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A call to JMP Scientists


Community Trekker


Apr 13, 2016

Hey JMP scientists,

I’m working on some cell based assay development projects where I’m using DOEs to try to optimize the parameters I can control in the experiments.


Setting up the layouts for 96 and 384 well plates to get a reasonable level of replication and avoid edge effects is kind of a pain. Are there some good strategies that don’t involve randomizing the entire plate with a robot?  (although I have a liquid handler for certain variables which are more amenable to randomization)  What kinds of designs and what terms (e.g. interactions, quadratic, cubic..)  are you putting into the designs?  How do you handle 'replicates' in the DOE custom design generator.   I am curious to get as much feedback as possible.  I understand the power and utility of DOE, I am a convert, but I just need help finally getting this effort off the ground and utilizing it in our day to day assay development and experimental design.


Thank you!




Apr 26, 2012

I've been lurking on this question because I'm sure there are likely some stong opinions on this topic. Here's one way to set up a 96 well experiment.


This design does some things that I think are useful.

In the experiment there are continuous factors:

   1. Drug, an inhibitor I'm interested in studying

   2. Stim, the thing stimulates a response the drug is supposed to inhibit

   3. Serum level, grows cells better but increases background of the response

and some categorical factors:

   1. Media type

   2. Cell line, different cell line for each plate


To make the plate setup reasonable I want to place some restrictions on randomization.  If I had lots of time I could program the pipetting robot to fully randomize the entire plate, but if just restrict randomization I can keep concentrations down columns and across rows the same, and plating gets a lot more simple.

First of all, for the scope of this, I essentially have 384 wells to work with, 96 per plate, and 4 plates (one for each cell line.) For the factors I'm working with, I can easily look at 2nd order interactions and quadratic effects for the continuous terms. When I set up the plates I have 12 columns and 32 rows (8 rows for each of the 4 plates.)   

In the DOE dialog, I'm going to restrict the randomzation for rows and columns. This means making some factors "hard" to change and some "very hard.", which makes this a split strip plot experiment. Whole plots correspond to hard, and Subplots correspond to very hard to change factors. So if I have 12 subplots, and 32  (8x4) whole plots, then the hard to change things (in this case Drug and Serum) will be constant down the columns and the each indifidual row will be the same across the plate. Let me explain this a little more. Each column has a different concnetration, the concentration is the same down the entire column. (Super handy if you're multichannel pipetting your own plates.)

The last thing to consider is the plate/cell line. What I really need is a three way split plot, so cell line/plate would be "Super Hard to Change", but that's not an option, and It might be better if there was a different subplot pattern for the columns for each plate, but this is pretty good. (realistically, just making 8 whole plots and 12 subplots and using the same plate layout for each of the four cell lines is probably good enough, plus it reduces opportunities for error in the plate layout.)






                Custom Design,
                {Add Response( Maximize, "Y", ., ., . ),
                Add Factor( Continuous, 2, 25, "Drug", 1 ),
                Add Factor( Continuous, 0.02, 0.1, "Serum", 1 ),
                Add Factor( Categorical, {"IMDM", "RPMI", "DMEM"}, "Media", 2 ),
                Add Factor( Continuous, 5, 50, "Stim", 2 ),
                Add Factor( Categorical, {"L1", "L2", "L3", "L4"}, "Plate Number", 2 ),
                Set Random Seed( 550796982 ), Number of Starts( 1 ), Add Term( {1, 0} ),
                Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {3, 1} ), Add Term( {4, 1} ),
                Add Term( {5, 1} ), Add Term( {1, 2} ), Add Term( {1, 1}, {2, 1} ),
                Add Term( {2, 2} ), Add Term( {1, 1}, {3, 1} ), Add Term( {2, 1}, {3, 1} ),
                Add Term( {1, 1}, {4, 1} ), Add Term( {2, 1}, {4, 1} ),
                Add Term( {3, 1}, {4, 1} ), Add Term( {4, 2} ), Add Term( {1, 1}, {5, 1} ),
                Add Term( {2, 1}, {5, 1} ), Add Term( {3, 1}, {5, 1} ),
                Add Term( {4, 1}, {5, 1} ), Add Term( {1, 3} ), Add Term( {1, 4} ),
                Add Term( {4, 3} ), Make Strip Plot Design, Set N Whole Plots( 32 ),
                Set N Subplots( 12 ), Set Sample Size( 384 ), Optimality Criterion( 2 )



Jun 23, 2011

I think that this matter of designing a sophisticated experiment with restrictions on the randomization is important and, therefore, this discussion could help a lot of users in a similar situation. It is a great example of what we hoped this discussion area in the JMP Community could be for all of us. I am bewildered that there has been no activity for a week. I doubt that it is because the last reply answered all of the remaining questions.

I hope that the discussion picks up again. Here are some points that I hope provoke more replies:

  • How is this case like your own if you develop assays? How is it different?
  • Do you have the same restrictions on randomizing the runs (wells)? More? Less? Just different?
  • The restrictions on the randomization clearly impact how practical the design is to run. Is it just as clear how the restriction impacts the analysis? What might happen to my interpretation of the regression model if the restriction is ignored in the analysis?
  • What are some practices that you find beneficial while designing, running, or analyzing an experiment with restrictions?

(Note that there is another related discussion started by the same user. I am going to cross-post this call to action and may the best discussion win!)

Learn it once, use it forever!



Jun 23, 2011

Part of the reluctance here might be the language that we use. Perhaps that should have been languages. The scientist talks about plates and wells, pipetting and dilution, incubation time, concentration, and so on. The statistician, on the other hand, (indirectly through the JMP user interface for custom design and directly through replies here) talks about types of factors, easy, hard, or very hard to change factor levels, whole plots and sub-plots, blocking, and so on.

Did you get all that? Probably not. That's normal.

So we need to take the time to explain ourselves to the other people in the discussion. The more advanced the topic and the issues, the more explanation is required to fully communicate the problem and the solution.

Learn it once, use it forever!