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RNAi screening

thestrider

I have an RNAi dataset with 3 technical replicates that was done in triplicate. For example:

Run 1

gene X 45

gene X 59

gene X 51

Control 90

Run 2

gene X 145

gene X 159

gene X 151

Control 190

Run 3

gene X 35

gene X 39

gene X 31

Control 50

The real dataset has hundreds of genes and dozens of controls per experiment. Each run tends to vary in its average, but the experiments were the same. I think this is due to room humidity, but it doesn't matter in the end. My interest is in the genes, not the run variation. I basically want to see which genes vary from control, and a probability (p value?). What is the best approach to analyzing this data with JMP?

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Chris_Kirchberg

Hi @thestrider

I would happy to guide you through this process.

I have some clarification/verification questions since my ultimate response will depend on these answers.

So does your table of data look something like this but with many genes?

Is there only one control per run but three technical measurements of the same condition within the same run (like in the table above)?
Do you have something like condition x is one experimental condition, with three technical replicates for gene x, and you have many conditions for the experiment (condition x, y, z, etc.)?
As many details as you can give the more accurate and appropriate my answer/solution will be. Even what the output of your data looks like helps.

Thanks,

thestrider

Hi Chris!

You are correct, my dataset looks like almost exactly like your picture, with 400 genes (gene x, y, z, etc) and some more metadata that is just for validating. (ie 96 well microplate location, the plate name etc)

Each run has many controls, some are empty vector, (baseline, no effect) others are known genes that generate high or low responses. Every microplate had controls, and we also just tested a control only microplate in each of the three runs. About 15% of my dataset (~2000 of 15000 measurements) is controls.

Do you have something like condition x is one experimental condition, with three technical replicates for gene x, and you have many conditions for the experiment (condition x, y, z, etc.)

I think this is a good way to describe it. Each gene in the list is a gene that was turned off. So I turned off 400 genes, one at a time. In each run I had three identical copies of each microplate, so there are 3 technical replicates of each gene. I should note here that randomly about 15% of data was censored out (bacteria didn't grow, contaminated, etc), so sometimes genes do not have 3 technical replicates. I then repeated this entire experiment the same way two more times. So in an ideal world I have 9 data points per condition. Censoring is random though so some genes have only 4-5 data points.

The empty vector control average varies across all three experiments. But the response of each gene should be similar. So a gene that lowers the value from baseline should always lower it, but the numbers themselves vary sometimes drastically in the three runs. So gene X might have a value of say 70 vs a 100 average baseline in one run, but a value of 120 vs a baseline of 300 in the next. I thought I might need to normalize, but I am worried that the data may be tailed and I am uncertain on the stats to do that. I am measuring fluorescent levels, and basically dim things don't get much dimmer but bright things can get very bright. So by tailed I mean in the example I just gave, a bright gene might be 350 vs 100 baseline, but in the next run it could be 1200 vs 300 baseline. In other words there is a big numerical range on the high end of the scale, but the low end there is a smaller range. I hope that makes sense.

I attached a little powerpoint, it shows what I am measuring, ( c. elegans nematodes) and a sampling of data.

Results Presentation.pptx

Chris_Kirchberg

Thanks @thestrider,

Yes, there are multiple things that need to be addressed here. I can't tell you what you should do statistically (which analysis you would need to do or how to address the between run variability due to external factors, like the shifting of control and treatment values) since we are advised to not give statistical advice on how to treat each of these issues, but I can point you to which platforms within JMP that could help address some of them

Within run statistical comparisons cannot be made since there is only one control per run and no way to assess variability for within run variation to make that comparison (at least not by traditional statistical methods that I am aware of). Between run variation can be estimated since there are three controls, but it will be biased because you can have up to 9 possible measurements for the condition of interest. This would be considered unbalanced and possibly give you unequal variance. Thankfully, JMP has a means to deal with unequal variance in the Fit Y by X platform (gene X response in the Y, Response section and condition in the X, Factor section). Then choose t-Test from the red triangle which will give you the means for each group and a p-value for testing if the condition is different from control, assuming unequal variance. The censored data is basically missing data (not measurable due to the reason given) and makes sense why you have added technical replicates to accommodate such issues for each run.

Also, as you have mentioned, values shift from one run to the other, including the control, almost like a match pair situation (or paired t-test like situation) or a batch effect. So the above alone is not enough to see if there is a statistical difference because of this additional bias. There could be many ways to address this. One may be to do multiple regression where run and condition are effects (kind of like batch and condition looking for batch effects and accommodating for them).

In this case, Fit Model is the platform to use and choosing Standard Least Squares as the personality will get you to the type of test/analysis that might be most appropriate in your case. I would first test to see if there is a run effect by putting run, condition and the interaction of run and condition as model effects and then Gene X response as the Y. You could do this for all genes (and that would be a lot to look at for hundreds of genes). The small example data you have, I have done this and there is definitely an effect due to run to run variability, so that means it must be accounted for in the statistical tests you perform.

I have created a JMP table and saved the Fit Y by X and the Fit Model analyses to the the data table so you can see how I performed each test. It is attached to this post.

It is best to either contact a statistician in house or see if one of our gracious community members are willing to advise on how best to treat your situation. Maybe also take the free Statistical Thinking for Industrial Problem Solving course might help learn more about statistics that could help in this situation. Or take a look at the Statistics Knowledge Portal for more statistical upskilling.

Sorry I am not able to give the statistical advice you might need, but I hope I at least pointed to where in JMP you could go to analyze your data and assess the experiment and test for significance of the conditions.

Best,

example.jmp

thestrider

@Chris_Kirchberg wrote:

Within run statistical comparisons cannot be made since there is only one control per run and no way to assess variability for within run variation to make that comparison

Hi Chris, thanks so much for this that is incredibly helpful. I am a little confused by this part, sorry I was unclear, but I have ~210 empty vector control data points for each of my three runs. They are spread across every microplate I did (18 plates per run) but are identical otherwise. I also have a similar number of controls using known high and low responders.