Experiment 1: mice of genotype1 compared to their wild type littermates,
Experiment 2: mice of genotype2 compared to their wild type littermates.
(Each experiment in its own right is pretty straightforward: To determine whether the experimental treatment (=the genotype) has a significant effect on the variable measured, do a two-sample t-test.)
My problem is the following: Experiment 1 and 2 were conducted in an independent way from each other. They measure the same variable and have different experimental groups (mice of two different genotypes), with their respective wild type littermates serving as control group (the wild type littermates of both experiments have the same genotype, but are from different cages & different generation of mice and cannot, for various reasons, be considered identical).
Another confounding problem is that experiment 1 and 2 were conducted and analyzed by 2 different observers, which is yet another reason to consider them independent from each other.
What I would like to do is compare the mice of genotype1 with the mice of genotype2. I could basically just go ahead and do that with the data I have, but because of the reasons outlined above and because it's considered good practice in mouse experiments to always only compare littermates (in an effort to reduce inter-subject variability), this is not feasible.
So probably the only way to achieve this comparison (somewhat dirtily) is to somehow normalize the data. Unfortunately, there is no inherent association or pairing between subjects in the experimental and control group, so the only strategy I can think of is to subtract from the measured variable of each experimental subject the control group mean and divide by the control group SD. I would then compare the normalized data using a independent two-sample t-test (after testing for equal variances).
I am skeptical whether this approach is legitimate and would greatly appreciate any comment or clarifying question.