I perform differential expression analysis on my microarray data but only 4 genes found to be expressed at fold change >2
and FDR < 0.01 and only 14 genes in FDR < 0.05
how it possible?? then I have to only count on pvalue?
Is that a log fold change, or actual fold change?
it is a log2 fold change. by pvalue < 0.05 , 500 genes and pvalue < 0.01, 151 genes at fold change > 2 deferentially expressed.
actually i don have replication.I have 4 treatments versus 4 controls which treatment is in 4, 8, 24 and 48 hours. box plot for QC showed the data are very well.
If you're looking for a log2 fold change of 2, then you're actually looking for 4 fold differences.
Those numbers are more reasonable. If you want a treshold on the fold change, you should use something like :
to keep both under-expressed and over-expressed genes (with a ratio of at least two fold).
How big is your dataset? Is it possible that there is no biological difference between your samples? Is it possible your samples are contaminated? Have you QC'd the microarrays?
If your data tells you something you didn't expect you can't automatically assume that the data is wrong. Maybe that is what is real.
Alternatively maybe there is human error in the processing, or stricter parameters than are required. Fold-change < log2 can still be interesting, there's no real reason to cut off there.
FDR of < 0.01 is stricter than I would usually use (< 0.05), but you can't just ignore FDR (That's like saying I don't care if I have false positives, as long as I get positives).
But maybe what you have is the real biological difference. Here is my new favourite picture to illustrate the point:
I don't know what groups you compared? Did you expect a lot to change or not?
If you want to loosen up, try a FDR < 0.05 (don't use uncorrected p-values), and get rid of the fold change cut off.
I think a volcano plot would be useful to see if the low number of DEG comes from the FDR cutoff or fold change cutoff or both.
To add to the other suggestions, low number of DEG could be caused by very bad replicates (or worse, wrongly associated replicates).
Login before adding your answer.
Use of this site constitutes acceptance of our User Agreement and Privacy