Hi @AdditiveTiger68,
Welcome in the Community !
In order to compare your data from two different tables, you will need to combine these two datasets in a stacked format, like this :
Then, using Graph Builder, you can plot your data using your Response in Y axis, and the Temperature as X axis, and the Protein ID as the Overlay variable. Using a local data filter may help to avoid too much different data and lines of fit (or box plots if temperature has a nominal data type) for different proteins.
If you're interested in analyzing your data, you can use the Platform Fit Y by X : Oneway Analysis (jmp.com)
However, since you only have 3 values for each protein and for each temperature, this seems to be a very low sample size to run any statistical tests.
I would recommend plotting your data individually by proteins, to check for patterns, and (with caution) analyzing the whole dataset with the platform Fit Y by X, to see if you have a general trend that may be statistically significant for the change in response depending on temperature. Please check that your data do respect the assumptions of the test you want to use (variance homogeneity, normality of the distribution and independant observation for parametric test) :
You might also be interested in Mixed models for Multiple Comparisons : Multiple Comparisons (jmp.com)
Depending on your objective, you might also be interested in equivalence test if your objective is to determine that variation of response for different proteins is still in an acceptable equivalent range of values (difference centered on 0, with +/- delta considered as acceptable).
As you can see, there are a lot of possibilities depending on your data and your objectives, so you may have several options in JMP that correspond to your needs.
I attached the example datatable so that you can see these different options.
Hope this answer will help you,
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)