My experiment is isolating primary cells from biopsy of different patients.
And then to culture the primary cells in two conditions, one is the negative control, and the experimental set-up is to culture with more hormone. I had cultured them in parallel (both are freshly cultured immediately after I isolated them from biopsy).
Then I got the assay result in two different culture conditions, I considered my results are paired result of each patient, so I performed paired T-test.
But when I presented it to my colleagues, she said paired sample t-test may not be appropriate here because there is some strict regulations about using paired sample t-test with samples from cell culture. I had briefly search for it for a while but cannot find it.
Can anyone tell me whether there is such regulation?
I had also performed independent sample t-test with my result, and got a much larger p-value. I think it is the individual difference between patients generated larger variance in the analysis.
Could anyone tell me the best test for my data?
As far as I can see you are correct with a paired t-test . Why would cell culture data be different? Without the absent reference, I would not take a partial memory to be scientific fact.
Best, David Booth
My knowledge about cell culture is too limited. The only reason I can come up with, why a paired t-test might not be appropriate for cell culture, is their exponential growth.
T-test assumes normal distribution; if you harvest during the exponential growth, your data may not be normally distributed. The most simple solution would be to perform a log-transform, as you do it on paper to describe the growth curve, i.e., to use the logarithm of your data for the test.
But I would be interested to read what others have tosay to this topic.
If exponential growth is the issue, then why not a log transformation of the data, and keep the paired sample t-test. It might depend on the data: cell counts versus CFU. If the range in counts is small then a Poisson distribution might be more appropriate.
The underlying properties of the data are important, and the source of the numbers helps inform on these underlying properties. However, exponential growth of bacteria is still exponential growth. For the statistical analysis the "bacteria" part of this description is unimportant. It is essential for your interpretation of the statistical result, but not for the mathematical calculations.
That said, there may be some critical text that you need to cite to satisfy the social aspect of getting your work published. Your work will be perceived as good if you cite a well known author in cell biology and rejected if you cite a statistician or a marine biologist even though the equations are all the same. You might consider publishing a separate paper if you have cause to think that the cell biology author got it wrong. Publishing a blended paper is more difficult unless the error is relatively trivial or specific to your project.
If you harvest during exponential growth clearly that may play a part in your interpretation of experimental results. However that was not the issue. All cells were of the same cell type to within a small number of spontaneous mutations perhaps. The only difference between the plates is that one plate contains hormone while the other does not. The inoculate for each paired plate comes from the same individual. I assume growth curves were obtained for cells with and without hormone so you know if hormone has an effect on cell growth and what if any that effect is so if such an effect exists you may consider it with the rest of the experimental results. Given all this unless some strange effect happens that requires explanation. This is statistically a paired t test. Any effect on the growth is known so any correction necessary can be made, such as was suggested by Dr. Ebert. Again when all necessary corrections, transformations and what have you are finished. The comparison is the paired t-test. This is exactly how we did chemical mutagenisis studies in Herman E. Brockman's lab many years ago. See the link for publications from some in the group with Materials and Methods sections. I apologize for my poor description but this was 50 years ago for me. Mea Culpa.
https://www.google.com/search?q=Herman+E+Brockman+Neurospora+yeast+mutagenisis&rlz=1C1CHBF_enUS847US847&oq=Herman+E+Brockman+Neurospora+yeast+mutagenisis&aqs=chrome..69i57j33.47880j0j8&sourceid=chrome&ie=UTF-8
Again, I see no reason why a poorly remembered something or other affects either the statistics or the biology. As always the assumptions of the test should be checked. Best, David Booth
@KC Lee, you do not mention the actual endpoint being compared. Is it growth rate (slope of growth curve?), cell numbers or increases in cell numbers at a fixed time of growth?, etc. What are your data points? Plate averages? Well averages in multiwell plates? Are these distributions normal? The endpoint is what needs to be examined for its distribution and is it normal or log-normal, or something else. If normal or log-normal the paired t-test can be used for the appropriately transformed data (to make it normal for the t-test).
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