How can you add control runs to a split-plot design?

Question

In pharmaceutical assays we often run controls on the plate which are used purely for the basis of quality control. The controls should not be part of the statistical model used to generate the DoE, and so previously I have tried to generate two DoEs (one for the main experiment, one for the controls) and merge them together. However, this is not trivial when you have a common split-plot setup, as the levels assigned to the split-plots will be random and so the 2 DoEs will not match up.

As a simple example, let's say I have a plate with a grid of 10x10 wells. I want to test the effects of cell line and reagent concentration. Cell line needs to be arranged in columns and so we need 10 split-plots that align with the columns. Reagent concentration can be randomised. But I also want to test some controls in each cell line, with let's say another reagent at fixed concentration, and without the altering the original reagent concentration. Ideally, I'd want to use most (90) of the wells to optimise the cell line & reagent concentration, and a few wells for the controls (10, 1 per column).

Is there anyway to ensure that the DoE is optimal for its purpose, whilst also adding in some extra control runs that don't adhere to this statistical model?

Victor_G · Answer

Hi @JoffJones,

Yes, in the design proposed, I added the control runs manually. It would also be best considering the randomization of runs inside each whole plots, to avoid having the control runs always at the end of a block.

There might be a solution for very large designs using Candidate set approach, where you define all possible combinations and constraints directly in a candidate runs datatable, and using the Custom Design platform to use these runs as covariates. But there might be some manual work to do after, as I'm not sure the blocking used for cell line factor might be respected, so you might have to further process the resulting design to add block effect. I will try to look more into this topic later.

There is also the possibility to use the control runs as baseline for your response (so not directly in the design), and model the difference between experiments and baseline for each specific cell line and whole plot.

Best,

JoffJones · Answer

Hi @Victor_G

Thanks for exploring this. I was just wondering how you added the control runs into the design? Did you just type them into data table? It would be useful if you know a solution that will easily scale for very large designs.

Best wishes,

Joff

Victor_G · Answer

Hi @JoffJones,

I think I better understand your context, you seems to be in a ituation of nested factors/design.

Here are some examples of such situations in the Community :

Disallowed Combinations not working

Using disallowed combinations to remove a factor from the DoE

I tried adding a categorical level "NogRNA" for your factor "gRNA" and expanding your minimum gRNA concentration from 10 to 0 and then, using disallowed combinations filter to create these conditions :

gRNA == "NogRNA" & gRNA concentration >= 0.01 | (gRNA == "Gene1" | gRNA == "Gene2"
 | gRNA == "Gene3") & gRNA concentration <= 10;

So that Custom Design doesn't create run without Gene levels and gRNA concentration different from 0 or with Gene levels and gRNA concentration less than 10. But the resulting design doesn't include any run with "NogRNA".

So I think the simple option could be to use your script and design, create your design with 80 or 90 runs and 10 blocks, and add 1 (or 2) runs per block with the conditions NogRNA, concentration = 0 and the same cell line as the other experiments in this block. Some modifications have to be done in the Factors column properties, and also in the script Fit Model, as this specific configuration involves nested factors (the concentration range depends on the level of factor gRNA : With/Without Gene). Example of nested factors : Example of a Two-Factor Nested Random Effects Model

Please find attached the design.

Another option could be also to measure baseline level in each block (concentration = 0, no Gene, specific cell line) and use this baseline level in the response as a difference between experiment and baseline (Y = experiment value - baseline value).

This would make the analysis easier, and since your primary interest is not in the specific level value of this baseline condition but more as a check for the cell lines, it could be a nice alternative.

Hope this answer might help you,

Custom Design_gRNA.jmp