Biological data, 3x3 independent assays this is an example experiment design for which I would need some help (the values are the same between assays but should be different). 

I want to test if the use of drug 1 or drug 2 has a significant effect on the output being measured. The control group was not exposed to any drug. The assay is then repeated 2 more times for statistic significance. Each assay is done at different days with different cells and media. 
The problem comes when a statistical test is to be chosen. Here are my thoughts: 


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*For each assay individually, ANOVA should not be used because I don't want to compare 3 groups, I want to compare the treated groups with the one control group. So, I would do t-test for Control Vs. D1 and Control vs. D2. Is this correct? 


But then, there's the 3 assays considered all together and the representation of the data. 
Doing a normalization, with Control being 100% and doing the mean of the 3 assays according to the % is a good way to show the data but statistics wise I don't know how to do this. Pooling all measurements for each group and calculate the mean (doing then the t-tests again) doesn't seem appropriate because even the control group usually shows variations. Normalizing, with Control being 100%, seems even wronger, as the Control group(s) won't have any deviation values.
I thought about introducing the Mean-SD-N for each 3 assays but Graphpad doesn't seem to accept that for t-test and 1wayANOVA.
 A: The stacking approach (pooling 3 experiments) is a bad way to go. You will absolutely have to account for the random, uncontrolled variation between experiments. Even if the protocol were followed to a T, who knows what differences in atmospheric pressure might have affected your mass spec's calibration. Control of variation can be achieved in a number of ways. 
The heirarchical approach would involve calculating test statistics for each experiment and combining them using weighting to obtain a pooled statistic. This is similar to a meta-analysis and makes few assumptions about the variance between samples. 
The mixed effect approach involves using either a MANOVA or repeated measures ANOVA by using fixed or random effects indicating the experimental iteration. I would strongly favor the fixed effect approach because, while it takes a toll on the effective degrees of freedom for the experiment, it handles the between-experiment variation very efficiently. (Whereas random effect inference  would depend upon an n=3 estimate of a normal random effect variance which is unstable indeed).
Secondly, I take issue with your idea to combine treatment groups. Without knowing dosing, or methods, one cannot assume the 1:1:1 assignment to trt1 / trt2 / ctl is in any way representative of a meaningful group when you combine trt1:trt2. This is not a balanced design issue, but the fact that the rather strong assumptions about trt1 and trt2 being a single treatment make inference extremely hard to interpret. If, on the other hand, trt1 and trt2 corresponded to different dosages of the same drug (or could be combined in a logical way), it could be valid to assign a single treatment variable, continuous, with 3 levels for 0 (control) trt1 dosage and trt2 dosage.
If your goal, however, is to test the hypothesis "H_0: treatment does not yield response" vs "H_1: at least 1 treatment yields a response different than control". You can use a likelihood ratio test to fit the "full" model (with treatment effects for trt1 and trt2) versus the "reduced" model (with no treatment variable) and conduct the 2 degree of freedom likelihood ratio test. I'm not too fond of the interpretation of those results, but it does afford you a lot of power to detect differences simultaneously, relative to conducting two tests: trt1 vs ctl and trt2 vs ctl and handling their multiplicity using Bonferroni correction.
PS before I get rained upon with "gentle" correlation/causation reminders, I'll note that I used the causal statement of hypothesis because the question deeply suggested this was some kind of in vitro (efficacy) test.
