This might be an easy one for those who do it on a regular basis...
I have performed an XTT proliferation assay on two different cell lines with 6 different concentrations of a substance. I repeated the assay twice.
For every assay i obtained 6 measurements per concentration and cell line. Now i am a little stuck with the analysis of my data.
Do i collect all values in one table for each concentration and cell line or do i evaluate every assay on its own?
If i combine the results, do i use the OD-Values or the calculated percentages?
I try to do proper statistics but as you can see i am a little lost here.
Any advice is very welcome. If you know any literature, feel free to let me know where i can find it.
Thank you very much in advance for your support.
If you have keep time of cultivation with XTT very strictly when virtually you have an opportunity to use OD data for comparison of relative "points".
Usually better to have positive and negative controls in each data set to normalize your data before analysis.
F.e. two way ANOVA could be used to discriminate effects of treatment and time after treatment. (Data analysis samples in Origin or GraphPad Prizm could give you ideas how to perform analysis of your data).
I do have untreated and "overly treated" samples. So i do have the 100% and the 0% marks.
I added a picture. This shows the kind of results i might get from the microplate-reader.
The substance i work with isnt overly stable, so i supect to get results that are not perfectly reproducable. How can i combine results from experiment 1 and 2? Besides the day on which i do the experiment all parameters should be the same.
I am not too familiar with graphpad. I tried to do students t-test and anova via excel. But only on one experiment. But i dont know how to combine thes results. Do i just take those values and put them in one table?
No problem. Use technical repeats to improve statistics. GraphPad is very intuitive an free for trial period. It is possible to organize data in spreadsheet to perform two-way ANOVA. As alternative you could analyse each cell line usin One-way ANOVA (also with technical repeats). Unfortunately I have no idea how to organize your data in Excel for it. But it surely possible.
As for me two-way ANOVA is better because it could respond what source of variation is greater treatment or cell type.
One more thing.. As I can see in some dilution series you not reached even 50 % of intact viability. In many situations it is better to report IC50 instead of response what your treatment affect viability. So, some improvements could be done in experiment design to get more accurate IC50 numbers and to take in account dynamics of cell proliferation.
Thank you very much for your effort.
Would it make sense to do all afor mentioned statistics? T-Test for each concentration and cell line, one way anova for all treatments and each cell line, and two way anova for treatments and both cell lines? Or is this simply overdoin it. It wouldnt mean no harm for me as i will obtain all the data anyway.
I guess i will have to take a deeper look at statistics that confirm certain hypotheses.
The values in the graphic are arbitrary. I made them up to better explain my problem. This is the way how my results/data will look like in the end. I dont have them yet.
You could choose any method of statistical analysis but please keep in the mind what multiple comparisons will affect your results (probability of false positive or false negative result of analysis). It is just a playing with p-values (p-hacking) but main results will be same.
Your data clearly indicate dose-dependent inhibition of XTT measurements. So, before doing your choice just ask self what question you want to answer using statistical analysis.
Many questions still hidden: Did XTT data are in accordance to other methods of estimation of viability or cell number, did you confirmed what XTT data related to changes in cell number only, how posttreatment incubation time did correspond to population doubling time, possibility of enchanted reactive oxygen species production etc.
Thus this methodology should be used as screening but not proving. So, better to describe results properly (IC50, errors, source of variation). My choice is two-way ANOVA and IC50 calculations.
Have a nice experiments!
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