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.

I'd use the Friedman test. It's used when the groups are dependent (e.g. repeated measures or randomized block design).
The format is friedman.test(y ~ A | B, data) where y is the numeric outcome variable, A is a grouping variable (Group A or Group B), and B is a blocking variable that identifies matched observations.
Here, y is your sensitivity, A is the group (whether it expresses or does not express the receptor), and B is the subgroup/block, each of the samples. This makes logical sense because consider another example where we test if urban, rural, suburban groups differ across a certain metric. These are the groups. In each group we have a sample (of people). We could take repeated measurements on these people. These are the repeated measurements on each sample in your cells.
