot) what kinda of interpretation can be done from that visualisation? cause it  seems that there are 4 main groups and that they have symmetry between each other.


Any questions you have or further information I can detail please ask! And any insight will be helpful . Thank you !
 A: PCA is a good way to study populations. So, a good starting point is to know which axis represents samples and which represents variables or features. In your case, it seems like solutions are samples. 


*

*In many naturalistic datasets, it's common to see eigen-spectrum plots that look like (A). So it's difficult to interpret much from it other than your samples lie on a line in the space defined by the reactions. 


For example for two reactions you will get a plot that looks like this:



*The score plot might be more useful in your case. Because they are useful for clustering, finding outliers, and identifying implicit patterns such as temporal behavior. 
To take advantage of this, you'll need to use your knowledge of the solutions to figure out what you can say about the score plot being grouped the way it has. For example, let's say you collected the data during different times of the day (morning, evening, etc.) and from these plots you realize that each group corresponds to one of those times. 


For example, a clustered score plot, might belong to a dataset like this:

