But OP generated normalized counts using edgeR, which is a more sophisticated method that FPKM. Wouldn't edgeR normalization be superior to a simple FPKM transformation?
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Could you provide a little more info on your design? What are you hoping to correlate? If you wanted to find the most highly expressed genes for example then using the normalized count matrix will suffice. If you are looking to compare expression profiles between genes across a series of samples then an additional standardization or transformation would help.
I also agree with Wouter that your edgeR normalization is better than FPKM.
I'm trying to find correlation of expression levels of a small set of genes belonging to one type of cancer with different sample set, but not across different types Cancers sample set.I hope I made myself clear, What be the right course?