Which statistical test should be used to compare two groups with biological and technical replicates? I'am conducting a drug screening experiment samples grouped by two conditions (expression or not of a specific molecular receptor). For the group expressing the receptor I have four diferent samples, while for those not expressing that receptor I have five different samples. And I am testing 10 different drugs on all samples. Each drug-sample screen is conducted in three technical replicates.
Now, I'm interested to know if there is statistically different sensitivity to each drug between both groups of samples.
How can I deal with the technical replicates statistically? Do I need to caculate the mean value of sensitivity for the three replicates for each sample? And then employ a non-parametric test, like the Kruskal-Wallis test?
Do I need to consider the multiple testing issue?
Eg:

*

*Drug A tested in samples A-D (group of cell lines expressing the receptor) in three technical replicates and in samples E-I (group of cell lines not expressing the receptor) in three technical replicates.


*Drug B tested in samples A-D (group of cell lines expressing the receptor) in three technical replicates and in samples E-I (group of cell lines not expressing the receptor) in three technical replicates.
...
And I want to compare the sensitivity of both groups, for each drug tested.

A mixed-effects model (a.k.a. hierarchical model) with a random effect to account for the technical replicates would be the standard way to analyse your data. Random effects are not ideal when the number of groups is as small, though, and that may introduce problems.
Other comments:

*

*There's no need for a nonparametric test, and no need for multiple testing corrections.


*"I want to compare the sensitivity of both groups, for each drug tested." This suggests that you want to put the response to both drugs in one model. It's likely possible but I can't recommend a good solution without more details about your experiment.


*FWIW, what you mean by 'sample' is not very clear. Looking at the original version of your question helped me understand it better; I think 'cell line' is more clear, though more detail about the experiment in general would help.
