A quantitative comparison between microarray, deepSeq, qRTPCR and Northern is in my opinion (and our experience) difficult and sometimes not possible. I trust Northern data most though they are not very sensitive. Keep in mind that these methods assume (!) e.g. equal efficiency in RT and priming (for qPCR), equal efficiency in ligation and RT for deepSeq. A factor of 2 is just one more cycle in qPCR! I agree that changes by a factor of 1.8 could be biologically very important but is it not strtching confidence in microarrays a bit too much?

Ceyhun, I have more questions than answers, but perhaps they will help us to get to the correct answer.

Are you using the same RNA samples for your qPCR that you used for the microarray? Which microarray platform are you using? Are the genes demonstrating downregulation by microarray initially highly expressed? I have used both the mouse and human ST arrays from Affymetrix and I have found them to have good dynamic range. However, if the majority of your downregulated genes initially show low signal intensity (suggesting a low transcript level) then any apparent reduction may just be due to technical error, which of course would not be apparent in qPCR.  So you want to set a relatively high background level cutoff if you want more reliable results.

Although qPCR is more sensitive, if your Ct values are greater than about 32, you will find that detection of less transcript will be difficult since data much greater than 32 Ct will be considerably less reliable.

How many genes are showing the phenomenon that you are describing? Are only the genes that initially showed low signal intensity giving you problems with qPCR or is it all genes that have shown downregulation?

Hopefully a few more details will get us to your answer.

Best.

The reverse transcriptases may be different between the platforms.  Self-priming activity of RTs could cause such a result. Try a control reaction where no exogenous primers are added to the cDNA synthesis reaction.  Increasing the temperature in the cDNA synthesis reaction may help reduce self-priming.

Thank you all for the answers,

Dear Wolfgang, you are right about the efficiencies and the factors, but the biggest factor is 4 for down-regulated genes im my data. 

Dear Ricardo, about your questions;

1) Yes, they the same RNA samples

2) I have used Affymetrix

3) The Ct values of down-regulated genes that I have tried so far are between 19 and 26

4) So far I have tried 6 down-regulated genes, except one they all showed the same problem and the Ct values of the gene that I found it down-regulated in qPCR are approximately 19, but the fold change is near -1.8 while it was -2.5 in microarray.

Both in up-regulated and down-regulated genes, Ct vales were lower than 28 so far. Also, I am going to try more genes.

Dear  Thomas, I am using high fidelity reverse transcriptase in cDNA synthesis for qPCR but I do not know which one Affymetrix uses. 

I want to add that the Ct values I mentioned above came from 3 ng cDNA (theoritically, if efficiency of cDNA synthesis was 100%) in 20 ul qPCR reaction.

1.8-3.5 fold down-regulation is quite a small change in expression and could be difficult to detect by any method. You mentioned the Ct of the candidate downregulated genes in RT-qPCR to be 19 and 26, which suggests that these transcripts are quite low abundance. In my experience, I found microarray data often shows "statistically significant" changes in genes of low abundance that are not detected by other methods (RT-qPCR, northern, and RNA-seq). I would guess the reason to be noise fluctations in microarray, since the background signal being inherently variable across the array is rather difficult to correct for. If these low abundance genes are critical for testing your hypothesis, then the only solution is more replicates in RT-qPCR to get more statistical power.   

Normalization of microarray data may be an issue, e.g., if your cultures are in very different growth state. Standard methods like "quantiles" assume that both samples should have the same signal distribution but this is not necessarily the case. So genes that actually show little changes and are not highly expressed may be "pushed down" by the normalization. See e.g. attached references (go to supplement for the yeast paper) and citations therein. Try e.g. LVS (least-variant set) normalization available in R.

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