I have an RNASEQ experiment with 6 samples (3 treatments and 3 corresponding controls, i.e. A,B,C and ctrlA, ctrlB, ctrlC).
I used cuffdiff to determine the differentially expressed genes/isoforms for the 3 comparison: A vs ctrlA, B vs ctrlB, C vs ctrlC and got some sets of interesting genes/isoforms.
When I compute the correlation between the samples and then clustering, I was expecting to see either the treatments clustering with their corresponding control or clustering together but separately from the control. Instead they all have high correlation > 0.98 with each other (treatment and control).
Is it possible to have DE genes among samples that are highly correlated? My first interpretation would be that the genes have a different expression magnitude across samples, but their trend is conserved across sample. Is this correct? What can I do to further test the data?
Thanks in advance
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Pfs
It is possible to have DE genes among samples that are highly correlated. Some treatments just don't cause a wide-spread change in gene expression.
A short answer would be to do replicates to give you more statistical power on the DE genes.
A few things you can ask yourself:
Was there a positive control in your treatments? I am assuming there is something interesting about this treatment that made you want to RNA-seq it in the first place. When you made your libraries, did you have a positive control to make sure your treatment worked?
Do you have any genes or qPCR-ed genes that are known to be down/up regulated after the treatment? You can compare your results and see if they match.
How did you map and calculate the expression levels? Did you use RPKM or tags for the DE analysis?
Did you ribo-deplete the libraries? What is your estimated coverage of the transcriptome?
IMHO, a bit of formatting will further enhance the readability of this excellent answer.
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