Selecting gene list for subsequent analysis in problematic microarray experiement I have an experimental design problem and I'm not sure which would the best way to proceed.
We have a micro-array experiment in which we compare gene expression profiles between 2 groups of patients. Each group has 12 independent biological replicates. Lets call these groups CT and EXP. The goal of this experiment is to identify differentially expressed genes between CT and EXP.
Two sets of hybridizations where done: first and second.
First hybridization had big issue: CT samples were processed at one time-point and EXP couple of months later, creating a perfectly confounded batch effect.
Second hybridization did not have this problem - all of the samples were processed at the same time.
I've analyzed both sets separately and came up with 2 lists of differentially expressed genes. 
My question is, should I discard the results of the first hybridization completely and stick with the second one since it didn't have an obvious design issue? Or should I use both sets of hybridizations and select only genes which show up as differentially expressed in every case (there are 2900 of such genes)?
Appreciate your help!
 A: In my opinon you should PCR "confirm" a subset you think is likely interesting (based on theory/guessing) either way. There is a history of problems with microarrays giving "false positives". It is not just batch effects, but also position on the chips, baseline differences, etc. Also keep in mind that different expression profiles can result in the same phenotype, so only looking at averages rather than also looking for within-group clusters of similar profiles may be suboptimal. There are a number of reasons that the observed differential expression may not be clinically important. You may have reviewers who think the same. If you do the PCR verification then your conclusions are stronger regardless, just remember if you "verify" enough different genes some are bound to come up as significant. Since the study sounds exploratory (you have no theory predicting certain differences beforehand), the results should be presented as such.
Also, if both arrays are from the same two sets of subjects then it may be interesting to see which genes were not substantially different in one but were so in the other. Is there reason to expect the expression profiles to be stationary/stable over time? Out of curiosity, did you see more apparent group differences in the confounded experiment?
Edit in response to comments: 
Interesting that nearly half the "group differences" can be accounted for by confounding and technical factors. I have no idea if this is the normal amount of variability seen for technical replicates of microarray data (never ran such an experiment myself). If it is much larger than usual it really calls the first study into question and really makes me wonder why there is such a large batch effect. On the other hand, if that is the normal amount of variability it makes me wonder what else is going on that affects the results.
Also if you are using significance testing to determine which genes are differentially expressed, keep in mind this warning by Gelman and Stern:

Consider two independent studies with effect estimates and standard
  errors of 25 ± 10 and 10 ± 10. The first study is statistically
  significant at the 1% level, and the second is not at all
  statistically significant, being only one standard error away from 0.
  Thus, itwould be tempting to conclude that there is a large difference
  between the two studies. In fact, however, the difference is not even
  close to being statistically significant: the estimated difference is
  15, with a standard error of sqrt(10^2 + 10^2) = 14.

Andrew Gelman and Hal Stern. The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant. The American Statistician, November 2006, Vol. 60, No. 4
You got me looking up how variable microarray results are supposed to be and I found a few papers that look of interest. Zakharkin et al reported relatively consistent results for technical replicates. Also it may be helpful to create scatter plots comparing the actual expression levels for each replicate as their figure 4:

We found that technical replicates within a biological replicate had
  higher and more consistent correlations with each other than with
  other biological replicates. Generally, our correlations were higher
  than those observed by Dobbin et al., 2005, for interlaboratory
  correlations between tumor samples [25] and were compatible with
  values for in-lab correlations obtained in another study [29].

Zakharkin et al. Sources of variation in Affymetrix microarray experiments. BMC Bioinformatics 2005, 6:214
Here is another paper that reports batch effects consistent with what you have reported:

In the SMRI brain expression microarray data set, batch effects
  accounted for nearly 50% of the observed variation in expression, to
  which site effects contributed 42% and date effects 7.3%.

Chen C, Grennan K, Badner J, Zhang D, Gershon E, et al. (2011) Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six
Batch Adjustment Methods. PLoS ONE 6(2): e17238. doi:10.1371/journal.pone.0017238
