This is primarily a statistics question, but the biological context may help frame the problem.
There are two samples treated with different shRNAs. 1 = control shRNA, 2 = target shRNA.
Then PCR is performed on several other (non-target) genes of interest. Several genes change in expression between control and target shRNA treatments, but the extent to which they change is highly correlated with the extent to which the target gene has been knocked down.
Since baseline gene expression/ Ct values are fairly variable between biological replicates, it is good to perform a paired t-test. I have done this for each gene of interest and then of course correct the P value based on multiple testing. This works, however I would like to increase my power by including more replicates of the experiment with variable knockdown efficiency, but have the efficient of knockdown taken into account. I basically need a way to correct for a confounding variable in a paired t-test.
It seems my options may include: 1) ANCOVA. This would likely work great for raw Ct values, but does not seem to easily accommodate paired data. 2) Multiple linear regression. Any advice on how to actually go about doing this would be appreciated (if it is indeed the recommended method)