Hi guys, I need to select a core of genes from an RNA-Seq dataset of 200 samples whose expression is able to explain an x-percent of variability of the entire dataset (i.e. across samples).
My point is that x-percent is normally an arbitrary threshold. Could be 75% or 80% according to what the scientist considers to be relevant. Is there a way to set in a quantitative way that threshold? 
Through simulations for example? In other word is there a way to prove that threshold for example below 75% are no more informative to explain the variability of data? 
In my mind I consider 75% a reasonable cut-off but I need to explain to non-statisticians and with numbers that values below are not of interest.


Thank you in advance
This can be done via PCA. Basically, if you perform PCA and then have 70% variation explained by PC1, you can then extract the variables / genes that are responsible for the majority of this [70%] variation along PC1. If your PC1 explains a lower number, like 25%, then you may have to look at numerous PCs that account for, e.g., up to 80% variation.

Curiously, I have implemented functionality for this in *PCAtools* (released with Bioc 3.9), and Aaron Lun has also recently added functionality for identifying the ideal number of PCs to retain.

For Aaron's code, look here: <a href="https://github.com/kevinblighe/PCAtools#determine-optimum-number-of-pcs-to-retain">Determine optimum number of PCs to retain</a>

For identifying variables responsible for variation along each PC, look here: <a href="https://github.com/kevinblighe/PCAtools#determine-the-variables-that-drive-variation-among-each-pc">Determine the variables that drive variation among each PC</a>

This Bioc package is new, so, please make any recommendations for its future development.

Kevin