Hi everyone — I’m planning BioLector (a 48 well bioreactor platform) runs and need advice on implementing my design in JMP. I should note up-front that biological triplicates are applied at the subplot (treatment) level (i.e., three biological replicate wells/runs for each unique combination of the subplot factors A–D within a whole-plot temperature setting). Experiment context (what I want to do) * Temperature must be constant during a BioLector run, so it is a hard-to-change / whole-plot factor. * Four additional factors A, B, C, D are easy to change and are subplot factors. * The analysis model should include all two-way interactions among the five factors and second-order (quadratic) terms for continuous factors (building a RSM). * I will use biological triplicates at the subplot level (three biological replicates of each A–D treatment combination inside each whole plot / temperature run). * I plan to use JMP Custom Design → split-plot → 6 whole plots (two for each temperature level)and 5 subplots per whole plot, but I noticed the Replicate option in “Augment Design” is greyed out for custom split-plot designs. What I'd love some guidance on * How to include biological triplicates at the subplot level, given that the usual replicate augment option isn’t available for split-plot designs. * Any general advice or common pitfalls to watch out for. Are there more suitable alternatives out there for my specific case. Any help is very much appreciated!

Hi @BenoitM, Of course, it is a sensible approach and make the design generation a lot easier. Here are the steps I followed: * Create a split-plot design with Temperature (Hard to change), 4 continuous factors (Easy to change), specify a RSM model, and create 6 whole plots with 30 runs in total. * Copy-paste this design 2 times, to enter the Bacterial species factor in the design as a new column and allocate one initial design per species. * Copy-paste the previous design by species 2 times, to get the final design with triplicates for the three species in the final design. You should get at the end a 270 runs DoE with 6 Whole plots, each Whole plots with 45 runs (15 runs by species, and each species having triplicates of 5 unique treatments). Make sure to randomize the runs inside each whole plot, and to order the runs by Whole plots, to respect the restriction randomization. Here is how the final design looks like with a Cell plot visualization and a Parallel plot (unrandomized, just sorted by Whole Plots to better understand the pattern repetition accross species and Whole Plots): Please find attached the design proposed with all scripts (visualizations, models, and randomized version of the DOE). Hope this answer will finally help you get to your final design,

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Custom Split Plot Design Replicates

BenoitM

Hi everyone — I’m planning BioLector (a 48 well bioreactor platform) runs and need advice on implementing my design in JMP. I should note up-front that biological triplicates are applied at the subplot (treatment) level (i.e., three biological replicate wells/runs for each unique combination of the subplot factors A–D within a whole-plot temperature setting).

Experiment context (what I want to do)

Temperature must be constant during a BioLector run, so it is a hard-to-change / whole-plot factor.
Four additional factors A, B, C, D are easy to change and are subplot factors.
The analysis model should include all two-way interactions among the five factors and second-order (quadratic) terms for continuous factors (building a RSM).
I will use biological triplicates at the subplot level (three biological replicates of each A–D treatment combination inside each whole plot / temperature run).
I plan to use JMP Custom Design → split-plot → 6 whole plots (two for each temperature level)and 5 subplots per whole plot, but I noticed the Replicate option in “Augment Design” is greyed out for custom split-plot designs.

What I'd love some guidance on

How to include biological triplicates at the subplot level, given that the usual replicate augment option isn’t available for split-plot designs.
Any general advice or common pitfalls to watch out for. Are there more suitable alternatives out there for my specific case.

Any help is very much appreciated!

Accepted answers

Victor_G

Hi @BenoitM,

Of course, it is a sensible approach and make the design generation a lot easier.
Here are the steps I followed:

Create a split-plot design with Temperature (Hard to change), 4 continuous factors (Easy to change), specify a RSM model, and create 6 whole plots with 30 runs in total.
Copy-paste this design 2 times, to enter the Bacterial species factor in the design as a new column and allocate one initial design per species.
Copy-paste the previous design by species 2 times, to get the final design with triplicates for the three species in the final design. You should get at the end a 270 runs DoE with 6 Whole plots, each Whole plots with 45 runs (15 runs by species, and each species having triplicates of 5 unique treatments).
Make sure to randomize the runs inside each whole plot, and to order the runs by Whole plots, to respect the restriction randomization.

Here is how the final design looks like with a Cell plot visualization and a Parallel plot (unrandomized, just sorted by Whole Plots to better understand the pattern repetition accross species and Whole Plots):

Please find attached the design proposed with all scripts (visualizations, models, and randomized version of the DOE).

Hope this answer will finally help you get to your final design,

Custom Split Plot Design Replicates_F.jmp

Victor_G

Hi @BenoitM,

Exactly, if you plan to analyze the results separately by species, you won't need any nesting in the design, as there will be separate models and pH ranges ( and/or any other factors ranges) for each species. So you can simply replace the values for pH by the ones used for each specific species. You can then modify the last design proposed "Custom Split Plot Design Replicates_F.jmp" with the actual values for each species.

If you plan on generalizing your analysis and seeing different impacts of pH variations depending on species and other factors, you can then use nesting in the model.

All the best for your experimentation and analysis !

All comments

Victor_G

Hi @BenoitM,

Welcome in the Community !

I see at least two options that could lead to a design appropriate for your needs:

You could add a 3-levels categorical "Hard-to-change" factor for subplot replicate (A, B and C or 1, 2 and 3 levels), adding the main effect for this subplot replicate factor in the model panel, changing Temperature as "Very Hard to Change" factor, checking the option "Hard to change factors can vary independently of Very Hard to change factors", and specify an appropriate number of whole plots (depending on the number of bioreactor platforms you want to use) and number of subplot (multiple of 3, I choose 3 so that each subplot experiment is replicated 2 times, like you want). This design will create replicate runs in the subplots, but there is no certainty that each run of each subplot will be triplicated.
You could also design first the subplot matrices, by creating 6 RSM designs for 4 factors with 16 runs, and then replicating them each up to 48 runs with the Augment Designs. After this step, you can combine the designs (by concatenating the tables), adding a Whole Plot factor column and applying Temperature factor levels to each of the 6 Whole Plots, and it should lead to the design you want. This option is perhaps more tedious, but it is sure that the replicates constraint will be enforced by having each run of each subplot triplicated. Make sure at the end of this process to randomize order of the runs inside each whole plot, using the workaround found here Randomize rows from a datatable by using Create a Subset Data Table with sampling rate of 1, and then sort the datatable by whole plot order to keep the split-plot structure.

Either one or the other option should create a design that respect your needs, you may only have to change the assumed model from the "Model" script in the datatable (to remove any effects linked to "subplot replicate" factor and the subplot random effect for option 1 for example).
Please find attached an example of such design, with 288 total runs, 48 runs per whole plot, and 3 replicates for each subplot experiment for both options. Some scripts are also saved in the datatable with the corresponding model assumed "Model (refined)" and some visualization of the design structure.

The comparison of the two designs performance shows similar results:

Power analysis:
Prediction variance and FDS:
Correlation map and design diagnostics:

Hope this answer will help you,

Custom Split Plot Design Replicates_Option-1.jmp

Custom Split Plot Design Replicates_Option-2.jmp

BenoitM

Hi @Victor_G

Thank you so much for the detailed explanation and the example designs! I think I need to clarify my situation a bit better to make sure we're on the same page:

I’m working with three bacterial species (M. sporium, M. buryatense, and M. acidophila). So far, I’ve been creating separate DOE files for each species (I’ve attached these in case that’s useful). Within each whole plot, I have the five subplot treatment combinations. I’m planning three biological replicates per subplot treatment. Across all three species, that gives me a total of 45 treatment conditions (5 subplots × 3 replicates × 3 species) for 1 out of 6 whole plots — which fits well into a single 48-well BioLector plate.

My Main Questions:

How do my 45 wells (15 wells per species) compare to your 48 wells per whole plot? I'm trying to understand if we're talking about the same thing or if there's a fundamental difference in our design structures.
How does having 3 separate species affect the split-plot design? Should "Species" be treated as an additional factor in the design, or is it acceptable to keep them as completely separate designs?
Can I keep my three DOE files separate, or do they need to be combined into a single design file?

I hope this is clear. Thanks again for taking the time to explain this!

Benoît

Indeed, I had some infos missing from the first post and question. I'm not entirely sure to follow :
How many factors do you have in total ? :

Temperature (Continuous, 3 levels, Hard to change),
Species (Categorical, 3 levels, Easy to change),
pH (Continuous, 3 levels, Easy to change),
TN (Continuous, 3 levels, Easy to change),
TN/TP (Continuous, 3 levels, Easy to change),
C/N (Continuous, 3 levels, Easy to change) ?

Do you confirm you would like to create a design with maximum 288 runs with 6 whole plots of 48 runs each ?

The designs options I have posted contained Temperature and 4 continuous factors (pH, TN, TN/TP and CN), but not the "species" factor.

Even if "bacterial species" is missing in my design option 2, you can still use the process used to create your final design:

Create individual subplot matrices for the 5 Easy-to change factors, specifying a model for each matrix with 16 (or 15) runs, and replicate each subplot design to get to the 45/48 subplot parts.
Concatenate the subplot parts to get the final design structure.
Link 2 whole plots to each temperature conditions.
Make sure all column properties and model script are ready for analysis (whole plot random effect column and attribute in model, Hard to change property for Temperature, etc...).

Concerning your two other questions:

@BenoitM wrote:
How does having 3 separate species affect the split-plot design? Should "Species" be treated as an additional factor in the design, or is it acceptable to keep them as completely separate designs

Yes, if species is an easy to randomize/change factor, I think it should be included in the subplot designs matrices. This way, it will make sure that the individual species are facing different levels of the other factors in a randomized way. However, if you want to triplicate each run of your subplot runs, you won't be able to have a full RSM model in only 16 runs with these 5 factors (minimum is 25). You might have to change the estimability of some higher order terms to "If Possible" and/or delete some model terms to reduce the number of runs to 16/15.

@BenoitM wrote:
Can I keep my three DOE files separate, or do they need to be combined into a single design file?

It depends on what you want to do in terms of objectives, modeling/statistical analysis, and practical realization of the experiments.
Will the DoEs be run independantly ? Or by batches ? Do you want to learn/analyze from them globally ? Or incrementally, "DoE by DoE" ? By species or globally ?

The design(s) structure(s) and analysis should reflect your objectives and goals.

Hope this follow-up answer will help you,

BenoitM

To answer some of your questions.

1) How many factors do I have in total?

Whole-plot (hard-to-change): Temperature — continuous (3 target levels).
Subplot (easy-to-change, per-species DoE): four continuous factors — pH, TN, TN/TP, C/N.
Species: I have 3 bacterial species, but I do not want species as a subplot factor, since I would like to analyze each species independently. So, per-species design/model will include Temperature + the four subplot factors (and their two-way interactions / quadratic terms as planned).

2) How the DoEs will be run

The DoEs will not be run independently. Each BioLector run takes roughly a week and will complete a single whole plot (at a fixed temperature) that contains one species block (5 subplots x 3 replicates = 15 wells) from each of the three species’ DoEs for a total of 45 wells (48 is max capacity).
After all runs are complete, I will analyze by species.
I do not want subplot treatments randomized across species, if that makes sense.

Given this setup, I think my question boils down to: Can I keep the attached DoEs separate for each species? If so, what is the most efficient way to include the biological triplicates? Would it be reasonable to simply copy each subplot row twice (to get three replicates per treatment) and then randomize only within each species block on the plate?

I hope this additional context helps in answering my question.

Benoît