PCA works best when the input data is approximately normally distributed on each dimension. It would be a good idea to do some initial data quality checks to verify that this is the case (and transform the data appropriately if not), or at least verify that the data is approximately normally distributed in the aggregate.
For looking at Illumina RNASeq data, what worked best for me (i.e. produced the most normal-looking data) was the following steps:
Removing genes that had low raw counts in all samples
Using DESeq's variance-stabilized transform (which transforms counts into a log-like distribution)
Further normalising the VST values by dividing by the longest transcript length within each gene (which I call VSTPk)
These steps are stated in a bit more detail in our Th2 paper that was published at the end of last year:
http://jem.rupress.org/content/early/2016/12/01/jem.20160470#materials-methods
