I am searching for the best approach to statistically evaluate differences in cell cycle distribution between different cell populations. We are analysing the cell cycle distribution, by staining DNA with PI and analysing what percentage of the cell population is in the different phases (GO/G1, S and G”/M) using flow cytometry and the Flow Jo software. So we get percentages of cells in these three phases for different cell populations and we want to analyse differences between the cell cycle distributions for these populations (usually treated cells in comparison to control). Using simple ANOVA or ttest statistics is probably not the best here, as the amount of cells in each of the phases is co-dependent on the amount of cells in the other phases. What would be the best approach?
hi..
One approach you could use to statistically evaluate differences in cell cycle distribution between different cell populations is the chi-squared test for independence. This test can be used to determine whether there is a significant association between two categorical variables, in this case, the cell population and the cell cycle phase.
To use the chi-squared test, you will need to organize the data in a contingency table, with rows representing the cell populations and columns representing the cell cycle phases. You can then calculate the expected frequency for each cell population/cell cycle phase combination based on the overall proportions of cells in each phase and the total number of cells in each population. The chi-squared statistic can then be calculated using the observed and expected frequencies, and the resulting p-value can be used to determine whether there is a significant difference in cell cycle distribution between the cell populations.
It is also possible to use other statistical tests to evaluate differences in cell cycle distribution, such as the K-sample test for comparing multiple groups, or the G-test for comparing proportions. It is important to choose the appropriate test based on the specific research question and the characteristics of the data.
I hope this information is helpful.
Hello.
You can try Friedman's test, and post-hoc analysis with Dunn's test.
Sincerely yours,
Artem Trofimenko
To make your S-phase determinations more accurate, I would suggest adding BrdU (BUdR) incorporation after a pulse exposure followed by the BrdU-DNA antibody (and your PI label) to clarify which of the cells between G1 and G2/M DNA content are actually in S.
A problem with the whole approach is that it may be important what fraction of your cells are in cell cycle. The G0 fraction (nonproliferating) may vary between your cell lines, and add to your G1 DNA-content fraction, confusing your cell cycle distribution. If your experimental system allows, you may wish to identify proliferating cells by adding another label (IUdR) in the previous cycle and its DNA antibody to your flow cytometry procedure.
I hasten to remind readers that this is not primarily a statistics problem because ignoring the G0 nonproliferating fraction will make nonsense of the derived cell cycle parameters (G0 will add to the G1 fraction, 2n DNA content and reduce others). Please see my recent entry, above.
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